Download Supply and Demand Shocks in the Oil Market and Their Predictive

Supply and Demand Shocks in the Oil Market and Their
Predictive Power1 2
Avihai (Avi) Rapaport3
The University of Chicago Booth School of Business and Department of Economics
Abstract
This paper identi…es supply and demand shocks which are speci…c to the oil market and separates them
from economy-wide shocks that a¤ect the demand for many asset classes, including oil. The identi…cation is
done by the sign and magnitude of the correlation between daily oil price %-changes and the aggregate stock
market index returns, excluding internationally diversi…ed oil-companies. Shocks that are speci…c to the oil
market - prominently due to geopolitical events in the Middle East or changes in the expectations thereof
- are identi…ed as inducing a negative contemporaneous correlation between oil price changes and stock
market returns. On the other hand, economy-wide shocks - prominently due to unexpected global economic
booms or busts - are identi…ed as inducing a positive correlation. I present a Dynamic Stochastic General
Equilibrium model comprised of a representative consumer, …rm, and an oil-sector with storage technology.
The model motivates this identi…cation scheme and matches several key asset-pricing, macroeconomic, and
oil-market-speci…c moments. I then employ a novel reduced-form methodology to show clear-cut empirical
evidence regarding the macroeconomic e¤ects of the shocks on the future realizations of the stock market’s
excess returns, dividend growth rates and on real GDP growth rates. Intuitively, the e¤ects are of opposite
sign depending on whether the oil price change originated from an oil-market-speci…c shock or from an
economy-wide shock. I also show predictability in the cross-section of industries’excess returns, predictable
variation in the change of the spot price of oil, and time-variation in the risk premium embedded in oil
futures.
1 I thank Lars Hansen, George Constantinides, Ralph Koijen, Eugene Fama, Harald Uhlig, Pietro Veronesi, Lubos Pastor,
Bryan Kelly, Stefano Giglio, Stavros Panageas, Axel Weber, Mathias Ho¤mann, Lutz Kilian, James Hamilton, Steven Levitt
and participants at Workshops at the University of Chicago Booth School of Business & Department of Economics and at the
University of Zurich for helpful comments.
2 I thank the Energy Policy Institute in Chicago (EPIC) for a generous Dissertation Award made in support of this paper in
2012, the Chicago Mercantile Exchange (CME) Group Foundation for a gift made in support of this paper in 2011, and to the
Center for Research in Security Prices (CRSP) for a grant made in support of this paper in 2010.
3 Job
Market Candidate in the Joint Program in Financial Economics. Email: [email protected]. Telephone:+1-312-4026855. First version: September 2010. Current version: May 2013.
1
1
Introduction
Oil is by far the single most important physical commodity input to the world economy4 . There is a big
interest in the macroeconomic e¤ects of changes in the availability of oil supplies, beginning with Hamilton
(1983), who documented an inverse relationship between oil price changes and real GDP growth rates.
More recently, with Kilian (2009a) being the signi…cant contribution, there has been a realization that the
price of oil is responding to the same economy-wide forces that simultaneously drive the quantities of many
macroeconomic variables and asset prices. It has become clear that an oil price change cannot be treated
as if it were exogenous to the economy, and that cause and e¤ect are not clearly de…ned in a regression of a
macroeconomic variable of interest on changes in the price of crude oil.
This paper’s contribution is to suggest a new way to identify changes in the price of oil that are due to
exogenous developments (i.e. shocks) that are speci…c to the oil-market, which a¤ect either the supply or
demand conditions in this market, or both of them at the same time. There are several di¤erent kinds of
such shocks which can, and evidence strongly suggests that they in fact have, hit the oil-market - especially
during and in anticipation of wars in the Middle East. These shocks are exogenous variations to the (1)
current oil production ‡ow; (2) to the future oil production ‡ow; (3) to the uncertainty surrounding oil
production; and lastly, (4) speculation in the commodity futures markets may also have lead to similar
e¤ects. These are presented in Table I and discussed in more detail in the subsection that follows (1.1).
Since these shocks tend to occur simultaneously they are di¢ cult to identify separately. Luckily, they all
share the same macroeconomic e¤ects which makes them jointly identi…able as a group using my novel
approach. These macroeconomic e¤ects are of clear interest to a greater audience of …nance, economics and
energy - academics and practitioners - and are generalizable to any major commodity that may become as
prominent as oil in the future. Academic researchers interested in the …elds of oil and the macroeconomy,
aggregate predictability or asset-pricing in production-economies, may …nd this paper useful. The main
contributions are the empirical identi…cation strategy and methodology, and the robust empirical evidence
which coincides with conspicuously known events such as wars and economic crises. The representative oilsector in the general equilibrium model provides clear elucidation to the identi…cation, is useful in matching
oil-market-speci…c moment, and is a solid building block for future research.
Using intuitive language at this stage, each of the oil-market-speci…c shocks leads to a rise in the price
of oil and at the same time, since they are detrimental to the global economy, leads also to a negative
return in the aggregate stock market (excl. oil-companies)5 . I …nd these two e¤ects, encapsulated in a
4 According to the Annual Energy Review 2011 by EIA.org, total nominal US petroleum expediture’s share of nominal GDP
was 4.9% in 2010 while total nominal energy expenditure’s share of nominal GDP was 8.3%.
5 I follow the normalization that an oil-market-speci…c shock leads to an increase in the price of oil and refer to it as being
adverse. The e¤ects are symmetric for a shock that leads to an oil price decrease.
2
negative correlation between oil price changes and aggregate stock-market returns, most suitable for my
identi…cation because they rely solely on prices of exchange-traded assets which are forward-looking6 and
precisely-measured.
On the other hand, what seems to drive the price of oil quite more often nowadays7 are economic shocks
that are not speci…c to the oil-market. In particular, my second prior is that unexpectedly strong (weak)
global economic growth leads to an increase (decrease) in the price of oil, as demand for the oil input increases
(decreases), and at the same time, to positive (negative) returns in the stock market, resulting in a positive
correlation.
My methodology is consistent with the identi…cation-by-sign-restrictions that has been proposed by
Uhlig (2005) as an alternative to the imposition of exclusion restrictions on the multiplier matrix in a Vector
Auto-Regression (VAR) framework, which is done in order to recover orthonormalized structural shocks. In
particular, I label changes in the price of oil which are negatively correlated with the returns of the stock
market index, as being due to an oil-market-speci…c shock. In contrast, I label changes in the price of oil
which are positively correlated with the stock-market returns, as being due to an economy-wide shock that
is not speci…c to the oil market.
My identifying sign restrictions are priors which I assume in order to back-out two parsimoniously selected
structural oil price shocks which are the most practical in forecasting changes in other macrovariables which
are of clear interest. I motivate the identifying sign restrictions by producing a theoretical model which
builds on the canonical Real Business Cycle (RBC) literature (see Kydland and Prescott (1982)) and is
consistent with empirical evidence found by other researchers, most closely related is Kilian (2009a; b).
I impose these restrictions, back-out the structural oil price shocks, and test whether the shocks have
explanatory power on the macrovariables of interest: (1) real GDP growth rates; (2) future ex-post-realized
excess stock-market returns, which proxy for the current aggregate risk-premium embedded in equities; and
(3) aggregate stock-market dividend growth rates. In the spirit of Uhlig (2005), I remain agnostic on what
these e¤ects should be and let the data speak for itself, so to say.
6 An example of a quantity variable that is not forward looking is oil production with severe capacity constraints. It is often
the case that it is known in the current-period, that there will occur an oil production disruption in future periods. Current oil
and stock-market prices will re‡ect this immediately, but oil production in the current period will change very little, providing
a poor identi…cation tool.
7 In the post-WWII era and upto to the early 1980’s, crude oil prices did not ‡uctuate at all for long periods of time until
they adjusted upwards abruptly. Hamilton (1983) explaines this: "Each month the Texas Railroad Commission (TRC), and
other state regulatory agencies like it, would forecast demand for petroleum for the subsequent month and would set allowable
production levels for wells in the state to meet this demand. As a consequence, much of the cyclically endogenous component
of petroleum demand showed up as a regulatory shift in quantities, not prices. On the other hand, the TRC’s sympathies were
clearly with the producers it was meant to regulate, and the commission was generally unwilling or unable to accommodate
sudden disruptions in supply, preferring instead to exploit these events to realize dramatic price increases."
3
With this exercise, I show that the oil-market-speci…c shock causes a change in the said macrovariables.
The causation is established by illustrating that the major swings in this shock’s time-series correspond to
known geopolitical events in the Middle-East which have exogenously a¤ected the oil market and the US and
global economies. I show these e¤ects in a predictive regression setting. I …nd intuitive and economically &
statistically signi…cant results, which are robust to several empirical criticisms and comfortably beat other
proposed oil price shocks in a horse-race.
Intuitively, I …nd that an oil price increase (and symmetrically decrease) that is due to an adverse oilmarket-speci…c shock causes a decline in economic activity and in the aggregate dividend growth rate, and to
an increase in the aggregate risk premium in equities. In contrast, an oil price increase (and symmetrically
decrease) that is due to an economy-wide shock causes an increase in economic activity and in the aggregate
dividend growth rate, and to a decrease in the aggregate risk premium in equities. Notice the e¤ects of a
given oil price change on the macrovariables is of opposite sign depending on whether it originated from an
oil-market-speci…c shock or from an economy-wide shock.
I motivate the identi…cation by producing a theoretical DSGE model comprised of (1) a representative
consumer-investor; (2) a goods-producing …rm which uses capital, labor and oil as inputs; and (3) an oil-sector
with storage technology which optimally decides how much of the oil production ‡ow to bring to market
and how much to store for future periods. I model each of the four oil-market-speci…c, and one economywide shocks, and show by way of calibration and simulation that they produce the impulse responses which
underlie the identi…cation and the hypothesized e¤ects. These are summarized in Table I below. For the
canonical parts of the model, I choose as parameter calibration, the values used by the respective authors
who raised them to academic prominence. For the oil-sector part of the model economy, I calibrate the
parameters to match key oil market speci…c moments.
1.1
Shock Classi…cation
[Insert Table I]
The …rst item in Table I is a shock to the production ‡ow of crude-oil and has been widely discussed in
the literature. The prominent example in my sample - 1983:M3-2011:M128 - is the abrupt and persistent
8.8% drop9 in the global production of crude oil during to the Persian Gulf war, which began in August
1990 and lasted until February 1991. It is uncontested in the literature that a negative shock to the supply
8 See
Appendix I for a detailed description of the oil-market-speci…c and economy-wide events during the sample period.
statistic is taken from Hamilton (2003).
9 This
4
curve of crude-oil increases its price. Albeit, to argue that it also leads to a negative return in the aggregate
stock-market requires substantiation.
Following the standard decomposition in Campbell and Shiller (1988) and Campbell (1991), we know
that variation in the current-period return - relative to its expected value - is driven (directly) by variations
in current-period cash-‡ows, expected future cash-‡ows, and (inversely) by variations in expected future
returns.
Current-period cash-‡ows are negatively a¤ected because oil is a real input to the production of many
goods - most important of which is transportation10 - and as such, changes in the oil-input availability cause
opposite changes in costs and like changes in cash-‡ows. The e¤ect on a speci…c company’s cash-‡ow would
depend on whether the company is a net-producer or net-consumer of oil. For the world and for the US
economies as a whole, oil is a net-input and increases in oil prices due to oil production-‡ow disruptions
depress aggregate cash-‡ows.
Future cash-‡ows are expected to be negatively a¤ected to the extent that the oil supply shock is persistent
- which it realistically is - and to the extent that there exist economic frictions which induce lags in the
transmission of the negative shock to the rest of the economy as has been found by Hamilton (1983; 2003)
and others.
The required rate of return is comprised of the risk-free rate and of a risk-premium. The increase in the
risk-premium in turn goes through two channels. The …rst channel is a generic countercyclicality which has
been documented extensively in the empirical literature (see Fama and French (1989)) and has been modeled
most successfully in consumption-based asset-pricing models as being due to externalities in consumption
(see Campbell and Cochrane (2001)). The other channel through which the risk-premium is a¤ected is the
extent to which the current oil production-‡ow shock is associated with an increase in the uncertainty about
future oil-production ‡ows. This directly increases the amount of undiversi…able risk in the economy and
leads to a higher aggregate risk-premium in equities. I model an increase in uncertainty separately as a
Precautionary Demand Shock of Type II (Item 3 in Table I) in order to elucidate its e¤ects more clearly.
The risk-free rate could be a¤ected in either direction as a result of an adverse productivity shock,
whether or not related speci…cally to oil. One e¤ect could be described as a ‡ight-to-quality from risky to
safe assets, which leads to a decrease in the risk-free rate. On the other hand, the decreased productivity
of capital makes the representative household want to divest. But, since investment is costly to adjust - as
was found necessary to incorporate in the modeling of production-based economies (see Jermann (1998)) the aggregate supply of capital cannot be easily decreased. To make these aggregate feasibility conditions
consistent with the desires of the capital owners, the interest rate must rise su¢ ciently far to discourage
divestment. I do not …nd empirical evidence for interest rate changes around oil-market-speci…c events in
1 0 Transportation
fuel expenditure is roughly 2=3 of total petroleum expenditure in the United States according to EIA.gov.
5
either direction. In the general equilibrium model, I choose a modeling and parameterization approach which
produces only moderate variability in the risk-free rate in response to productivity shocks.
If markets are e¢ cient, the oil price increase and stock market decrease should occur simultaneously as
agents react immediately to re‡ect the lower current and future dividends in addition to the higher expectations of the stock-market required rate-of-return. Hence, this type of shock induces a negative instantaneous
correlation between the oil price change and stock market return. I propose a straightforward model of an
economy with an oil-sector which produces the negative correlation between oil price changes and aggregate
stock-market returns, conditional on an oil production-‡ow disruption. I show that this phenomenon is
robust to several model speci…cations of varying complexity. Empirically, the stark change in the correlation
to very negative territory around war-dates is further corroboration.
A Precautionary Demand Shock of Type I is de…ned as a news shock that informs agents that in future
periods there will be an oil production-‡ow disruption (with certainty). Institutions that carry crudeoil inventory such as re…ners and shippers increase their demand for oil-storage in the current period, in
anticipation of the future shortfall of oil supply, which crowds-out the amount of oil as demand for input
to production. It is the storage technology that induces the direct relation between the current-period spot
oil price and the expected future spot price. The resulting decrease in the amount of oil that is available as
input to production in the current-period leads to the same macroeconomic e¤ects as in the current-period
production ‡ow disruption (i.e. Supply Shock) stated above. Following the same logic, we get the same
identi…cation encapsulated in a negative correlation between oil and stock market returns.
A Precautionary Demand Shock of Type II is de…ned as a shock to the second moment of oil production
which is my approach to modeling increased uncertainty. The same inventory-maintaining agents would
increase their demand for oil-inventory and crowd-out the amount of oil demanded as input to production.
The mechanism by which this comes to be is the desire of the stake-holders in the oil-sector to smooth
their cash-‡ows, which is exacerbated by the existence of a convenience yield that rises at an increasing
rate, as inventories approach zero. In order to avoid costly stock-outs, oil-companies choose to increase
their inventories in the face of increased uncertainty about oil production which leads to a decrease in the
aggregate amount of oil supplied as input to production. This e¤ect scales with the risk-aversion of the
stakeholders in the oil-sector, which in an incomplete markets may have a higher degree of risk aversion than
the aggregate. Again, the resulting decrease in oil inputs to consumption leads to the same e¤ects as the
…rst oil supply shock and to the identi…cation by negative correlation between oil and stock market returns.
A prominent example of a mixture between precautionary demand shocks of type I & II is the run up
in the price of crude oil in late 2002 - when George W. Bush II passed Iraq Resolution in Congress. The
legislation preceded the (anticipated) invasion of Iraq by the US armed forces in March 20th, 2003. These
6
precautionary shocks have been studies by Kilian (2009a), Kilian and Alquist (2010) and Kilian and Murphy
(2012) where they are hypothesized to be associated with similar macroeconomic e¤ects.
Speculation in the crude-oil futures market has been claimed to have caused part of the large increase in
the price of oil in 2007-2008. Furthermore, it is claimed that regulation should be imposed in order to curb
speculation (see Masters (2008)). While the precise de…nition of speculation is still in debate (see Fattouh,
Kilian, and Mahadeva (2013) for an overview), I will herein set my own de…nition. Speculation in the oil
market is a change in the prices of spot and future oil, that is not warranted by the fundamental determinants
of supply and demand, and that does not lead to a change in the quantities of oil produced, consumed or
carried-over as inventory. It is realistic to assume that - due to unmodeled behavioral reasons - the aggregate
assessment by market participants of the price of a barrel of crude oil can diverge from its fundamental
value. The fundamental value, in turn, is the equilibrium outcome of supply and demand of oil consumption,
production and inventory which in turn are functions of the state-variables and of the distribution of future
shocks in the economy. It is not consistent that speculation, de…ned in this way, would be predictable because
if it were, other speculators would take bets against it and drive it away. Furthermore, it is not consistent
that fundamental oil-market participants would react to speculation because if they did - by changing the
amounts of oil they supply and demand - the unwarranted deviation would also be eliminated or at least
minimized. I therefore model the fundamental suppliers and demanders of crude oil as being captured by
this behavioral aberration in a way that leads to no-change in the quantities of oil produced, consumed
and carried-over. An increase in the price of oil associated with a speculation shock leads to a transfer of
resources, in the current-period, to the stake-holders in the oil-sector at the expense of stake-holders in the
rest of the economy, without a¤ecting any other real quantities in the economy. This results in a negative
correlation between oil price %-changes and stock-market returns.
On the other hand, the main economic shock that leads to a positive correlation between oil and stockmarket returns is a shock to global real economic activity. An unexpected increase in the rate of economic
activity would lead to an increased demand for oil. At the same time, the increased activity is translated
into increased current-period and future dividends and to a decrease in the discount rate through the usual
countercyclicality which has been empirically documented and theoretically modeled in the literature. Symmetrically, an adverse economy-wide shock - such as the one that lead to the Great Recession - would lead
to a lower price of oil and to negative current-period stock-market returns.
I calculate the correlation between the oil and stock market returns (excl.
month,
t,
oil-companies) in each
using daily return and price change data for the days in that month. De…ned exactly,
t
=
corrt ( log Pdo ; Rdsm ), is the month t correlation between oil % changes and stock market returns calculated
with the daily data f log Pdo ; Rdsm gD
d=1 where d = 1; :::; D are the trading days in month t. The correlation
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series is shown in Figure 1.
[INSERT FIGURE 1]
Notice …rstly the red dots in the bottom half of Figure 1 - which represent signi…cant and negative
correlations. These conspicuously coincided with oil-market-speci…c events. In chronological order, notice
the negative correlations in 1985-6, when Saudi Arabia - the largest member of the OPEC cartel - decided
to increase production in response to persistent cheating by other OPEC members which nearly unravelled
the cartel. A few years after that came the Gulf War in 1990-91. Notice next, the drop in the price of oil
the began in Nov ’93. The exogenous event there was Iraq’s acceptance of UN resolution 715, which was
perceived as signaling a hastened resumption of Iraqi oil exports. The next noticeable oil-market-speci…c
events were the 9/11/2001 terrorist attacks, followed by the Iraq War and congressional approval in 2002-03.
Notice the 2005 hurricane season which severely damaged US re…nery capacity. Also, notice the negative
correlations in May-Aug 2008 during which the price of oil climbed to an all time high of $145 per barrel
for no clear exogenous reason and has been proposed in front of the US Senate (see Masters (2008)) as
evidence of deleterious …nancial speculation. Lastly, notice the stark change of the correlation from positive
to negative territory in Feb 2011, during what is known as the Libya civil war which had also resulted in
actual oil supply disruption. It is clear therefore that the major periods in which the oil-price %-change
correlated very negatively with the stock market returns could be traced to conspicuous oil-market-speci…c
events.
A closer look at the Gulf War reveals that, between Aug’and Oct’of 1990, when Saddam Hussain invaded
Kuwait and con‡agrated its vast oil …elds, the price of oil increased about 87:6% while the stock market
decreased
17:3%. On Jan’ 17th 1991, the day the USA initiated operation Desert Storm with massive
aerial bombardments, the price of oil declined
33% while the price of the stock market index increased
5:66%. According to the suggested identi…cation, this was an adverse oil-market-speci…c shock which was
followed by its reversal. It is interesting to notice that for a straight …ve-months the returns correlated very
negatively, as new information was priced daily into the assets’prices. It is apparent that new information
regarding oil-production disruptions in Kuwait was dominating global asset prices on a daily basis for a
prolonged period of time.
This inverse relation between oil price changes and stock market returns also existed - in monthly frequency - during the large ’73-’74 and 79’-80’oil price hikes that were associated with the OPEC oil embargo
on the west, and with the Iranian Islamic revolution, respectively. These periods are not included because
of the unavailability of daily oil price data, which is the frequency required for my identi…cation.
8
On the other hand, notice the positive correlations during the recent economic bust and recovery in
2008-10 and during the Asian and Russian …nancial crisis in 1997-8, which culminated with the blow-up of
Long Term Capital Management. Both events are associated with major adverse economy-wide shocks. For
example, on September 15th 2008 - when Lehman Brothers …led for bankruptcy - the price of oil dropped
5:6% and the stock market tanked
4:6%. According to the suggested identi…cation, this was an adverse
economy-wide shock that was not directly related to the oil market.
1.2
Comparison to Similar Work
The …rst empirical paper published that explicitly disentangled di¤erent shocks in the oil market is Kilian
(2009a). Three supply and demand shocks in the oil market are identi…ed there: (1) shocks to the current
physical availability of crude oil; (2) shocks to the current demand for crude oil driven by ‡uctuations
in the global business cycle; and (3) shocks driven by shifts in the precautionary demand for oil. The
methodology employed is a structural VAR for the (1) %-change in global crude oil production; (2) for
an index of real economic activity based on international shipping freight rates; and (3) for the real price
of crude oil based on the Re…ner’s Acquisition Cost (RAC). Exact identifying-restriction and a recursive
structure for the multiplier matrix are imposed in order to decompose the reduced form errors of the VAR
into the orthogonalized structural shocks labeled above. Then, real US GDP growth rates and in‡ation are
regressed on lags of the structural shocks to test if these macrovariables react di¤erently to the shocks. In
an extension paper, Kilian (2009b) examines the e¤ects that these di¤erent shocks have on real US stock
returns by adding them to the VAR above and imposing more identifying restrictions. Kilian and Murphy
(2012) incorporates data on above-ground oil inventories in OECD countries and use an identi…cation-bysign-restrictions methodology to show similar e¤ects.
My results are consistent with the evidence in Kilian (2009a; b) and Kilian and Murphy (2012). My
identi…cation approach di¤ers in that the former uses price and quantity data in monthly frequency for
identi…cation, while I use price data alone, in daily frequency. The shortcoming of aggregate quantity data
is that it is measured with error, is subject to smoothing, and is not forward looking, save for above-ground
inventory data as explained in Kilian and Murphy (2012). My approach is less parametric than the structural
VAR methodology and is nimble in highlighting the events of interest. The advantage of the structural VAR
approach over mine is that it is able to distinguish between an episode of two o¤setting shocks and an episode
of no-shocks. I therefore increase the frequency employed, and use daily return and price change data, in
order to more clearly uncover the underlying time-varying correlation11 . It is mostly the case that one of the
1 1 Second moments of return and price change distributions are theoretically observable in a continuous-time brownian-motion
setting, if data can be sampled at in…nitesimal frequency.
9
two types of shocks dominates the daily time-series which results in sharp contrasts between positive and
negative correlations, in correspondence to prominent exogenous events, as evident in Figure 1.
Two other recent papers - by Silvennoinen and Thorp (2011) and by Büyükşahin and Robe (2011) relate the seemingly structural increase in the correlation during and after the ’08-’09 …nancial crisis to the
"…nancialization" of the oil market. That is, to the increased …nancial ‡ows into hedge funds that trade in
commodities and into commodity linked Exchange Traded Funds (ETF). They argue that these …nancial
innovations have removed informational frictions and resulted in a higher, positive correlation. I argue more
precisely, that the alleviation of informational frictions resulted in a more prominently positive correlation
when economy-wide shocks prevail but more prominently negative correlation when driven by oil-marketspeci…c shocks. The Libya civil war in Feb 2011 perfectly illustrates that the correlation turns very negative
when an oil-market-speci…c shock hits, even in light of secularly increased …nancial ‡ows into the oil market
which are characterized by more positive correlation.
Wei (2003) contains the only DSGE model, to my knowledge, that studies the relation between the oil
price changes and stock market returns. It was brought forth in order to study if the 1973-74 oil price spike
can account for the corresponding stock market decline. He assumes that the price of oil is given exogenously
and …nds a negative relation between oil price changes and stock market returns but that the stock market
actual decline was an order of magnitude larger than the one implied by his calibrated model. My model goes
further in showing the plausibility of that historical stock market decline by adding an uncertainty shock
to oil production combined with a high-degree of local curvature for the representative consumer-investor,
which signi…cantly enhances the reaction of stock prices, but which Wei (2003) abstracts away from. In
fact, my model implies a roughly 1-to-1 percentage decrease in the value of the stock market index (cum
dividends), relative to the percentage increase in the price of oil.
2
2.1
Literature Review12
Oil and Economic Growth
A lot has been written about the relationship between oil price shocks and economic growth. A common
approach in the work done, both empirical and theoretical, is to evaluate the response of macroeconomic
aggregates to changes in the price of oil. In an important paper, Hamilton (1983) showed that nominal oil
price changes in the periods 1949:Q2-1972:Q4 are negatively correlated with real GNP growth rates in future
periods. He argues for causality along the lines of Granger (1969), and shows that oil price changes could not
12 I
apologize towards authors which I undoubtedly have left out. I will gladly add a reference to your work or edit an existing
one.
10
be predicted from earlier movements in other macro variables. He corroborates the argument by explaining
the institutional structure in charge of oil price setting in the US during those periods and he argues that
most of the oil spikes during those periods can be attributed to exogenous events such as military con‡icts
in the middle east.
Hamilton (2003) shows results of a simple autoregression with the purpose to predict quarterly real GDP
growth rates,
log Y , with information available at time
1, which consists of lagged real GDP growth
rates and lagged quarterly nominal oil price13 rates-of-change, log P o .
log Y = 1:14 + 0:20
(0:18)
(0:09)
0:004
(0:026)
where quarter
log Y
log P o
1
+ 0:05
(0:09)
0:027
1
(0:026)
log Y
log P o
2
0:1
(0:09)
0:034
2
(0:0026)
log Y
3
log P o
3
0:19
(0:09)
log Y
0:065 log P o
(0:027)
4
(1a)
4
is running on the periods 1949:Q2-1980:Q4
The coe¢ cient on the fourth quarterly lag of oil price %-change ( log P o 4 ) is negative and statistically
signi…cant (t-statistic =
2:4), and an F -test leads to a rejection of the null hypothesis that the coe¢ cients
on lagged oil prices are zero with a p-value of 0:005. Hamilton’s reasoning for the choice of nominal quarterly
oil prices on real GDP growth rates was that the nominal oil price changes are of a magnitude larger than
the changes in the general price level and that the results were materially unchanged when accounting for
in‡ation.
2.1.1
Other Functional Forms
During the 1980’s the price of oil declined signi…cantly and especially so in 1986 when OPEC nearly collapsed
with the unilateral withdrawal of Saudi Arabia - the swing producer of the cartel - from the production
quota system, in Aug-’85. The fact that these oil price declines failed to bring about an economic expansion
resulted in the literature on the asymmetry of the macroeconomic variables’response to oil price increases
and decreases. If (1a) is re-estimated with data that includes the 1980’s, the coe¢ cients on lagged oil price %changes become less negative which, according to Hamilton (2003), is due to the symmetry mis-speci…cation
implied by (1a). Speci…cally, for
log Y = 0:72 + 0:28
(0:11)
(0:07)
0:003
(0:026)
1 3 The
running from 1949:Q2-2001:Q3 we have:
log Y
log P o
1
1
+ 0:13
(0:07)
0:003
(0:006)
log Y
log P o
2
2
0:06
(0:07)
0:004
(0:006)
log Y
log P o
3
3
0:12
(0:07)
log Y
0:016 log P o
(0:007)
oil series data Hamilton (2003) uses is the Producer Price Index for crude oil (WPI0561)
11
4
4
(1b)
Business cycle models such as Lilien (1982), Davis (1987), Loungani (1986) and Hamilton (1988) demonstrate that sectorial imbalances lead to asymmetric reaction of unemployment and incomes to energy and
other shocks. These studies have noted that an oil price increase will decrease demand for some goods but
possibly increase demand for others. If it is costly to reallocate labor or capital between sectors then an
oil price shock, either positive or negative, will have contractionary implications in the short-run. A price
decrease also depresses demand for some sectors, and unemployed labor is not immediately shifted elsewhere.
So, an oil price increase will have negative implications to economic activity due to the role of oil as a real
input to the aggregate production function and due to the sectorial imbalances created by the relative price
change per se. On the other hand, an oil price decrease will have both positive and negative implications
to economic activity. The positive implication is due to it’s role in the aggregate production function as an
input which has become cheaper. The negative implication is the disruptive e¤ect of reallocating resources
between sectors, due to the relative price change per se, which is costly.
Mork (1989) was the …rst to estimate separately the e¤ects of real oil price increases and decreases on
real GNP growth and found that in the periods 1949:I-1988:II, real oil price increases had a negative e¤ect
on real GNP growth rates but that real oil price decreases had no e¤ect. His speci…cation of the oil price
increase series was the maximum between the (a) log di¤erence of the oil price level series and (b) zero. The
oil price decreases series was created in a similar way.
Mork’s interpretation was subsequently challenged by Hooker (1996) who showed that even this asymmetric speci…cation …ts the data poorly in subsequent periods and that oil prices cease to Granger-cause
macroeconomic variables since 1986. In response to this, Hamilton (1996) notes that most of the increases in
oil prices since 1986 have followed immediately on the heels of even larger decreases. He claims that the key
question is whether the oil price increase is big enough to reverse any decrease observed in the immediately
preceding quarters. Hamilton (1996) creates a variable named "net-oil-price-increase" which is the maximum
of (a) zero, and (b) the di¤erence between the log-level of the nominal crude oil price in the period
and
the log of the maximum value for the level achieved during the previous four quarters.
Lee et al. (1995) suggests that what matters is how surprising an oil price increase is based on the
observed recent changes. He does this by estimating a GARCH(1; 1) model and allows for 4 lags in the
mean for the real oil price %-change series. He uses the the orthonormalized errors from this model to predict
real GDP growth rates.
Hamilton (2003) examines these suggested non-linear speci…cations for the oil price %-change series and
concludes that the transformation proposed by Lee et al (1995) seems to do the best job of explaining GDP
growth rates. In addition, a measure that speci…es that an oil shock occurs when oil prices exceed their
12
3-year peak also seems to be acceptable. The 3-year "net-oil-price-increase" measure is justi…ed by him since
the price increase in the …rst half of 1999 set a new annual high but it did not recover all that was lost during
the Asian …nancial crisis of 1997-8.
2.1.2
Theory models
Many models built to study the e¤ects of oil price changes on the economy begin with a production function
that relates output to inputs of capital, labor, and energy. In these models, an exogenous decrease in the
supply of oil reduces output directly by lowering productivity and indirectly to the extent that lower wages
induce movement along a labor supply curve (Rasche and Tatom (1977; 1981); Kim and Loungani (1992),
changes in business markups (Rotemberg and Woodford (1996)), or capacity utilization rates (Finn (2000)).
These models view recessions as supply driven rather than demand driven. A supply shock in the …nalgood market a¤ects the ability of …rms to produce the gross domestic product, which means that it directly
a¤ects either the prices or quantities of factor inputs or the production technology. Demand shocks in the
…nal-good market, on the other hand, a¤ect spending by the households, business, and government that
purchase the GDP. This is not to be confused with the language of this paper which refers to supply and
demand shocks in the crude-oil market. According to the above models, an oil price increase produces a
recession because it makes cars more costly to manufacture. This seems to be in contradiction to reports
in the trade and business press, in which the problem is portrayed as a decline in the number of cars
demanded by consumers; see for example the trade press accounts in Lee and Ni (2002). Several early
models focused instead on the demand side e¤ects of an oil price increase. In these models, an increase in oil
prices would increase the overall price level, which, given the Keynesian assumption of rigid wages, reduces
employment. Examples of such models include Pierce and Enzler (1974), Solow (1980), and Pindyck (1980).
Energy prices and availability may be quite relevant for a host of other durable goods purchases, including
housing. In particular, the decision of how far to live away from the workplace is dependent on the cost of
the daily commute. When energy prices and availability are uncertain, it is rational to postpone purchases
of large ticket items as in Bernanke (1983). Recently, several DSGE models have been written to describe
how the price of oil and macroeconomic variables comove in response to di¤erent shocks. Bodenstein and
Guerrieri (2011) and Bodenstein et al (2011) take a two-country model to the data and study the reaction
of macrovariables to country-speci…c oil supply shocks, various domestic and foreign activity shocks, and oil
e¢ ciency shocks. They emphasize the international trade implications in a setting of incomplete or complete
markets between the countries but do not study the stock-market implication. They …nd that oil supply
shocks do not account for a signi…cant share of the variance of the price of oil in an economy where oil is
endowed in each period, but this may be because they abstract away from the storability of oil which makes
oil prices today forward-looking with respect to supply and demand conditions in the future.
13
2.2
Oil and Stock Markets
Several empirical papers have been written on the relationship between the price of oil and stock market
returns. These papers disagree on the e¤ects that oil price shocks have on aggregate stock market returns.
Chen, Roll and Ross (1986) …ndings suggest that oil price changes have no e¤ect on the stock market. Jones
and Kaul (1996) reported a stable negative relationship between oil price changes and aggregate stock returns
that is fully accounted for by their e¤ect on cash ‡ows in the US and in Canada but that is not fully accounted
for by the their e¤ect on cash ‡ows in the UK and Japan. Huang, Masulis and Stoll (1996), however, found
no negative relationship between stock returns and changes in the price of oil futures. Sadorsky (1999)
…nds that oil price changes and oil price volatility have negative and asymmetric e¤ects on stock returns. In
particular, he …nds that oil price increases explain more of the variance in returns than do oil price decreases.
2.3
Theory of Storage and Futures Prices
The theory of storage of Kaldor (1939), Working (1949), Brennan (1958), and Telser (1958) is the dominant
model of commodity forward and futures prices. It relates the commodity’s current and expected future
prices, via the optimality condition of a representative …rm with storage technology. The storage …rm equates
the value of selling the marginal barrel of oil, at the price it would fetch in the spot market, to the value
of carrying-over that marginal unit as inventory to the next period. This optimality condition dictates that
the current-period spot price of oil should equal the expected value of a barrel of oil in the following period,
less the physical storage cost of carrying that marginal unit as inventory, plus the marginal convenience
yield derived from having another unit of the commodity as inventory. This value is discounted to the
current-period by the required rate-of-return that is appropriate for the non-diversi…able risk embedded in
such a transaction. The per-unit physical storage cost includes warehousing, insurance, and administrative
charges. The marginal convenience yield arises because inventory can have productive value. It can arise
when holding inventory of an input lowers unit output costs and replenishing inventory involves lumpy costs.
Alternatively, time delays, or high costs of short-term changes in output can lead to a convenience yield on
inventory held to meet customer demand for spot delivery. For example, a re…ner might maintain crude-oil
inventory in order to smooth his production of gasoline-products. Further down the value chain, a gasoline
distribution …rm might keep inventory in order to smoothly meet unexpected demand. The theory says that
the positive marginal convenience yield on inventory falls at a decreasing rate as inventory increases. It was
developed in order to explain the seasonal behavior of spot and futures prices for agricultural commodities
- in particular, futures prices that are below spot prices before harvests, when inventories are low and the
marginal convenience yield on inventory is high. Routledge (2000) further …nds that volatility is higher when
there is backwardation, which is consistent with the e¤ects of such a convenience yield. Kogan et al (2009)
14
mention that there exists very little theoretical work investigating the pricing of commodity futures using
a production economy framework. They document a V-shaped relationship between the oil futures curve
and oil futures volatility and bring a model that emphasizes the implications of investment capacity and
irreversibility constraints in oil production.
An alternative view splits the futures price into an expected risk premium and a forecast of a future
spot price. French (1986) and Fama and French (1987) bring evidence for the ability of the basis spread,
or the di¤erence between the current-period spot price and the current-period futures price, to forecast
future changes in spot price but …nd only little evidence of variation in the expected risk-premium of futures
contract. Earlier empirical work tries to shed light on the sign of the expected risk-premium in (long) futures
contracts. Cootner (1960) brings evidence of seasonality in the expected premiums in wheat contract which
corresponds to net-long or net-short hedging pressures which vary with the harvest cycle. Dusak (1970)
estimates the unconditional CAPM-betas of several commodity futures contracts and …nds that they are
indistinguishable from zero, while Breeden (1980) develops an ICAPM counterpart and estimates future
contracts consumption-betas. Hamilton and Wu (2012) …nd evidence of a structural break circa 2005 in the
expected risk-premium awarded to long-side investors in oil futures and relate this to an increase in …nancial
‡ows by investors in Exchange Traded Funds (ETF) which roll over investments in near-term oil futures.
3
Empirical Evidence
Please see Appendix II for Data and Variable de…nitions.
3.1
Identi…cation by Correlation
The time-varying correlations which are presented in Figure 1, and serve as identi…cation, are calculated in the
following way. I take the daily CRSP US value-weighted stock market return, Rdsm , and subtract the daily
returns on an the indexes of oil- and coal- companies, Rdoil and Rdcoal , which are comprised of shares from the
same universe of CRSP US traded companies. I create Rdsm =
1
(1 ! oil
! coal
)
d
d
Rdsm
oil
! oil
d Rd
! coal
Rdcoal
d
where ! oil and ! coal are the market-capitalization weights of the oil and coal industries and Rdsm is the
resulting market index return that excludes their returns. The industry returns are obtained from Kenneth
French’s website and their composition is documented in Fama and French (1997). The reason for taking out
the oil-companies index is that it is dominated by Internationally Diversi…ed Oil Companies (IDOC) - such
as Exxon-Mobil and Shell - who have historically had only few assets in the Middle-East and therefore did
not su¤er signi…cantly from oil-production disruptions due to military con‡icts. The owners of the majority
of the vast …elds in the Middle-East are National Oil Companies (NOC) - such as Saudi Aramco or Kuwait
15
Petroleum Company - shares of which are not publicly traded. So, while a production-disruption will hurt the
valuation of an NOC14 , it will lead to two o¤setting e¤ects on the current-period returns of an IDOC whose
production ‡ow is una¤ected. On the one hand, the increase in the price of oil will bene…t the current-period
and future cash‡ows which leads to positive current-period returns. But on the other hand, the increase in
the aggregate risk-premium has the opposite e¤ect. The same goes for coal producers whose cash‡ows are
improved because of the increase in the price of coal by its nature as a substitute for crude-oil, but su¤er
from the rise in the aggregate risk-premium. Furthermore, some oil companies may be fully or partially
hedged with respect to oil price changes, but the companies represented by the entire non-oil US equities
index cannot possibly be fully hedged. In sample, the correlation between the oil and coal industries’returns
and the change in the price of oil was positive during most of the oil-market-speci…c episodes so I exclude
them from the market index to get a sharper identi…cation. The other series that underlies the correlation
is the log di¤erence in the daily spot price of the West Texas Intermediate Crude (WTIC) oil,
The correlation,
t
log Pdo .
log Pdo ; Rdsm ), is for month t and is calculated with intra-month daily data,
= corrt (
f log Pdo ; Rdsm gD
d=1 , where d = 1; :::; D are the trading days in month t. The correlation series is shown in
Figure 1 above.
3.2
Novel Predictor
Next, I create two monthly series,
t
=
j
tj
0
t
and
+
t ,
from the original correlation series, calculated as:
if
t <0
otherwise
Notice that for any month t only one series,
and
t
or
+
t ,
+
t
=
t
0
if
t >0
otherwise
is positive while the other one is zero. The purpose
is to create two orthogonal series of oil price changes representing innovations15 in the price of oil that are
due to either an oil-market-speci…c or an economy-wide shock. Speci…cally, I create:
"oil
t
specif ic
=
t
log Pto
and
"economy
t
wide
=
+
t
log Pto
1 4 I substantiate this claim with a revealed preference argument. Fix the amount of oil-inventory carried over from the previous
period and compare the valuation of an NOC, with oil-storage capabilities, who in the current-period has an oil production
‡ow that is either low (due to an exogenous disruption) or average. The quntities of oil brought to market and carried-over to
the next period in the low state are feasible in the average state, but the converse is not true. Under the assumption of net
discounted cash‡ow maximization we get that the valuation in the low state is lower than in the average state. This leads to a
negative realized current-period return, relative to the expected return, on the equity of an NOC when a low production ‡ow
state materializes.
1 5 Results are the same when allowing for autoregressive lags in the mean of oil or stock-market returns and taking the
innovations from such predictive regressions.
16
I sum "oil
t
specif ic
and "economy
t
specif ic
"oil
(t;t+2)
2
P
i=0
"oil
t+i
wide
over 3 consecutive months to create
specif ic
"economy
(t;t+2)
and
wide
2
P
i=0
"economy
t+i
wide
for clearer visual interpretation, and I present them in Figure 2 below.
[Insert Figure 2]
Notice in the top panel, from left to right, the S -shaped patterns of positive (negative) followed by
negative (positive) changes in the price of oil during the highlighted oil-market-speci…c events: (1) the
temporary breakaway of Saudi-Arabia from the OPEC quota system; (2) the Gulf war; (3) the acceptance
- by Iraq - of UN resolution 715 which was perceived as hastening the process of export resumption; (4)
9/11; (5) Iraq war; (6) the 2005 US Gulf Coast hurricane season; (6) the bout of …nancial speculation in
commodities during the summer of ’08; and (7) the Libya civil war in 201116 . Focusing again on the events of
the Gulf war, in Aug-Sep of ’90 - the …rst two months of …ghting - the price of oil went up 27:8% and 36:9%
while the intra-month daily correlations were
0:829 and
0:625, suggesting an adverse oil-market-speci…c
shock. In contrast, the intra-month correlation in Jul ’90, a month before the war unexpectedly began, was
an uninteresting
was
0:015. In Jan ’91, when the US military got involved, the change in the spot price of oil
27:8% while the intra-month correlation was
0:658, suggesting the reversal of that shock.
Notice in the bottom panel, the negative oil price changes during the Asian …nancial crisis which culminated with LTCM’s blow-up. Notice also the same S -shaped pattern of negative and then positive oil
price changes during the ’08-’09 …nancial crisis and recovery. Focus on the events of the ’08-’10 …nancial
crisis and recovery. This pattern also appears in the top panel since the correlations then were very positive.
More speci…cally, in Sep-Nov ’08, beginning with the bankruptcy of Lehman Brothers, the stock market
experienced returns of
39:48%, and
9:26%,
17:0%, and
7:81%, and the change in the price of spot oil was
13:74%,
21:98%, and while the intra-month correlations were 0:281, 0:413, and 0:742, suggesting an
adverse economy-wide shock. In Mar-Apr ’09, when the stock market recovered 8:91% and 10:2% of what it
had lost, the change in the price of oil was 10:39% and 2:9% while the intra-month correlations were 0:527
and 0:769, suggesting the reversal of the economy-wide shock.
In the predictive regressions that are soon to follow I will test whether changes in the price of oil that
are due to either oil-market-speci…c or economy-wide shocks have di¤erent macroeconomic e¤ects. I will
1 6 The Lybia civil war appears as a positive change in the price of oil that is due to an oil-market-speci…c shock, but, within
that month, the price of oil went up and then down on news of the war’s escalation and practical conclusion, in a similar pattern
to the former events.
17
regress future realizations of the stock-market, excess returns (RX sm ), dividend ( log Dsm ) and real GDP
(
log Y ) growth rates on lags of "oil
specif ic
I combine the two orthogonal series, "toil
and "economy
specif ic
wide
.
and "economy
t
wide
, to create one series of oil price
changes that are due to both oil-market-speci…c and economy-wide shocks, that takes into account that their
hypothesized e¤ects on the aggregate risk premium, and on the real GDP and dividend growth rates, are of
opposite sign, as summarized in Table I. I achieve this by interacting the correlation,
price change,
t,
and the monthly oil
log Pto .
"opposite
t
t
log Pto
For illustration of the methodology, if the price of oil went up 20% in a given month, and the correlation
between oil and stock returns during the days of that month was
0:5, then that 20% rise represents a
value in the combine oil shock series, "opposite
. Alternatively, if the price of oil went down
t
month, and the correlation was +1 then thatm
10% drop represents again a
10%
10% in a given
10% value in the oil shock
series17 , "opposite
.
t
Notice that, in accordance with the identi…cation in Table I, an oil price increase that is associated with a
, and is due to an adverse oil-market-speci…c
negative correlation, is captured by a negative value of "opposite
t
shock. An adverse oil-market-speci…c shock should further cause an increase in the risk premium and a
decrease in dividend and GDP growth rates. On the other hand, an oil price decrease that is associated with
a positive correlation, is also captured by a negative value of "opposite
, and is due to an adverse economy-wide
t
shock. An adverse economy-wide shock further causes an increase in the risk premium and a decrease in
dividend and GDP growth rates. This illustrates that de…ning "opposite
is a compact way to test these e¤ects
t
in a setting of predictive time series regressions. In a predictive regression of stock-market excess returns
we therefore expect a positive coe¢ cient on "opposite
, and in a predictive regression of GDP and dividend
t
growth rates we expect a negative coe¢ cient.
Similarly, I combine the two orthogonal series, "toil
specif ic
and "economy
t
wide
, and create a series of oil
price changes that are due to both oil-market-speci…c and economy-wide shocks, that takes into account that
their hypothesized e¤ects on the expected future change in the price of oil and on the expected premium
from investing-long in oil futures are of the same sign, as is also summarized in Table I. I de…ne it as the
sum of "oil
t
specif ic
and "economy
t
wide
:
1 7 Zero correlation between oil price changes and stock market returns could mean there was neither an oil-market-speci…c
nor an economy-wide shock in that month, or it could mean there was an o¤setting combination of both. But, since the two
o¤set each other, we should expect no-change in the macrovariables of interest either.
18
"same
t
"toil
specif ic
+ "economy
t
wide
For visual clarity I present a 3-months sum,
"same
(t;t+2)
2
P
i=0
"same
t+i
and
"opposite
(t;t+2)
2
P
i=0
"opposite
t+i
[Insert Figure 3]
Notice, in the bottom panel of Figure 3, that the same S-shaped patterns that appeared in the individual
oil-market-speci…c or economy-wide series, now appear in this one time-series. On the other hand, notice
in the top panel of Figure 3 that the patterns that appeared in the oil-market-speci…c get inversed, due to
the interaction with the negative correlation, while the patterns that appeared in the economy-wide series
remain.
3.2.1
Oil Shock Variable as Fitted Returns
This subsection brings to the surface the confounding of stock-market returns in the novel oil shock variables.
In particular, "opposite
can be viewed as projected stock-market returns during month t, as proxied by
t
the contemporaneous oil price change, while allowing for di¤erent signs and magnitudes of the projection
coe¢ cient.
Let state s 2 S
f"
"; " + "g
fOil-Market-Speci…c;Economy-Wideg. I assume state dependence
only in the covariance between intra-month daily oil price changes and stock-market returns and drop the
superscript s where I assume no state-dependance. Let d = 1; :::; D be the trading days in month t. Let
sm
log Pdo and
sm
log Pto be the daily and monthly spot oil price changes, and let Rd and Rt be the daily
r h
i
2
E f log Pdo E ( log Pdo )g as the popand monthly de-meaned stock-market returns. De…ne o;d
ulation value of the daily standrad deviation of spot oil price changes. I assume for this exposition, that
the population standard deviation is constant within each month, but varies between months. In other
words, I assume intra-month homoskedasticity such that 8 fd; d0 g 2 t, I have o;d = o;d0
o;t . I deh sm
i
…ne similarly, ro;d E Rd f log Pdo E ( log Pdo )g js , as the population value of the state-dependent
daily covariance between oil price changes and stock-market returns where I also assume intra-month sta-
s
tionarity such that 8 fd; d0 g 2 t, I have sro;d = sro;d0
ro;t . In monthly frequency I similarly de…ne,
r h
i
h sm
i
2
E f log Pto E ( log Pto )g and sro E Rt f log Pto E ( log Pto )g js . The rest of the deo
…nitions in intra-month daily frequency, in monthly frequency and their interdependencies are summarized
in Appendix II.
19
sm
I now project the daily de-meaned stock-market returns on daily oil price changes to get18 Rd
s
ro;t
log Pdo +
sm
d
E[Rd j log Pdo ; s] =
for d = 1; :::; D trading days in month t.
s
ro;t
=
Then, take conditional expectations,
log Pdo .
sm
Project also monthly de-meaned stock-market returns on monthly oil price changes, E[Rt j log Pto ; s] =
s
ro
log Pto =19
sm
E[Rt j
log Pto ;s]
s
ro;t
s
ro;t
log Pto for t = 1; :::; T months in the sample.
log Pto =
Rearrange to get
.
The …rst novel predictor could now be equivalently expressed as:
"opposite
t
t
log Pto =
o;t
r;t
sm
E[Rt j log Pto ; s].
Which means that "opposite
could be interpreted in two ways: (1) Monthly changes in the price of oil that
t
are due to oil-market-speci…c and economy-wide shocks, which takes into account that their macroeconomic
e¤ects on the aggregate risk-premium, and GDP and dividend growth rates, are of opposite sign. This is
accomplished by scaling the oil price change with
s
t,
which allows the correlation to take on negative and
positive values; (2) Projected monthly stock-market returns, proxied with contemporaneous oil price changes,
in a setting which allows for sign- and time- variation in the projection coe¢ cient. The projected stocko;t
market returns are further scaled by
r;t
has prominent variability
which gives intuition to why "opposite
t
around oil-market-speci…c events, as the volatility in the oil price change dominates then.
The second novel predictor could be similarly expressed as:
"same
t
+
t
+
t
log Pto =
sm
o;t
E[Rt j
r;t
sm
o;t
E[Rt j
r;t
log Pto ;s] if
t
log Pto ;s] if
t <0
0
The above gives rise again to two interpretations of "same
: (1) Monthly changes in the price of oil that
t
are due to oil-market-speci…c and economy-wide shocks, which takes into account that their macroeconomic
e¤ects on the expected future change in the price of oil and on the risk-premium embedded in oil futures
prices are of the same sign. This is accomplished by scaling the oil price changes by
+
t
+
t
which takes
the absolute value of the correlation if it is negative; (2) Projected monthly stock-market returns, proxied
with contemporaneous oil price changes, in a setting which allows for sign- and time- variation in their
projection coe¢ cient. Again the projected stock-market returns are scaled by
o;t
r;t
but are further scaled by
( 1) if the projection coe¢ cient is negative, which keeps the sign of the monthly oil price change.
sm
1 8 For
practical reasons I assume that other contemporaneous factors in determining Rd
that ommitted variables do not bias st .
1 9 See Appendix II for the truly innocuous assumptions that result in this identity.
20
are orthogonal to
log Pdo such
3.3
3.3.1
Predictive Regressions
Excess Returns
I now turn to predicting excess stock market returns using the newly constructed oil shock series. An
adverse oil-market-speci…c shock, that leads to an oil price increase, should be associated with an increase
in aggregate risk premium that may be realized, ex-post, as higher than average excess returns. On the
other hand, an adverse economy-wide shock, that leads to an oil price decrease, should also be associated
with an increase in aggregate risk premium. Both of the shocks will be captured by a decrease in the
proposed oil shock series, "opposite
, due to the di¤ering signs of the correlation in each case. Thus, in
t
a predictive regression of excess stock market returns on the lags of the oil shock variable I expect to
see a negative coe¢ cient. I predict 12 months of cumulated excess returns, rolling monthly, calculated,
sm
sm
RX(t;t+11)
= R(t;t+11)
f
. I predict excess returns with four lags of cumulated 3-months oil shocks.
R(t;t+11)
Since the 12-months excess returns are overlapping and the regression is rolling monthly, I will estimate
standard errors that are corrected for serial correlation following Hansen and Hodrick (1980), with 12 lags
entering the estimated variance-covariance residual matrix, and following Newey and West (1987), with 24
lags entering the estimated variance-cavariance matrix. The results are robust to the selection of lags and to
the forecast horizon. Throughout the paper I choose to present the most visually interpretable and reliable
results, although better …t can be achieved with slight variations in the de…nitions of the variables. I dedicate
Appendix III for robustness checks, in which I control for possibly confounding variables that also capture
changes in expected returns. I also test whether the predictability is driven by the oil-market-speci…c or
economy-wide shock series by including them separately. In addition, I address issues of serial correlation
in the predictor variable, and criticism of over…t due to the seeming dominance of a few episodes in the
predictor time-series. Lastly, I run a horse race with the other suggested variables in the literature.
The main results of this subsection are presented in Figure 4 above, which plots the realized (blue line)
and predicted (red line) excess 12-months stock market returns in the following linear, in-sample regression
model:
sm
RX(t+1;t+12)
=
o
+
opposite
1 "(t 2;t)
+
opposite
2 "(t 5;t 3)
+
opposite
3 "(t 8;t 6)
+
opposite
4 "(t 11;t 9)
+ u(t+1;t+12)
[Insert Figure 4]
The t-statistics are reported in row 1 of Table III and in the top-right corner of the …gure. Notice that all
four coe¢ cients on the oil shock series are negative and that the …rst and second lags are highly signi…cant
with corrected t-statistics of
4:05 each. The t-statistic for the sum of the four explanatory variable lags is
21
also highly signi…cant with a value of
3:00. The R2 equals 10:3%. The statistics and …t could be further
improved by tweaking the de…nition of "opposite
, but I keep it in its simplest and believable form and I do not
t
dredge the data. The blue line is the realized 12-months excess returns while the red line is the …tted values,
which traditionally are interpreted as expected excess returns in the stock-market over the risk-free rate. In
other words, the red line is the aggregate risk premium in equities prevalent at the time of the forecast, based
on information up to that time, and relevant for the following 12-months horizon. Notice the very good …t
of the regression during the stock market bounce that came on the heels of the ’90-’91 oil supply shock of
the Gulf war. It seems that the adverse supply shock that hit the oil market in Aug-Oct ’90 was associated
with a higher aggregate risk premium that was realized, ex-post, and over the following 12 months, as higher
than average excess returns - over 25% - coming out of that economic shock and recession. In Jan ’91, as
the USA got involved in the war, stock and oil market participants viewed this as a sign that supplies would
be soon restored and the price of oil went down, the stock market rebounded, and the risk premium went
back down. Notice also the very good …t of the regression during the stock market recovery after the ’08-’09
crash. It seems that in late ’08 market participants realized a major recession was imminent as a result of the
…nancial and housing crisis. This led to a precipitous drop in the price of oil, as investors were discounting
lower demand for this major commodity, and in the prices of stocks, as investors increased their required
rate of return from risky investments and discounted lower dividend growth rates. This economy-wide shock
was associated with a rise in the aggregate risk premium which was captured by the oil shock variable and
was realized as higher ex-post excess returns coming out of that recession. Notice also the similar rise in
expected excess returns associated with the Russian …nancial crisis.
[Insert Table II]
Excess Returns - Robustness
Table II above presents the statistics for the excess stock market returns’
predictive regressions. In row 1, I add the popular covariates used to control for changing expected returns,
dpt , deft and termt , which are the dividend yield, corporate default yield spread and the US government term
yield spread. This follows evidence originally documented in Fama (1990). In addition, it is clear from the
exposition in subsection 3.2.1 that stock market returns are confounded in "opposite through the interaction
of the oil price change with the correlation. Speci…cally, when an adverse oil-market-speci…c shock occurs
then the change in the price of oil is positive and the return in the stock market is negative. The hypothesis
is that this is associated with a rise in the aggregate risk premium. On the other hand, when an adverse
economy-wide shock occurs then the change in the price of oil is negative and the return in the stock market
is also negative. The hypothesis this time suggest that this is also associated with a rise in the aggregate risk
premium. Therefore, I control for four lags of stock market returns in the predictive regression and show,
22
in row 4, that the results are materially unchanged and that the coe¢ cients on the lagged stock-market
returns are insigni…cant. Note that the dividend yield, dpt , which also captures mean-reversion in stock
returns comes out to be marginal with a t-statistic of 1:45. Explicitly, the results reported in row 4 are for
the following equation:
sm
RX(t+1;t+12)
=
::: +
::: +
sm
1 R(t 2;t)
sm
2 R(t 5;t 3)
+
opposite
1 "(t 2;t)
+
opposite
2 "(t 5;t 3)
+
+ Xt :::
sm
3 R(t 8;t 6)
+
sm
4 R(t 11;t 9)
opposite
3 "(t 8;t 6)
+
opposite
4 "(t 11;t 9)
+
+ :::
+ :::
::: + u(t+1;t+12)
Where I now collect Xt = (dpt ; deft ; termt )0 and
=(
dp
;
def
;
term
).
In row 3 of Table II, I separate oil-market supply and demand shocks by the sign of the estimated
correlation and show that they have opposite e¤ects on future stock market excess returns, as hypothesized
in Table I and as arises from the DSGE model that is to follow. I replace the four 3-months lags of "opposite ,
with four lags of each of the separated shocks, "economy
sm
RX(t+1;t+12)
=
::: +
ew economy wide
1 "(t 2;t)
::: +
+
os oil specif ic
1 "(t 2;t)
wide
and "oil
specif ic
+ Xt + :::
ew economy wide
2 "(t 5;t 3)
+
ew economy wide
3 "(t 8;t 6)
os oil specif ic
2 "(t 5;t 3)
+
os oil specif ic
3 "(t 8;t 6)
+
. Speci…cally, I run:
+
+
ew economy wide
4 "(t 11;t 9)
os oil specif ic
4 "(t 11;t 9)
+ :::
+ :::
::: + u(t+1;t+12)
Notice that all four lags of the supply shocks are positive, meaning that an oil price increase due to a
supply disruption is associated with an increase in the aggregate risk premium. The …rst and second oil
supply shock lag is individually signi…cant with a t-statistic of
2:17. On the other hand, notice that all
four lags of the demand shocks are negative, meaning that an oil price increase due to a demand shock
is associated with a decrease in the aggregate risk premium. The …rst and second oil supply shock lag is
individually signi…cant with a t-statistic of 2:61.
It is evident that there are a few major events in the shocks time series, of which the ’90-’91 Gulf war,
and the ’08-’09 …nancial crisis and recovery stand out in particular. I exclude these periods from the data-set
23
and report the results in row 4 of Table II. All four oil shock lags are negative, the t-statistic for their sum
is
2:46, and the second lag is individually signi…cant with a t-statistic of
2:46 as well. Better statistics
are produced when I exclude th ’87 Black Monday stock market crash which I argue was niether due to an
oil-market-speci…c shock nor due to an economy-wide shock. The Black Monday crash was limited to global
stock exchages and seemed to be a behavioral phenomenon combined with a technical systems failure that
was speci…c to stock and not commodities markets.
Another way to use the correlation to identify oil-market-speci…c and economy-wide shocks is to de…ne
the following dummy variable:
0
B
dt = @
0
B
B
dt = B
B
@
I then rede…ne "opposite
t
dt
1 if
1 if
1
>
0
t
C
A
t <0
1 if
1 if
or
t
t
> 1:96
<
( t)
1
C
C
( t) C
C
A
1:96
0 otherwise
log Pto or "opposite
t
20
log Pto and show in row 5 of Table II that the
dt
results are robust to this transformation.
Yet another variation is to use only the signed-correlation values that are locally signi…cant by de…ning:
t
0
B
=@
t
if
j t j > 1:96
0 otherwise
In row 6 I report similar results when using "opposite
t
t
1
( t )C
A
log Pto ,
In response to criticism along the lines of Forbes and Rigobon (2002), which show that heteroskedasticity
biases correlation estimates in the direction of overrejection, I de-GARCH the daily return and price-change,
series-by-series, and calculate the correlation between the i.i.d (along-time) errors. Speci…cally, I run a
GARCH(1; 1) with a constant mean for the daily oil price change series and then for the daily stock market
return series over the entire sample. Then, I calculate intra-month contemporaneous correlations between
the orthonormalized errors of the series, and use these to identify the oil shocks. The results are reported in
row 7 of Table II and are materially unchanged.
2 0 ( ) is estimated using bootsrapped correlations using the same intra-month daily data used to calculate
t
critical value for the t -statistic of a two-sided signi…cance test with a 5% chance of a type I error.
24
t.
1.96 is the
Another criticism which I address is along the lines of Ferson, Sarkissian and Simin (2003) and NovyMarx (2012), who show that if expected stock market returns are highly persistent then, as a result of
spurious regression bias and data mining, right-hand-side variables such as the dividend yield - which is
highly persistent itself, produces biases in the direction of overrejection in predictive stock market excess
return regressions. This is relevant in this case because even though "opposite
is not persistent at all, the 3t
months sum "opposite
(t;t+2) is, not surprisingly, positively autocorrelated with its 1st and 2nd monthly lags. I tackle
opposite
this in two ways, the …rst is to sample "opposite
, which
(t;t+2) every 3 months and create a quarterly series "
again has practically zero autocorrelation. Row 8 of Table II reports similar, highly signi…cant results for
this quarterly regression of 12-months excess returns on a constant and four lags of quarterly oil shocks. The
second way is instead of summing "opposite
for 3 months and including 4 lags, I include 12 lags of "opposite
in
t
t
the predictive regression. The pattern of …tted returns remains exactly the same with conspicuous variation
in 12-months expected excess returns during the oil-market-speci…c and economy-wide events. The t-statistic
for the sum of the 12 lags of "opposite
is signi…cantly negative with a value of
t
2:17, and the
2
df =15
value to
test the regressions’signi…cance has a value of 39:8 with a p-value of 0.0005.
Lastly, I run a horse race with four other oil shock variables that have been proposed in the literature21
as useful at capturing variation in the price of oil that is due to oil-market-speci…c events and were said to
cause economic slowdowns. In row 9 I use the basic oil price change (log-di¤erence) as Hamilton (1983) …rst
suggested. The coe¢ cients come out to be insigni…cant and negative which counterintuitively suggests that,
identi…ed in this way, adverse oil-market-speci…c shocks are associated with a decline in the aggregate risk
premium. The reason of course is the confounding of oil-market-speci…c and economy-wide shocks where the
latter seems to dominate. In row 10 I use oil price increases following Mork (1989) which de…nes an oil supply
shock as the log-di¤erence between oil prices, if it is positive, and zero otherwise. Results are insigni…cant
and of the wrong sign. In row 11, I try Hamilton’s (1996; 2003) net-oil-price-increase measure of oil supply
shocks which is de…ned as the log di¤erence in oil prices, if the most recent price is above the previous 12- or
36- months high, and zero otherwise. Results are insigni…cant and of the wrong sign as well. In row 12 I try
Lee’s (1995) measure of oil supply shocks which are the normalized errors from a GARCH(1; 1) model with
four autoregressive lags for the mean monthly oil price change. Again, results are insigni…cant and of the
wrong sign. Figure 5 below is a visual comparison of these competing "oil supply shocks" to my proposed
oil-market-speci…c shock.
[Insert Figure 5]
Notice …rstly the Oil Price Change series, which is the second panel from the top and represents the
3-months oil price change. Notice the large negative price change during what was associated with Saudi
2 1 It
is beyond the scope of this work to recreate the results in Kilian (2009b).
25
Arabia’s decision to increase production signi…cantly above its OPEC quota. Looking at the top panel Oil-Market-Speci…c Shock - which is the 3-months sum of the interaction between the oil price change and
the intra-month correlation, we clearly see that this is identi…ed as a positive oil-market-speci…c shock which
was followed by its reversal. Looking at the alternative oil shock variables, one can see that the Oil Increase
and the JDH Shock do not capture this event at all while the Lee Shock seems to have picked up this drop,
shifted by a few months.
Moving forward in time, looking at the Oil Price Change series again, notice the pattern of large positive
oil price changes followed by negative ones during the onset of the Gulf war and its relatively swift resolution.
This pattern is the most prominent in the Oil-Market-Speci…c shock series (top panel). The Oil Increase and
JDH Shock capture only the initial positive changes but do not capture the reversal. More precisely, they
argue that the reversal does not contribute to forecastability for good economic reason, as explained in the
literature review. The Lee Shock seems to capture this pattern but in a way that does not make this event
conspicuous when looking at the entire series.
The next oil-market-speci…c event was the drop in the price of oil the began in Nov ’93 and was followed
by a local-recovery. The exogenous event there was Iraq’s acceptance of UN resolution 715, which was
percieved as signaling a hastened resumption of Iraqi oil exports, and was adopted earlier by the rest of the
UN active members in Oct ’91. Only the positive part of this oil price change pattern appears in the Oil
Increase series while it doesn’t appear in the JDH Shock one.
The next visible ‡uctuation in the Oil-Market-Speci…c series is around the 9/11 dates. This perhaps is the
more ambiguous events as it was the only clear episode of a mixture of oil-market-speci…c and economy-wide
shocks. Oil spot and futures market reopenned two-days before the equities markets did on Sep 17th . By the
end of the …rst day of trading, oil was up 4:2% and stocks were down
5:1% relative to their pre-event close.
This signals that market-participants viewed this initially as an oil-market-speci…c event that could be most
realistically described as an increase in uncertainty regarding future supplies, or as de…ned in Table I, as a
precautionary demand shock of type 2. Later on that month, on Sep-24th the price of oil collapsed
and the stock market rebounded 3:8%. The average correlation for Sep was
16:5%
0:64. The interpretation is
that within that month, there occured an adverse oil-market-speci…c event which was then reveresed and
this resulted in the negative correlation. But, the oil price gave back more than what it had gained initially
suggesting that it was a confounding adverse economy-wide shock that lead to the negative oil price change
for the whole month. My methodology nonetheless classi…ed the monthly oil price decline in Sep as a positive
oil-market-speci…c shock. The Oil Increase and JDH Shock register no-variation in the month of Sep 2001.
Next is the oil price increase leading up to Iraq war in ’02-’03. The price of oil increased in the period
preceding the war, when it was clearly communicated by Congress and the UN Security Council, and later
declined with the onset of the war. The Oil Increase and JDH Shock series only capture the small positive
26
returns preceding the war but not the declines associated with the successful deployment of US troops on
Iraqi soil.
The oil price increase during the 2005 Hurricane season and following reversal are discernible in the
Oil-Market-Speci…c series, but the increase is more apparent in the Oil Increase and JDH Shock series, while
again the decrease is non-apparent.
The next oil-market-speci…c event is the large price increase and subsequent reversal during May-Aug of
’08, which seems to have been associated with speculation in the oil futures market. The Oil Increase and
JDH Shock series capture the increase but not subsequent decrease.
The last oil-market-speci…c event is the oil price increase in Feb ’11, in what is known as the Lybian
civial war which resulted in an actual oil production ‡ow disruption. It appears in the Oil Increase series
but not in the JDH shock series.
3.3.2
GDP Growth Rates
GDP data is published quarterly so I sample the 3-months summed oil-market-speci…c and economy-wide
specif ic
shock series, "oil
and "economy
(t;t+2)
(t;t+2)
and "economy
wide
wide
, every three months to create two quarterly series,"oil
specif ic
, that matches the real GDP growth data.
In predicting real GDP growth rates I use the same lag structure as Hamilton (1983; 2003), which seems
to be the appropriate lag predictor structure and predicted horizon relevance. I furthermore allow for a
contemporaneous oil price change variable. The contemporaneous relation between oil price changes and
real GDP growth rates comes out of every theoretical model built to study the relation between economic
activity and oil production-‡ow disruptions. Rasch and Tatom (1977) were the …rst to suggests theoretically
that the coe¢ cient should be negative and indeed showed estimates a signi…cantly negative coe¢ cient in their
sample. It is more pertinent to provide an explanation to why lagged oil price shocks should be correlated
with the current period real GDP rate-of-change. One reason is that the storage technology induces a
forward looking element into spot prices, which I label as a precautionary demand shock of type 1 in Table
I. Another reason are unspeci…ed, real-world economic frictions, that lead to time-delays in the e¤ects of
a current-period oil production-‡ow disruption on economic activity. Yet another plausible reason for the
delayed e¤ects are systematic measurement errors in the GDP data aggregation.
Correspondingly, authors like Jones and Kaul (1996), Blanchard and Gali (2007), and Kilian (2009a)
allow for a contemporaneous oil price change as explanatory variable in their estimations. The regression
results for this subsection are therefore not strictly predictive but instead explanatory. Table III below
reports the estimation results in which the main regresson model is the following22 :
2 2 Including oil price shock of lags that are higher than 2 quarters does not add signi…cant predictive power in any of the
regressions in table III.
27
log Y =
+
4
P
i=1
i
log Y
i
+
2
P
os oil specif ic
i "
i
i=0
+
2
P
i=0
ew economy wide
i "
i
+u
[Insert Table III]
[Insert Figure 6]
In all the speci…cations I control for four quarterly lags of real GDP growth rates as in Hamilton
(1983; 2003). In row 2 of Table III, I report results using the separately identi…ed oil-market-speci…c and
economy-wide shocks, "oil
specif ic
and "economy
wide
. Notice that all three coe¢ cients on the oil-market-
speci…c shocks are negative, that the coe¢ cients on the contemporaneous variable, and …rst and second lags
are highly individually signi…cant with t-statistics of
5:53,
coe¢ cients is signi…cantly negative with a t-statistic of
4:12 and
4:88, and that the sum of all …ve
3:87. On the other hand, notice that the coe¢ cients
on the economy-wide shocks is signi…cantly positive with a t-statistic of 2:32. Furthermore, it is interesting
to note that the contemporaneous variable is the most signi…cant out of the economy-wide variables. This
is hardly a surprise since a change in the price of oil that is due to an economy-wide shock in the oil market
is an endogenous consequence of a shock to global economic activity that is captured by real GDP growth
rate. Indeed, the negative shock that hit the economy in 2008:Q3-2008:Q4 led to a large drop in the price
of oil with quarterly oil price changes equalling
real GDP with growth rates equalling
28% and
0:93% and
55:5%, and at the same time to a decline in
2:33% for the two quarters, respectively. In addition,
it seems that the economy-wide shock had signi…cant marginal predictive power on real GDP growth rates
one quarter into the future, as can be inferred from the positive and signi…cant t-statistic on the …rst lag.
GDP Growth Rates - Robustness
If the oil supply shock variable has a positive value in a given
quarter then it means, to a certain extent by construction, that stock market returns were negative during
that quarter. But, it may be that the predicted decline in real GDP growth is due to the decline in stock
prices. On the other hand, if the oil demand return has a negative value in a given quarter it means, since it
is conditioned on a positive correlation between stocks and oil returns, that stock returns were also negative
during that quarter. But, it may be that the predicted decrease in real GDP growth, in this case, was due
again to the decrease in stock prices. Therefore, I control for a contemporaneous variable and four lags of
stock market returns in row 2 of Table III and show that the results are materially unchanged and that the
di¤erence in the signs of the oil supply and demand return coe¢ cients is not a confounding e¤ect of the
interaction of the oil return series with the correlation between oil and stock returns. In particular, I run:
28
log Y =
+
4
P
i=1
i
log Y
i
+
4
P
i=0
os oil specif ic
i "
i
+
4
P
i=0
ew economy wide
i "
i
+
4
P
i=0
iR
sm
i
+"
In row 3 of Table III, I report statistics for using the oil shock variable, "opposite , that takes into account
that oil-market-speci…c and economy-wide shocks have opposite e¤ects on real GDP growth rates. In rows
3-7 I run a horse race between these competing oil shock variables. In particular, row 4 uses Hamilton’s
(2003) net-oil-price-increase variable, row 5 uses the oil price change series as per Hamilton (1983), row 6
uses oil increases alone as a proxy for supply shocks - which according to Mork (1989) are the only ones
relevant to predicting real GDP growth. Row 7 uses Lee’s (1995) normalized oil return series. Notice that
the R2 is highest for the Oil Shock variable suggested in this paper and that the contemporaneous variable
and …rst two lags are signi…cantly positive, as expected by the identi…cation summarized in Table I. This
implies that adverse oil demand shocks are associated with lower real GDP growth rates, as are adverse oil
supply shocks, only that the latter are associated with an increase in the price of oil while the former with a
decrease. Notice in row 4 - JDH Shock speci…cation - that the …rst lag is signi…cantly negative suggesting a
negative relation between real GDP growth rates and adverse supply shocks. This is hardly a surprise given
the ‡exibility in choosing the right transformation to the oil price series. The JDH Shock speci…cation fails
to …t the data well during the ’08-’09 recession as this was associated with a demand shock. Notice in row
5 - Oil Return - that none of the oil variable are signi…cant and that the contemporaneous variable is of the
wrong sign - positive. This should be interpreted as being driven by the demand shocks in the time series
and to the failure of this series to distinguish between supply and demand shocks. The Oil Increase and Lee
Shock results in rows 6 and 7 are insigni…cant as well.
3.3.3
Dividend Growth
To avoid seasonal ‡uctuations in dividend growth I sum 12 months of dividends and calculate a year-overyear dividend growth rate series that is rolling monthly. I run the same predictive regressions for dividend
growth rate,
sm
. only now I include in the estimation. The results are very strong if six lags of oil
D(t+1;t+12)
shocks are included. That more lags are needed here is not a surprise given the sluggishness of dividends in
adjusting to economic conditions, which is most likely a result of managerial signaling considerations [Add
Reference]. Results are presented in Table IV below in which the main results is of the following predictive
regression model:
sm
D(t+1;t+12)
=
+:::
opposite
1 "(t 2;t)
+
opposite
2 "(t 5;t 3)
+
opposite
3 "(t 8;t 6)
+
+ Xt + ::::::
opposite
4 "(t 11;t 9)
+:::u(t+1;t+12 )
29
+
opposite
5 "(t 14;t 12)
+
opposite
6 "(t 17;t 15)
+ :::
[Insert Table IV]
[Insert Figure 7]
Row 1 of Table IV uses 6 lags of 3-months oil shocks to predict the following 12-months of year-over-year
dividend growth. In particular, I run:
The 2nd-6th lags of the oil shock are signi…cantly positive with an R2 of 23:7% which implies that dividend
growth is predicted to be lower if an adverse supply shock occurs that raises the price of oil. It also implies
that dividend growth is predicted to be higher if a positive demand shock occurs that raises the price of oil.
Row 2 of Table IV shows that the results remain unchanged when controlling for dp, term and def and 6
lags of stock market returns. Row 3 shows that the results are indeed driven by both supply and demand
e¤ect - in opposite ways - when I separate them into two di¤erent series of changes in the price of oil that are
due to either supply or demand shocks, by the correlation with the stock market. Rows 4-7 run a horse-race
using the alternative speci…cations discussed in the GDP predictability subsection and show similar results.
Namely, JDH Shocks are signi…cant but fail to account for predictability due to demand shocks, the Oil
Return shock lags are insigni…cant and of mostly the wrong sign. Again, this is because this de…nition fails
to distinguish between oil supply and demand shocks and it seems the demand shock series dominates. Oil
Increases and Lee Shocks are insigni…cant. The R2 in the speci…cation suggested in this paper to predict
dividend growth, presented in row 1, is the highest of all the comparable alternatives suggested in rows 4-7.
3.4
Industries’Cross-Section
Di¤erent industries use oil as input to di¤ering degrees. Furthermore, demand for the industries’goods and
services are a¤ected to di¤erent extents by changes in consumer discretionary due oil-market-speci…c shocks.
Evidence of the latter e¤ect is documented in Edelstein and Kilian (2009). On the other hand, industries’
returns covary di¤erently with oil price changes that are due to economy-wide shocks. I test whether there is
evidence for time-variation in the cross-section of industries’risk premium in response to oil-market-speci…c
and economy-wide shocks.
Let i = 1; :::; 49 be the Fama and French (1997) 49 industries. For each industry i, and each month t, I
calculate
i
t
= corrt (
log Pdo ; Rdi ) using intra-month daily data for that month and "i;opposite
=
(t;t+2)
i
t
log Pto .
The results are presented in table V, of which the main result is of the following combined time-series
and cross-section regression model:
30
i
RX(t+1;t+6)
=
::: +
::: +
+
i
1 M Ct
i;opposite
1 "(t 2;t)
i
1 R(t 2;t)
+
+
+
i
2 B=Mt
+ :::
i;opposite
2 "(t 5;t 3) :::
i
2 R(t 5;t 3)
+ :::
+ui(t+1;t+6)
Where M C i and B=M i are the natural logarithm of the average …rm market capitalization, and book-tomarket ratio. RX i and Ri are industry i excess return over the risk-free rate and plain return, respectively.
then calculate t-statistics based on the time series of the coe¢ cients and report them below. The methodology
employed is to run the cross-section regression for each month t. I then estimate the covariance matrix of the
coe¢ cients from the time-series using a multivariate version of the Hansen and Hodrick (1980) and Newey
and West (1987) methodologies, in order to account for the serial correlation along the time dimension.
Notice the signi…cance of the …rst oil shock lag (t-statistic equals
3:41). This suggests that the oil shock
variable seems to capture changing expected returns in the cross section, corroborating the market-wide,
time-series results discussed earlier.
[Insert Table V]
3.5
Spot Oil Price Change
If oil was prohibitively expensive to store, then spot oil prices would be independent between periods and
as there would be no way for oil supply or demand to adjust to shocks in periods other than the current.
Furthermore in this case, a change in the price of spot oil due to a one-time oil-market-speci…c or economywide shock in the current-period, would be expected to completely revert to the long term mean in the
following period, resulting in negative serial-correaltion in oil price % changes. In empirical reality and in
my theory model, oil is storable and shocks are persistent. But, since oil is not entirely free to move between
time-periods, and since shocks are not permanent and are expected to die out, we have that a given change
in the price of oil is expected to be followed by a change in the opposite direction, independent on whether
it originated from an oil-market-speci…c or an economy-wide shock.
Furthermore, in the case of a production-‡ow disruption, the oil-sector makes up for some of the shortfall
by taking out oil from the stock. As the stock temporarily declines, the marginal convenience yield rises,
which leads to a further ampli…cation of the expected decline in the price of oil.
31
In addition, and again consistent with Acharya et al (2013), when an adverse economy-wide it is the case
that the oil-sector’s risk of default rises. Due to job relocation costs of senior executives in case of default,
this is associated with increased incentives in the oil-sector to hedge their future production. The limited
ability of speculators to take the other side causes leads to depressed oil future prices which makes hedging
more expensive. Consequently, producers scale back on the amount of inventory they carry forward. As this
inventory hits the spot market, it depresses current spot prices, but increases future expected spot prices.
o
log P(t+1;t+6)
=
::: +
same
1 "(t 2;t)
+
same
2 "(t 5;t 3)
+
(6)
same
3 "(t 8;t 6)
+
log Pto
log Ft
+
same
4 "(t 11;t 9)
+ :::
u(t+1;t+6)
[Insert Figure 8]
3.6
Premium in Oil Futures
When an adverse oil production ‡ow shock occurs, the oil sector responds to the shortfall by reducing both
the amount it supplies the market and the amount it stores. The decrease in the level of oil storage leads
to an increase in the marginal convenience yield,
0
(S), which makes the supply of oil more sensitive to
further oil-market-speci…c shocks, as taking out oil from storage to meet further production shortfalls has
now become more expensive. This means that the conditional covariance of spot and future oil price changes,
with the returns on the aggregate wealth, has become more negative as a result of the adverse oil production
‡ow shock. The aggregate invester thus demands less compensation, in terms of a risk premium, for being
long oil futures because the return on this part of his portfolio is expected to covary more negatively, and
thus serve as a better hedge, relative to his aggregate holdings.
Ceteris paribus, when an adverse precautionary demand shock of type II occurs - an increase in uncertainty regarding oil production ‡ows - we have the same decrease in the risk premium in long oil futures. The
intuition here is even clearer: oil production ‡ow shocks incudce a negative covariation between returns in
oil futures and returns on the aggregate wealth portfolio. As the volatility of this shock process increases, so
does the covariation, and commensurately the required risk-premium decreases with the increased hedging
property of this …nancial instrument. In general equilibrium, we also have the result that the oil sector
increases its oil-inventory holdings as the precautionary savings motive increased. This serves to diminish
the partial equilibrium e¤ect but does not completely reverese it.
32
In the speculation case, the intuition for the one-to-one decline in the long-side oil futures premium is
clear-cut. The entire economy is inexplicably caught by what could be described as a behavioural abberation.
The i.i.d speculation shock hits the oil futures market and leads to an increase in the oil futures price. At the
same time, the oil sector wrongly percieves this as being due to an expected increase in the price of oil and
passes the increase in its entirety onto to the spot price. Oil consumers are also captured, and believe that
oil has commensurately become more productive. The result in the oil market is that supply and demand
are a¤ected such that the quantity of oil does not change while the spot price increases by the value of the
speculation shock, and so does the future price. Since actual oil consumption does not change, no other
quantity in the economy changes either. The aggregate risk premium (of the goods-producing …rm’s equity)
remains at its long term mean, and so does the risk premium of the oil-sector’s equity. This shock facilitates
a transfer between the goods-producing …rm and the oil sector which, through the increase in the price of
spot oil, and results in the negative current-period return on the equity of the stock market. The rational
expectation in the model is that the value of the speculation shock would be zero in the next period and
commensurately the price of spot oil would completely revert to it’s long-term mean, leaving long oil futures
investors with a loss.
I …nd that when an adverse economy-wide shock occurs the risk-premium in long-futures rises. This is
consistent with Acharya et al (2013) which have built a model that incorporates two main features: (1)
net-short hedging pressure by oil producers; and (2) capital constraints of the net-long speculators on the
other side. They show that when the speculators’equity is hit by an adverse shock, their ability to mitigate
the price e¤ect of the produers’hedging pressure is diminished. This leads to a bigger downside price impact
in oil futures and to an increase in the risk premium to long futures contracts.
o
log Pt+6
::: +
same
1 "(t 2;t)
+
(6)
log Ft
=
same
2 "(t 5;t 3)
+
+
(6)
log Ft
same
3 "(t 8;t 6)
+
log Pto
same
4 "(t 11;t 9)
+ :::
u(t+1;t+6)
[Insert Figure 9]
4
DSGE Model
In this section I take a model of the economy, which builds on the Real Business Cycle (RBC) literature,
and which has been shown by Uhlig (2007) and others, has being able to match key macroeconomic and
33
asset pricing moments. To this representative agent model I add a representative oil-sector with storage
technology. I explicitly model the four oil-market-speci…c shocks and one economy-wide shock and I show
that they produce the impluse responses summarized in Table I and which underlie the empirical results in
this paper.
4.1
Household
The representative consumer-investor maximizes his life-time utility, Vt , from consumption, de…ned recursively following Kreps and Porteous (1979) and Epstein and Zin (1989):
h
(1) Vt = (1
1
) [Ct
(2) Ht = Ht
1
+ <1t
Ht ]
+ (1
)hCt
1
i11
where
h
i
1
<t = Et Vt+1
1
1
Here, Ct is consumption and Ht is the external habit level. Notice that I use a per-period utility
function that has an external habit which follows Abel (1990). The external habit delivers a highly locally
concave utility function needed in order to match the sizable unconditional aggregate risk premium and easily
introduces countercyclicality in its conditional mean. The recursive preference structure is useful because it
allows disentangling risk aversion from inter-temporal elasticity of substitution. A value that is above unity
is needed for the latter parameter in order to subdue the volatility of the risk free rate to level observed
empirically. It is also needed in order to keep the risk free rate from being counterfactually countercyclical.
The asset-pricing kernel of this consumer is
(3) Mt =
Ct
Ct Ht
1 Ht
Vt
<t 1
1
The budget constraint of the consumer equates his consumption expenditure to his dividends income,
Dtsm and Dtos , from owning the good-producing …rm and the oil-sector, plus his labor income, Wt Lt .
(4) Ct = Dtsm + Dtos + Wt Lt
Lastly, the consumer supplies all of her available labor, normalized to Lt = 1 in each period, since labor
does not enter her utility.
4.2
Good-Producing Firm
The representative consumption-good producing …rm takes capital, labor and oil, (Kt
1,
Lt and Ot ) as inputs
to a Cobb-Douglas production function with output Yt , and which is subject to a productivity process Zttf p .
k
l
1
(5) Yt = (1 + Zttf p )Kt! 1 L!
t Ot
!k !l
34
The productivity shock process evolves according to the AR(1) process
(6) Zttf p =
tf p
Zttf p1 +
tf p tf p
et
p
etf
t
where
i:i:d
N (0; 1)
Capital is predetermined in equation (6) and I therefore use the convention that it enters with a lag.
Capital further evolves according to the following accumulation equation
(7) Kt = (1
)Kt
1
+ ( KItt 1 )Kt
1
where It is investment, which is subject to a convex adjustment cost, or more preciesly, a concave
investment implementation function, following Jermann (1998)
(8)
( KItt 1 ) =
(9)
0
1=
( KItt 1 ) =
1 1=
It
Kt
1 1=
1=
It
Kt
(1= )
1 1=
1
1=
1
Due to the highly locally concave utility function, the representative consumer preferes very smooth
consumption paths. The convex adjustment cost is needed to counter his desire to smooth by changing
investment too abruptly, which results in counterfactual countercyclical dividends. The per-period goodproducing …rm’s dividend is de…ned as
(10) Dtsm
Yt
Pto Ot
Wt Lt
It
Dtsm is the residual income left of production revenue, less wages, Wt , paid to labor employed, less oil
expenditure - the product of the price of oil, Pto , and the quantity purchased, Ot - less the capital-investment
outlays. The superscript sm stands for "stock-market" (excl. IDOCs as explained).
The representative good-producing …rm maximizes its net present value, adjusted for marginal utilitly
this cash‡ow contrbutes to the representatvie consumer, who owns it. The …rst order optimality conditions
for [Kt ], [Lt ], and [Ot ] are
(11) 1 = Et Mt+1
0
It
Kt
1
t+1
! k YK
+
t
Yt
(12) Wtf = ! l L
t
(13) Pto = (1
(1
)+ (
It+1
Kt
I
0 ( t+1
Kt
)
)
It+1
Kt
[Kt ]
[Lt ]
!k
! l )Yt =Ot = (1
!k
! l )(1 + Zttf p )Kt!
k
1
1
l
1
L!
t Ot
!k !l
[Ot ]
Equation (11) is the Euler equation which trades o¤ marginal productivity of capital in the current-period
and in the next, taking into account the convex adjustment cost.
Equation (12) speci…es that the friction-less real-wage, Wf , equal the marginal productivity of labor. I
introduce a real-wage rigidity following Blanchard and Gali (2005) of the following form
(14) Wt = (Wt
!
1)
Wtf
1 !
35
where the actual real-wage paid is Wt and is a geometrically weighted average between the friction-less
wage in the current-period, and the actual wage in the previous-period. This is again needed in order to
counter the abrupt adjustment of the wage in general equilibrium, due to the high local concavity of the
utility function, which leads to counterfactual countercyclicality of dividends.
Equation (13) equates the real-price of oil to its marginal productivity and de…nes oil demand by the
oil-consuming sector of this economy. It also clearly shows how the economy-wide productivity shock a¤ects
the demand for oil.
The price, Ptsm , of a claim to the good-producing …rm’s dividends is caluculated recursively
sm
sm
(15) Ptsm = Et Mt+1 Pt+1
+ Dt+1
where and the total return to investors is de…ned as
Ptsm +Dtsm
Ptsm1
(16) Rtsm =
The return, Rtsm , in equation (16) is the counterpart the return on the stock market index, excluding
IDOCs, used in the empirical part of the paper.
4.3
Oil-Sector
The representative oil-sector in this economy receives an oil endowment in each period,
o,
which is subject
to a shock process Zto . Given the realization of the shock in the current-period, the oil-sector chooses the
quantity of oil to sell in the market, Ot , while taking the price of oil Pto as given23 . Its oil-inventory, St ,
therefore evolves according to
(17) St = (1
where
o
o )St 1
+
o (1
+ Zto )
Ot
is the direct (…xed) oil storage cost. This stands for physical storage costs, as well as insurance
and administrative costs which I assume are proportional to the quantity of oil carried over as inventory.
The shock process Zto evolves according to following AR(1) model:
(18) Zto =
o
Zto
1
+
o o
t et
and
eot
i:i:d
N (0; 1)
with a further AR(1) process for the stochastic volatility of the oil production-‡ow shock
2 3 A model of the oil-sector that is more consistent with price-taking behaviour begins with a continuum of small oil …rms
and whereby Ot and St are their aggregated quantities. The oil-sector in this economy is an approximation, and simpli…cation
of the aggregated equations from such a model.
36
(19)
o
t
=
o
SS (1
o
)+
o
o
t 1
o
+
et
o
and
et
o
i:i:d
N (0; 1)
This …rm too, maximizes the net present value of its cash-‡ows, adjusted for their marginal contribution
to the representative consumer-investor who owns it. The oil-sector’s per-period dividend,Dtos , where the
superscript os stands for "oil-sector", are the following
(20) Dtos = Pto (Ot
(St ))
The oil-sector’s per-period dividend is comprised of to parts: its (1) revenues from oil sales, Pto Ot ; less
its (2) inconvenience cost from operating with low levels of inventory,
(St )24 .
The inconvenience cost is the direct source of the convenience yield discussed in the literature25 . The
oil sector cannot operate with zero inventory as production process are halted, and as inventories abound it
operates smoothly. I therefore model
St goes to in…nity. Furthermore,
0
(St ) as going to in…nity as St goes to zero, and as going to zero when
(St ) is negative and
00
(St ) is positive, meaning that the inconvenience
cost is ameliorated as we increase inventory, but at a decreasing rate. The inconvenience cost is necessary for
empirical and theoretical reasons, and it is also a handy way to prevent oil-storage levels from ever hitting
zero or going into negative territory. For the numerical simulation I choose a simple functional form:
(21)
(22)
(St ) =
0
1
(St ) =
with
St
St
>0
( +1)
[Insert Figure 10]
The …rst order optimality condition of the oil-sector with respect to [Ot ], or equivalently with respect to
[St ] is
(23) Pto (1 +
0
(St )) = (1
o )Et
o
Mt+1 Pt+1
[St ]
This is the familiar Euler equation that has been modeled directly by French (1985) and Fama and
French (1987). On the left hand side we have, the current-period spot price of oil, which is the revenue from
selling a margnial barrel of oil at the market. Also on the left hand side is the marginal inconvenience cost,
0
(St ), incurred when selling that marginal barrel of oil instead of carrying it as inventory. The convenience
yield referred to in the literature is
0
(St ), and has all the properties discussed. On the right hand side
2 4 (S ) is in denominated in oil barrels and enters the current-period dividend as P o (S ) for arithmetic tractability in
t
t
t
deriving the closed-form, non-stochastic, steady-state values.
2 5 See
literature review subsection titled "Theory of Storage".
37
is the revenue from carrying over one marginal barrel of oil as inventory, which is its expected spot price
of oil in the following period, discounted to today with the appropriate rate-of-return of the representative
consumer-investor, less the direct cost of storage.
I derive the price, Ptos , and returns, Rtos ,on the equity of the oil-sector although these are not part of
the empirics in this paper. The real-world counterpart to these are the unobserved price and returns on the
equity of a Middle-Eastern NOC, which are privately held.
os
os
(24) Ptos = Et Mt+1 Pt+1
+ Dt+1
Ptos +Dtos
Ptos 1
(25) Rtos =
4.4
Rates, Premiums and Futures
The one-period risk-free rate, Rtf , the risk premium embedded in the good-producing equity, RPtsm , and in
the equity of the oil-sector, RPtos , are de…ned as
(26) Rtf = (Et [Mt+1 ])
sm
Et [Rt+1
]
(27) RPtsm
os
Et [Rt+1
]
(28) RPtos
1
Rtf
Rtf
The price of a fully collateralized investment in an oil forward contract which matures in n periods, and
o
. But, an investment in oil futures does not require
delivers one barrel of oil, is Et Mt+1 ::: Mt+n Pt+n
(n)
any initial outlay of resources. Therefore, oil forward prices with n periods to maturity, Ft
(n)
(29) Ft
=
, satisfy:
o
Et [Mt+1 ::: Mt+n Pt+n
]
Et [Mt+1 ::: Mt+n ]
The excess return of a fully collateralized long investment in oil forwards, over the risk-free rate, is
(30) RPtF
(n)
h
i
(n)
o
Et log Pt+n
=Ft
Lastly, the expected continuosly-compounded rate-of-change of the spot price of oil is
(31) Et
4.5
o
o
log Pt+1
= Et log Pt+1
=Pto
Shock Discussion
The shocks in this DSGE model correspond to the oil-market-speci…c and economy-wide shocks presented
in Table I and discussed throughout the empirical part of this paper. Speci…cally, eot , in equation (18)
is the traditional supply shock, or more precisely the oil production-‡ow shock. In order to model the
38
precautionary demand shock of type I - the news shock about future oil production ‡ow disruptions - I
simply change equation (17) such that the shock process Zto enters with a lag, Zto 1 . The precautionary
demand shock of type 2 - an increase in uncertainty about future oil production ‡ows - is modeled explicitly
o
p
as et in equation (19). The economy wide shock is etf
t .
4.5.1
Speculation Shock
I now turn to explaining my approach towards modeling the e¤ects of behavioral speculation. I model the
consequences of this unmodeled, but very intuitive, herd-like behavior as an i.i.d shock process, Ztspec
(32) Ztspec =
spec spec
et
and
espec
t
i:i:d
N (0; 1)
The unpredictable shock a¤ects 3 equations simultaneously in the following way:
(n)
(29*) Ft
=
o
Et [Mt+1 ::: Mt+n Pt+n
]
(1
Et [Mt+1 ::: Mt+n ]
(23*) Pto (1 +
0
(13*) Pto = (1
!k
(St )) = (1
o )Et
+ Ztspec )
o
Mt+1 Pt+1
(1 + Ztspec )
! l )Yt =Ot (1 + Ztspec )
[St ]
[Ot ]
The price of oil futures, rederived in equation (29*), unexplcably increase by the value of the shock.
This models irrational long-side future-market participants who bid prices above what is justi…ed by the
current state-variables in the economy and by the distribution of future shocks. They are going to su¤er an
expected loss, that does not correspond to an ex-ante rational hedging consideration, due to their over-zealous
purchases.
At the same time, the oil-sector is captured by this misconception and thinks that future oil prices are
going to be commensurately higher, and therefore passes the increase to spot prices by increasing the amount
stored and decreasing the amount supplied for sale.
The good-producing …rm does not maintain its composure in light of these and are due led to think that
the productive value of a barrel of oil today is higher than warranted. They therefore increase their demand
for crude-oil.
While prices of spot and future oil mechanically rise by the size of the speculation shock, the last two
e¤ects have exactly o¤setting e¤ects on the quantities of oil supplied and demanded for production.
[Insert Figure 11]
39
4.6
Calibration
4.7
Impulse Responses
I calibrate the model using the standard parameter values employed in the literature which, in this model
as well, generate similar macroeconomic and asset-pricing moments. I solve the model using a numerical
perturbation method, which employs a third-order taylor expansion around the deterministic steady state.
I now recreate the impulse responses in Table 1 which are the basis for my empirical methodology and
results. The shocks are normalized to a value of one standard-deviation for each shock and are plotted in
%-deviation from steady-state values or in basis-point absolute deviation. The key things to notice are that
for each adverse oil-market-speci…c shock, we have on-impact: (1) an increase in the spot price of oil; (2) a
negative return in the stock-market; (3) a rise in the risk premium in the stock-market; (4) a decline in the
stock-market’s dividends; (5) a decline in production of the consumption-good; (6) a decline in the expected
future change in the price of oil; and (7) a decline in the risk-premium awarded to long investors in oil futures.
On the other hand, for an adverse economy-wide shock we have on-impact: (1) a decrease in the spot price
of oil; (2) a negative return in the stock-market; (3) a rise in the risk premium in the stock-market; (4) a
decline in the stock-market’s dividends; (5) a decline in production of the consumption-good; (6) an increase
in the expected future change in the price of oil; and (7) an increase in the risk-premium awarded to long
investors in oil futures. Notice that a given change in the price of oil is associated with opposite-sign impluse
responses on the macroeconomic variables Rtsm ; Dtsm ; RPtsm ; and Yt depending on whether it originated from
an oil-market-speci…c shock or from an economy-wide shock.On the other hand, a given change in the price of
oil is associated with same-sign impluse responses on the inancial variables in the oil market Et
and RPtF
(1)
o
log Pt+1
independent on whether it originated from an oil-market-speci…c shock or from an economy-wide
shock.
4.7.1
Supply Shock (Oil Production Flow)
In the model, the negative wealth e¤ect of the oil supply shock leads to a decrease in consumption which
brings it closer to the external habit level. The decreased surplus of consumption over the habit, which scales
with the (local) risk-averseness of the representative consumer, leads to a rise in the aggregate risk-premium.
[Insert Figure 12]
4.7.2
Precautionary Demand Shock 1 (News about Future Production Flow)
[Insert Figure 13]
40
4.7.3
Precautionary Demand Shock 2 (Uncertainty of Production Flow)
[Insert Figure 14]
Pindyck (2001) has modeled the demand for oil storage as a direct function of oil price volatility. I build
a model of the oil-sector with more primitive building blocks, which delivers endogenously on this e¤ect.
4.7.4
Speculation
[Insert Figure 15]
4.7.5
Economy-wide
[Insert Figure 16]
5
Conclusions
The results suggest that indeed not all oil price changes have the same e¤ects on the aggregate stock market’s
expected future excess returns and on the growth rates of real GDP and aggregate dividends. The paper’s
main contribution is to shed light on the di¤erent macroeconomic e¤ects of changes in the price of oil
depending on whether they originated from oil-market-speci…c or economy-wide shocks. In particular, and
according to the literature’s concurrent view, rises in the price of oil that are due to oil-market-speci…c
shocks have a negative e¤ect on real GDP growth. In this paper I add evidence to show that adverse
oil-market-speci…c shocks are also associated with an increase in the aggregate risk premium, a result that
the literature was unable to show convincingly. On the other hand, I identify economy-wide shocks and
show that they bring reinforecing evidence to the hypothesis the aggregate risk-premium is countercyclical.
Adverse economy-wide shocks predict a signi…cant rise in future excess returns and lower dividend growth in
the following 12-months. They are also associated with lower real GDP growth rates. Another contribution
is in the methodology employed for identi…cation. I show that …nancial data in daily-frequency contains
signi…cant information about macroeconomic variables of interest, and could be easily extracted using a
simple methodology. I show that …nancial data can be used to extract important information about the
macroeconomy, which reverses the usual roles employed in …nance research, which is to use macroeconomic
data to extract information about …nancial variables.
41
6
Future Additions
It seems like, in order to match the magnitude of the change in the risk premium in oil futures, and in
particular their countercyclical response to economy-wide shocks, I need to introduce a …nancial friction
which make hedging oil production in futures markets more expensive when an adverse economy-wide shock
occurs.
Model capacity constraints in the oil-market as a storage cost,
o (St ),
that is an increasing function of
oil inventory St . This will model the highre marginal storage costs of oil stored in shipping tankers at sea, as
opposed to in permanent facilities onshore. In addition, incorporating convex oil quantity, Ot , adjustment
costs also seems realistic. Studying the implications these mechanisms have on the risk-premium in oil
futures might be interesting.
It might be insightful to replace the aggregate good-producing …rm with a continuum of heterogenous
…rms which have production functions that are oil-intensive to di¤ering degrees.
It might be insightful to make simplifying assumptions, such as the ones in the log-linear model in Hansen,
Heaton and Li (2008), which will enable derivation of semi-parametric closed-form solutions to my theory
model. This may help shed more light on the mechanism which cause changes in the macrovariables of
interest, and in particular about the risk-premium in equities and in oil futures.
More empirical moments that are related to the oil-market need to be matched by the model. A di¢ cult
one is to recreate the predictability results of the empirical section, using data simulated by the theory
model.
42
7
References [INCOMPLETE]
Barsky, Robert B., and Lutz Kilian. "Oil and the Macroeconomy since the 1970s." The Journal of Economic
Perspectives 18.4 (2004): 115-134.
Bernanke, B.S., 1983. Irreversibility, uncertainty, and cyclical investment. Quarterly Journal of Economics 98, 85–106.
Blanchard, Olivier J., and Jordi Galí. The Macroeconomic E¤ects of Oil Shocks: Why are the 2000s so
di¤erent from the 1970s?. No. w13368. National Bureau of Economic Research, 2007.
Büyükşahin, B., Robe, M., 2011. Does "Paper Oil" Matter? Energy Markets’ Financialization and
Equity-Commodity Co-Movements.
Campbell J.Y., Cochrane J.H., 1999. By force of habit: A consumption-based explanation of aggregate
stock market behavior. Journal of Political Economy 107, 205-251
Campbell J.Y., Shiller R.J., The Dividend-Price Ratio and Expectations of Future Dividends and Discount Factors. The Review of Financial Studies , Vol. 1, No. 3 (Autumn, 1988), pp. 195-228
Chen, N., Roll R., Stephen R. 1986. Economic forces and stock market. In: The Journal of Business,
Vol. 59, No. 3 (Jul., 1986), pp. 383-40.
Davis, S.J. 1987. Allocative disturbances and speci…c capital in real business cycle theories. The American
Economic Review, Vol. 77, No. 2, Papers and Proceedings of the Ninety-Ninth Annual Meeting of the
American Economic Association (May, 1987), pp. 326-332.
Davis, S.J., Haltiwanger J. 2001. Sectoral job creation and destruction responses to oil price changes.
Journal of Monetary Economics 48 (2001) 465-512.
Hamilton, J.D. 1983. "Oil and the macroeconomy since world war II". Journal of Political Economy 91,
pp.228-248.
— — — — — — . 1988. "A neoclassical model of unemployment and the business cycle". Journal of Political
Economy 96, pp.593-617.
— — — — — — . 1989. "A new approach to the economic analysis of nonstationary time series and the
business cycle". Econometrica, Vol.57 No. 2 (Mar., 1989), pp. 357-384.
— — — — — — . 1994. Time Series Analysis. Princeton, NJ: Princeton University Press.
— — — — — — . 1996. "This Is What Happened to the Oil Price/Macroeconomy Relation," Journal of
Monetary Economics, , Oct. 1996.
— — — — — — . 2003. "What is an oil shock?". Journal of Econometrics 113(2), pp.363-398.
— — — — — — . 2005. "Oil and the Macroeconomy". New Palgrave Dictionary of Economics, 2nd edition,
edited by Steven Durlauf and Lawrence Blume, Palgrave McMillan Ltd., 2008.
— — — — — — . 2009a. "Understanding crude oil prices". Energy Journal, forthcoming.
43
— — — — — — . 2009b. Causes and Consequences of the Oil Shock of 2007-08, Brookings Papers on
Economic Activity, Spring 2009: 215-259.
Fama, E.F., French, R.K., Business conditions and expected returns on stocks and bonds, Journal of
Financial Economics, Volume 25, Issue 1, November 1989, Pages 23-49, ISSN 0304-405X, 10.1016/0304405X(89)90095-0.
Fama, E.F., 1990. "Stock Returns, Expected Returns, and Real Activity". Journal of Finance, Vol. 45,
No. 4 (Sep., 1990) pp. 1089-1108.
Fama, E.F., French, R.K., 1997. Industry costs of equity. Journal of Financial Economics Volume 43,
Issue 2, February 1997, Pages 153–193
Ferson, W. E., Sarkissian, S. and Simin, T. T. (2003), Spurious Regressions in Financial Economics?.
The Journal of Finance, 58: 1393–1414.
Finn, M.G., 2000. Perfect competition and the e¤ects of energy price increases on economic activity.
Journal of Money, Credit, and Banking 32, 400–416.
Gri¢ n J.M., Neilson, W.S., 1994 The 1985-86 Oil Price Collapse and Afterwards: What does Game
Theory Add?
Granger, C.W.J, 1969, "Investigating Causal Relations by Econometric Models and Cross-spectral Methods"., Econometrica , Vol. 37, No. 3 (Aug., 1969), pp. 424-438.
Fisher, I., The Debt-De‡ation Theory of Great Depressions. Econometrica , Vol. 1, No. 4 (Oct., 1933),
pp. 337-357
Forbes, K. J. and Rigobon, R. (2002), No Contagion, Only Interdependence: Measuring Stock Market
Comovements. The Journal of Finance, 57: 2223–2261.
Hooker, M.A., 1996. What happened to the oil price-macroeconomy relationship? Journal of Monetary
Economics 38 (1996) 195-213.
Hansen, L.P., Hodrick, R.J., 1980. Forward Exchange Rates as Optimal Predictors of Future Spot Rates:
An Econometric Analysis. Journal of Political Economy Vol. 88, No. 5 (Oct., 1980), pp. 829-853
Huang R.D., Masulis R.W., Stoll H.R., 1996. Energy Shocks and Financial Markets. In: The Journal of
Futures Markets, Vol. 16, No. 1, 1-27 (1996).
Jones C.M., Kaul G. 1996. Oil and the Stock Markets. In: The Journal of Finance, Vol. 51, No. 2 (Jun.,
1996), pp. 463-491.
Edelstein, Paul, and Lutz Kilian. "How sensitive are consumer expenditures to retail energy prices?."
Journal of Monetary Economics 56, no. 6 (2009): 766-779.
Kilian L. 2009a. Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the
Crude Oil Market, American Economic Review, 99(3), June 2009, 1053-1069.
— — — -. 2008. Exogenous Oil Supply Shocks: How Big Are They and How Much Do They Matter for
44
the U.S. Economy? Review of Economics and Statistics, 90(2), 216-240, May 2008.
— — — -. 2009b. The Impact of Oil Price Shocks on the U.S. Stock Market (with Cheolbeom Park),
International Economic Review, 50(4), November 2009, 1267-1287.
Kilian, Lutz, and Robert Vigfusson. "Pitfalls in estimating asymmetric e¤ects of energy price shocks."
FRB International Finance Discussion Paper 970 (2009).
Kilian L., Alquist R. 2010 What Do We Learn from the Price of Crude Oil Futures?, Journal of Applied
Econometrics, 25(4), June 2010, 539-573.
Kilian L., Barsky R.B. 2004 Oil and the Macroeconomy since the 1970s, Journal of Economic Perspectives,
18(4), Fall 2004, 115-134.
— — — — — — — — — . 2002 Do We Really Know that Oil Caused the Great Stag‡ation? A Monetary
Alternative, in B. Bernanke and K. Rogo¤ (eds.), NBER Macroeconomics Annual 2001, May 2002, 137-183.
Kim, I., Loungani, P., 1992. The role of energy in real business cycle models. Journal of Monetary
Economics 29, 173–189.
Lee, K., Ni, S. and Ratti, R.A. (1995). "Oil Shocks and the Macroeconomy: The Role of Price Variability."
Energy Journal 16, 39-56.
Lee, K., Ni, S., 2002. On the dynamic e¤ects of oil price shocks: a study using industry level data.
Journal of Monetary Economics 49, 823–852.
Lilien, D.M. 1982. Sectoral Shifts and Cyclical Unemployment. The Journal of Political Economy, Vol.
90, No. 4 (aug., 1982), pp.777-793.
Loungani, Prakash (1986). "Oil Price Shocks and the Dispersion Hypothesis." Review of Economics and
Statistics 58, 536-539.
Masters, Michael W. "Testimony of Michael W. Masters, Managing Member/Portfolio Manager, Masters Capital Management, LLC. "Testimony before the US Senate Committee on Homeland Security and
Governmental A¤airs (2008)".
Mork, K. 1989. Oil and the macroeconomy when prices go up and down: an extension of Hamilton’s
results. In: Journal of Political Economy, 91, pp, 740-744.
Newey, Whitney K; West, Kenneth D (1987). "A Simple, Positive Semi-de…nite, Heteroskedasticity and
Autocorrelation Consistent Covariance Matrix". Econometrica 55 (3): 703–708.
Novy-Marx, R., 2012, Pseudo-Predictability in Conditional Asset Pricing Tests: Explaining Anomaly
Performance with Politics, the Weather, Global Warming, Sunspots, and the Stars. NBER Working Paper
No. 18063.
Pierce, J.L., Enzler, J.J., 1974. The e¤ects of external in‡ationary shocks. Brookings Papers on Economic
Activity I, 13–61.
Pindyck, R.S., 1980. Energy price increases and macroeconomic policy. Energy Journal 1, 1–20.
45
Rasche, R.H., Tatom, J.A., 1977. Energy resources and potential GNP. Federal Reserve Bank of St.
Louis Review 59 (June), 10–24.
— — — — — — — — — -, 1981. Energy price shocks, aggregate supply, and monetary policy: the theory and
international evidence. In: Brunner, K., Meltzer, A.H. (Eds.), Supply Shocks, Incentives, and National
Rotemberg, J.J., Woodford, M., 1996. Imperfect competition and the e¤ects of energy price increases.
Journal of Money, Credit, and Banking 28, 549–577. Wealth, Carnegie-Rochester Conference Series on
Public Policy, Vol. 14. North Holland, Amsterdam.
Sadorsky P. 1999. Oil price shocks and stock market activity. In: Energy Economics 21 1999. 449-469.
Silvennoinen, A., Thorp S., 2011, Financialization, crisis and commodity correlation dynamics.
Solow, R.M., 1980. What to do (macroeconomically) when OPEC comes. In: Fischer, S. (Ed.), Rational
Expectations and Economic Policy. University of Chicago Press, Chicago.
46
Appendix I - Oil-Market-Speci…c and Economy-Wide Events - [INCOMPLETE]
The major events that a¤ected the crude oil market were:
1. Suez Canal Crisis - Oct-Nov 1956 - 10.1% drop in world production26 - following the Egyptian nationalization of the Suez canal, Britain, France and Israel launched an attack. Supplies of oil were held
from the market because of the blocked naval passage.
2. Trans-Arabian Pipeline Rupture - May ’70 - A tractor in Syria punctured the pipeline preventing
500,000 barrel of Saudi crude a day from reaching Mediterranean ports.
3. Yom Kippur or Arab-Israeli War - Oct ’73 - 7.8% drop in world production - Coalition of Arab states
lead by Egypt and Syria launch a surprise attack on Israel. Eleven days following the outbreak of this
war, OPEC announced an oil embargo on the west.
4. Iranian Islamic Revolution - Jan ’78 to Feb ’79 - 8.9% drop in world production.
5. Iran-Iraq War - 7.2% drop in world production - Oil supply disruptions were in Sep-Oct ’80 while the
war lasted until Aug ’88.
6. Saudi Arabia Abandons OPEC Quota System - Aug ’85 to Aug ’86 - Saudi Arabia decides to increase
production by about 4.1 million barrels a day due to cheating by other OPEC members. Lastly, notice
in Figure 7, the large negative returns in the oil market in ’86. A good account of what happened
around that time is presented in Gri¢ n and Neilson (1994). In short, Saudi Arabia - the largest member
of the OPEC cartel - decided to increase production in response to persistent cheating by other OPEC
members and this served to reverse some of the major oil price increases during the mid-70’s and early80’s. This drop in the price of oil has been studied in the literature and was considered interesting since
it failed to produce a boom in the economy like the negative relation between oil %-change and real
GDP growth suggested up to that time. The theory and empirical …ndings that were a result of that
event argue that an exogenous oil price decrease due to a positive supply shock, as this one might be
considered, should have o¤setting positive and negative e¤ects on the economy since on the one hand
it directly increases productivity by the fact that a production input has now become cheaper, while
on the other hand, there is a large cost associated with reallocating labor and capital between sectors
that were a¤ected by this relative price change. On the other hand, since there was a clear decision to
increase production it might be contended to be an endogenous response to high demand and therefore
it is unclear that there should be any meaningful e¤ect on the stock market and macroeconomy as
2 6 Drop
in production …gure is from Hamilton (2003)
47
the e¤ects of supply and demand may o¤set each other. If indeed this type of oil price decrease does
not have a material aggregate e¤ect on the economy, as Mork (1989) showed for the …rst time, then
aggregate stock prices should re‡ect that fact and not correlate very well with it. As can be seen in
Figure 8, the pattern of a large negative return become muted once interacted with the correlation.
The identi…cation scheme suggested in this paper is therefore suitable to capture this asymmetry and
lack of e¤ect, and seems to infer that information from the correlation of oil and stock returns.
7. Gulf War - Aug ’90 to Feb ’91 - 8.8% drop in world production - Iraq, lead by Saddam Hussain, invades
Kuwait and as a desperate measure con‡agrated its oil …elds.
8. Asian and Russian Financial Crises - Jul ’97 to Aug ’98.
9. Iraq War - Mar ’03 - In Oct ’02 congress passed a law authorizing military action against Iraq and in
November ’02 the UN Security Council gave Saddam Hussain "a …nal opportunity to comply with its
disarmament obligations". A prominent example is the run up in the price of crude oil in late 2002
- when George W. Bush passed Iraq Resolution in Congress - which preceded the much anticipated
invasion of Iraq by the US armed forces in March 20th, 2003. It was only when US forces executed a
swift military campaign - conquering Baghdad after 19 days - did oil prices go back down.
10. Hurricanes Rita and Katrina - Aug-Sep ’05 - re…ning capacity severely damaged in the Gulf of Mexico
11. Oil breaking all time highs reaching a high of $145 per barrel - May-Aug 2008. That this episode
clearly appears in the oil-market-speci…c shock series is consistent with evidence brought in Hamilton
(2009) and Edelstein and Kilian (2009) that the recession of late 2008 was ampli…ed and preceded by
an economic slowdown in the automobile industry and a deterioration in consumer sentiment.
12. Lehman Bankruptcy - Sep 15th 2008 - In the following months oil fell to a low of $33 per barrel.
13. Libya Civil War - Opposition to Muammar Gadda… lead to oil supply disruptions.
Notice also the recessions in ’48-’49, ’53-’54, ’57-’58, ’69-’70, ’73-’75, ’80, ’81-’82, ’90-’91 and ’07-’09 which
were all preceded by a large increase in the price of crude oil [ADD SHADED RECESSION DATES].
48
Appendix II - Data and Variables - [INCOMPLETE]
Rtsm is value weight NYSE/NASDAQ/AMEX total return for month t from CRSP.
Rtf is the 1 year US government bond risk-free return for month t, from CRSP.
log Pto is the log di¤erence in the closing price of the nearest-to-expiration oil future, at the last day of
trading in month t over the one in month t
o
log P(t;t+2)
=
log Pto +
o
log Pt+1
+
1.
o
log Pt+2
is the 3-months cummulated oil return.
log Y is the quarterly, continuously compounded, seasonally adjusted, rate of change of real GDP for
quarter
obtained from FRED database.
log P o is the log di¤erence in the closing price of the nearest-to-expiration oil future, at the last day of
trading in quarter
over the one in quarter
1.
sm
sm
sm
R(t;t+11)
= (1 + Rtsm ) (1 + Rt+1
) ::: (1 + Rt+11
) is the 12 months, cummulated forward, stock market
return.
f
R(t;t+11)
= (1 + Rtf )
f
(1 + Rt+1
)
:::
f
(1 + Rt+11
) is the 12 months, cummulated forward, 1 year US
government bond risk-free return.
Rsm is the quarterly value weighted NYSE/AMEX/NASDAQ value weighted return including dividends
from CRSP.
f
sm
sm
RX(t;t+11)
= R(t;t+11)
R(t;t+11)
is the cumulated 12-months stock market excess return.
P11
Dt i
i=0
is the 12 months dividend yield. The data is obtained by manipulating the includingdpt =
Pt
and excluding- dividends return data of the value weighted NYSE/NASDAQ/AMEX total market from
CRSP.
deft is the di¤erence in yields between Moody’s seasoned Baa and Aaa Corporate Bonds indexes’yields
obtained from FRED database.
termt is the di¤erence in yields between the 10 years US government bond and the 3 months bill.
D(t;t+11) =
Dt +Dt+1 +:::+Dt+11
Dt 12 +Dt 11 +:::+Dt
1
is a monthly series of the 12-months aggregate US stock market
dividend growth.
49
Pt o
(+)
(–)
(+)
(–)
(–)
(–)
(–)
(–)
Dtsm , Et Dtsm
1 
(–)
(+)
0
(+)
(+)
(+)
RPt sm
Macro-variables
(+)
(–)
0
(–)
(–)
(–)

Yt , Et Yt 1
Result/Hypothesis
Correlation Sign

(–)
(–)
(–)
(–)
(–)*
(–)
Et Pt o1
(–)
(–)
(–)
(–)
(–)
(–)
RPt F
Oil-Market variables
1
* The impulse response to the (adverse) future production-flow disruption (news) shock that is set to occur in the next period is an increase in the price of oil, spread
between the current period and the next. The price is thence expected to decline in the following period, (t  2) .
(Productivity Shock)
Economy-Wide
Pt o
(–)
Speculation Shock
(I.I.D. Wedge)
Oil-Market-Specific
(–)
Precautionary Demand
Shock 2
(Volatility Shock to
Production Flow)
(–)
Precautionary Demand
Shock 1
(News about Future-Period
Production Flow)
Pt o
(–)
Rtsm
Supply Shock
(Current-Period Oil
Production Flow)
Shock
Prior
1
GDP; Et Pt o1 is the expected future change in the spot price of crude oil; RPt F  Et Pt o1  Ft1 is the risk-premium, or the expected excess return from investing (long)
in an oil futures contract of a one-period maturity;
Pt o is the current-period change in the spot price of crude oil; Rtsm is the current-period return in the stock-market (excl. oil-companies); Dtsm is the current-period
stock-market’s dividend growth rate; RPt sm  Et Rtsm1  Rt f is the expected excess stock-market return over the risk-free rate. Yt is the current-period change in real
Impulse responses to Oil-Market-Specific and Economy-Wide shocks.
Table I
-0.09
-0.81
0.07
2.33
-0.05
-0.59
-0.08
-0.76
-0.07
-0.67
b
t(b)
b
t(b)
t(Σb)
b
t(b)
t(Σb)
b
t(b)
b
t(b)
1
2
3
4
5
Cnst.
4.75
1.95
4.52
1.93
4.81
1.74
5.09
1.55
dpt
-4.57
-0.59
-2.85
-0.47
-5.36
-0.92
0.66
0.11
termt
-0.11
-0.73
4.59
2.20
4.53
2.10
2.47
1.15
deft
0.37
1.59
-0.55
-4.01
-0.49
-3.90
-0.40
-3.96
ε (t − 2,t )
-2.94
-0.27
-1.58
-3.09
-0.37
-1.88
-0.40
-1.89
0.73
2.75
0.39
1.36
ε oil−specific
-0.88
-3.61
ε opposite
-0.77
-3.77
ε opposite
-0.66
-4.26
ε (t−8,t−6)
ε opposite
ε (t−5,t−3)
commensurate with the lag structure of the novel predictors.
0.19
1.03
-0.18
-1.04
-0.24
-1.50
-0.19
-1.16
ε (t−11,t−9)
-0.74
-2.08
ε (t − 2,t )
-1.09
-2.24
-0.38
-1.13
ε (t−8,t−6)
ε economy−wide
ε (t−5,t−3)
sm
Dependent Variable: RX (t+1,t+12)
-0.17
-0.49
ε (t−11,t−9)
4.76
2.20
0.16
0.99
R(sm
t − 2,t )
0.17
1.00
0.23
1.26
sm
R(t−5,t−3)
0.24
1.26
0.12
0.55
sm
R(t−8,t−6)
0.11
0.48
-0.08
-0.57
sm
R(t−11,t−9)
18.7%
19.0%
18.3%
7.5%
9.3%
2
Radj
a composite index that incorporates the hypothesis that the effects of the aforementioned are of opposite sign. I control for past stock-market returns, R(smt−2,t ) ,
shocks, respectively, identified by the sign of the correlation between oil price % changes and stock-market (excl. oil-companies) returns. The predictor ε (opposite
t − 2,t ) is
− wide
can be interpreted as innovations in the price of spot oil, during the 3-month period {t-2,t-1,t} that are due to oil-market-specific and economy-wide
ε (economy
t − 2,t )
− specific
The default spread is the end-of-month t difference in yields on portfolios of corporate bonds rated Baa and Aaa by Moody’. The novel predictors ε (oil
and
t − 2,t )
controls dpt , termt , and deft follow definitions in Fama (1990): the dividend yield is the 12-month sum of dividend up to and including month t, divided by the
price at the end-of-month t; the term spread is the end-of-month t difference in yields of the constant maturities 10-year US Treasury-bond and the 3-month bill;
of interest equal zero, and the R 2 , adjusted for degrees of freedom. The excess return, RX (smt+1,t +12) , accrues during the months t+1 up to and including t+12. The
This table show statistics for predictive regressions of the 12-month CRSP-universe stock-market excess return, over the rate offered on a 12-month, constant
maturity, US Treasury-Bill. The sample period is 1983:04-2011:12. I calculate OLS coefficients and t-statistics which are adjusted for heteroscedasticity and serialcorrelation, following Newey and West (1987), with 24 lags entering in the estimation of the variance-covariance matrix of the residual matrix. Very similar results
are obtained using Hansen and Hodrick (1980) standard errors, with 12 lags entering. I also present t-statistics to test the hypothesis that the sum of the predictors
RX (sm
α + δ CONTROLSt + β1ε (opposite
+ β 2ε (opposite
+ β 3ε (opposite
+ β 4ε (opposite
+ u( t +1,t +12)
t +1,t +12) =
t − 2,t )
t − 5,t − 3)
t −8,t − 6)
t −11,t − 9)
Table II
-0.07
-0.71
-0.07
-0.67
-0.05
-0.58
-0.01
-0.07
-0.05
-0.59
b
t(b)
b
t(b)
b
t(b)
b
t(b)
b
t(b)
6
7
8
9
10
Cnst.
4.65
1.65
6.18
1.88
4.54
1.66
4.70
1.67
5.03
1.68
dpt
-4.88
-0.81
-12.89
-1.09
-4.76
-0.83
-2.54
-0.36
-3.28
-0.69
termt
4.45
2.06
4.16
1.82
4.51
2.14
3.91
2.09
4.01
1.84
deft
ε (t−8,t−6)
-0.29
-3.32
-0.18
-2.30
Correlation Dummy
ε (t−5,t−3)
-0.09
-1.35
ε (t−11,t−9)
-0.44
-2.30
-0.24
-1.11
-0.18
-1.05
-0.76
-3.69
-0.36
-1.82
-0.22
-1.27
-0.78
-3.37
-0.49
-3.33
-0.83
-4.14
ε opposite
-0.45
-1.96
-0.36
-1.99
-0.31
-1.96
-0.21
-0.80
Sampled Quarterly
-0.40
-1.95
Remove 87’,90’-91’ & ’08-’09 Events
-0.50
-4.05
DeGARCH(1,1) Daily Series-by-Series
-0.19
-1.29
Sig. (5%) Correlation Dummy
-0.16
-2.56
ε (t − 2,t )
ε (t − 2,t )
ε (t−5,t−3)
ε (t−8,t−6)
sm
Dependent Variable: RX (t+1,t+12)
Table II – Continued (a)
ε (t−11,t−9)
R(sm
t − 2,t )
sm
R(t−5,t−3)
sm
R(t−8,t−6)
sm
R(t−11,t−9)
15.5%
17.1%
17.9%
12.9%
23.7%
2
Radj
-0.04
-0.38
-0.05
-0.44
-0.08
-0.78
-0.07
-0.72
b
t(b)
b
t(b)
b
t(b)
b
t(b)
11
12
13
14
Cnst.
4.63
1.53
5.02
1.73
4.85
1.61
4.52
1.54
dpt
0.08
0.01
0.62
0.10
-5.98
-0.96
-2.87
-0.48
termt
2.49
1.45
2.27
1.27
2.95
1.58
2.60
1.43
deft
-0.01
-0.92
-0.34
-0.68
-0.04
-0.26
-0.15
-1.32
ε (t − 2,t )
ε (t−8,t−6)
-0.14
-1.32
-0.07
-0.47
-0.06
-0.34
-0.02
-0.96
-0.01
-0.78
Lee Shock
-0.22
-0.55
JDH Shock
-0.03
-0.25
Oil Price (%) Increase
-0.16
-1.18
Oil Price (%) Change
ε (t−5,t−3)
-0.01
-0.75
0.20
0.79
-0.10
-0.64
-0.05
-0.55
ε (t−11,t−9)
-0.24
-1.77
ε (t − 2,t )
ε (t−8,t−6)
-0.24
-1.40
-0.19
-1.63
Oil Price (%) Decrease
ε (t−5,t−3)
sm
Dependent Variable: RX (t+1,t+12)
Table II – Continued (b)
0.02
0.14
ε (t−11,t−9)
R(sm
t − 2,t )
sm
R(t−5,t−3)
sm
R(t−8,t−6)
sm
R(t−11,t−9)
9.6%
10.4%
13.7%
13.5%
2
Radj
0.21
2.05
0.27
2.51
0.21
2.13
0.26
2.58
0.22
2.32
0.25
2.74
0.21
2.52
b
t(b)
t(Σb)
b
t(b)
t(Σb)
b
t(b)
t(Σb)
b
t(b)
3
4
5
6
0.27
2.53
0.68
9.85
b
t(b)
0.38
2.49
2
1
0.27
2.03
∆ logYτ −1
b
t(b)
Cnst.
0.34
3.63
0.36
3.80
0.30
3.34
0.32
3.45
0.28
2.68
∆ logYτ −2
-0.09
-1.31
-0.10
-1.43
-0.08
-1.09
-0.10
-1.37
-0.17
-2.09
∆ logYτ −3
0.11
1.42
0.10
1.32
0.13
1.92
0.12
1.68
0.08
0.92
∆ logYτ −4
-1.13
-1.38
-1.49
-2.07
2.43
1.89
3.11
2.11
2.30
1.24
ετ
-1.98
-1.73
ε oil−specific
-2.49
-2.22
-3.17
ε oil−specific
1.49
1.47
2.53
ε opposite
2.24
2.28
2.75
ε opposite
2.53
1.40
−1
ε opposite
ετ
−2
-2.55
-3.54
-2.82
-4.72
2.39
2.41
2.75
2.81
3.27
2.45
ετ
5.06
1.95
5.91
2.19
ετ
Dependent Variable: ∆ logYτ
past stock-market returns, Rτsm , commensurate with the lag structure of the novel predictors.
0.47
0.37
−2
1.29
0.89
2.06
1.35
ετ
ε economy−wide
1.59
1.23
1.92
−2
ε economy−wide
ετ
0.92
1.22
1.10
1.39
Rτsm
0.94
1.88
0.86
1.76
Rτsm−1
0.88
1.64
0.76
1.35
Rτsm−2
41.7%
39.5%
38.6%
36.4%
10.4%
26.6%
2
Radj
The predictor ετopposite is a composite index that incorporates the hypothesis that the effects of the aforementioned are of opposite sign. I control for
predictors ετoil − specific and ετeconomy − wide can be interpreted as innovations in the price of spot oil, during quarter τ that are due to oil-market-specific and
economy-wide shocks, respectively, identified by the sign of the correlation between oil price % changes and stock-market (excl. oil-companies) returns.
degrees of freedom. Real GDP growth rate, ∆ logYτ , is the continuously-compounded rate of change for real GDP during quarter τ . The novel
coefficients and t-statistics. I also present t-statistics to test the hypothesis that the sum of the predictors of interest equal zero, and the R 2 , adjusted for
This table shows statistics for predictive regressions of the quarterly US real GDP growth rate. The sample period is 1983:Q4-2011:Q4. I calculate OLS
α + δ CONTROLSτ + β 0ε τopposite + β1ε τopposite
∆ log Yτ =
+ β 2ε τopposite
+ uτ
−1
−2
Table III
0.27
2.4
0.54
4.69
0.42
4.13
0.27
1.98
b
t(b)
b
t(b)
b
t(b)
8
9
10
0.37
2.52
0.26
1.88
0.35
2.71
0.26
2.05
b
t(b)
∆ logYτ −1
7
Cnst.
0.31
2.93
0.28
3.15
0.34
3.9
0.33
3.13
∆ logYτ −2
-0.17
-2.06
-0.12
-1.60
-0.13
-1.64
-0.17
-2.10
∆ logYτ −3
0.07
0.87
0.08
1.06
0.02
0.24
0.06
0.71
∆ logYτ −4
ετ
−1
ετ
-0.12
-0.39
-0.25
-1.39
−2
0.07
1.07
-0.64
-0.92
-0.40
-0.87
-0.02
-0.35
Lee Shock
-2.25
-5.41
JDH Shock
-1.35
-4.12
-0.04
-0.90
-2.08
-1.89
-0.07
-0.19
Oil Price (%) Increase
0.60
1.21
Oil Price (%) Change
ετ
ετ
−1
ετ
−2
1.24
1.61
0.74
2.13
-0.60
-2.49
Oil Price (%) Decrease
ετ
Dependent Variable: ∆ logYτ
Table III – Continued
Rτsm
Rτsm−1
Rτsm−2
26.5%
36.9%
38.5%
30.0%
2
Radj
0.06
3.86
0.09
2.30
0.06
1.45
0.09
2.26
0.05
1.43
b
t(b)
t(Σb)
b
t(b)
b
t(b)
b
t(b)
b
t(b)
t(Σb)
2
3
4
5
6
0.10
1.94
b
t(b)
1
Cnst.
-0.20
-0.12
0.43
0.25
-0.12
-0.08
0.48
0.29
0.19
0.09
dpt
-2.14
-0.57
-5.50
-1.21
-2.92
-1.01
-6.08
-1.67
-7.85
-1.80
deft
0.92
0.58
0.77
0.46
1.02
0.62
0.90
0.53
1.82
1.03
termt
0.00
-0.02
-0.08
-0.68
0.07
0.68
0.13
1.58
0.21
1.77
Lag 1
-0.09
-0.60
-0.16
-1.29
0.13
1.23
0.20
1.98
0.31
2.18
Lag 2
3.67
0.38
3.98
0.35
5.37
0.35
5.06
-0.39
-3.26
-0.26
-0.38
-2.16
-3.21
-2.42
ε oil−specific
-0.31
-2.89
ε oil−specific
0.28
3.23
ε opposite
0.31
3.47
ε opposite
0.40
3.23
Lag 4
ε opposite
Lag 3
-0.44
-3.02
-0.43
-2.84
0.41
5.09
0.35
5.77
0.37
4.57
Lag 5
3-Month Oil Shock
-0.38
-2.68
-0.32
-2.55
0.36
3.19
0.28
3.31
0.28
3.08
Lag 6
0.18
1.14
0.22
1.36
Lag 1
0.16
0.97
0.23
1.32
Lag 2
0.33
2.64
0.34
2.15
2.52
0.31
2.44
ε economy−wide
0.34
1.88
Lag 4
ε economy−wide
Lag 3
0.43
3.17
0.33
3.26
Lag 5
3-Month Oil Shock
Dependent Variable: ∆D(sm
t +1,t +12)
0.35
2.18
0.26
2.04
Lag 6
opposite sign. I control for past stock-market returns, R(smt−2,t ) , commensurate with the lag structure of the novel predictors.
0.22
2.83
0.22
2.86
Lag 1
0.18
3.17
0.19
3.29
Lag 2
0.16
1.93
0.14
1.91
Lag 3
0.08
0.86
0.07
0.88
Lag 4
-0.01
-0.09
-0.02
-0.21
Lag 5
3-Month Market Return
-0.04
-0.41
-0.04
-0.47
Lag 6
34.4%
28.3%
35.2%
29.2%
24.0%
13.0%
2
Radj
changes and stock-market (excl. oil-companies) returns. The predictor ε (opposite
t − 2,t ) is a composite index that incorporates the hypothesis that the effects of the aforementioned are of
during the 3-month period {t-2,t-1,t} that are due to oil-market-specific and economy-wide shocks, respectively, identified by the sign of the correlation between oil price %
− wide
− specific
yields on portfolios of corporate bonds rated Baa and Aaa by Moody’. The novel predictors ε (oil
and ε (economy
can be interpreted as innovations in the price of spot oil,
t − 2,t )
t − 2,t )
of 12-month dividends accrued during months t+1 up to and including t+12, divided by the dividends accrued during months t-11 up to and including t. The controls dpt , termt ,
and deft follow definitions in Fama (1990): the dividend yield is the 12-month sum of dividend up to and including month t, divided by the price at the end-of-month t; the term
spread is the end-of-month t difference in yields of the constant maturities 10-year US Treasury-bond and the 3-month bill; The default spread is the end-of-month t difference in
This table show statistics for predictive regressions of the 12-month CRSP-universe stock-market dividend growth rate. The sample period is 1983:04-2011:12. I calculate OLS
coefficients and t-statistics which are adjusted for heteroscedasticity and serial-correlation, following Newey and West (1987), with 24 lags entering in the estimation of the
variance-covariance matrix of the residual matrix. Very similar results are obtained using Hansen and Hodrick (1980) standard errors, with 12 lags entering. I also present tstatistics to test the hypothesis that the sum of the predictors of interest equal zero, and the R 2 , adjusted for degrees of freedom. The dividend growth rate, ∆D(sm
t +1,t +12) , is the sum
∆D(sm
+ β 2ε (opposite
+ β 3ε (opposite
+ β 4ε (opposite
+ β 5ε (opposite
+ β 6ε (opposite
+ u( t +1,t +12)
α + δ CONTROLSt + β1ε (opposite
t +1,t +12) =
t − 2,t )
t −5,t −3)
t −8,t − 6)
t −11,t −9)
t −14,t −12)
t −17,t −15)
Table IV
11
10
9
8
7
-0.01
-0.01
0.63
0.27
0.09
2.25
0.06
0.98
0.09
2.29
b
t(b)
b
t(b)
b
t(b)
0.68
0.42
0.38
0.20
0.09
2.05
b
t(b)
0.14
0.09
0.12
2.95
dpt
b
t(b)
Cnst.
-5.98
-1.50
-1.22
-0.20
-6.62
-1.77
-6.84
-1.65
-8.97
-2.27
deft
0.86
0.47
1.12
0.73
0.98
0.56
1.40
0.74
1.45
0.89
termt
Lag 3
Lag 4
Lag 5
0.09
2.89
0.11
4.84
0.12
5.23
0.04
0.42
0.10
1.23
0.16
1.84
0.17
2.07
Sig. (5%) Correlation Dummy
0.05
1.81
Correlation Dummy
Lag 2
0.12
1.72
0.11
3.11
Lag 6
0.17
1.81
0.29
3.44
0.34
5.25
0.34
5.64
0.27
3.26
0.09
1.37
-0.03
-0.24
0.18
2.11
0.28
3.47
0.32
4.78
0.24
1.95
0.33
4.28
0.32
2.02
Sampled Quarterly
0.17
1.22
ε opposite
0.04
0.34
0.25
2.98
0.34
1.81
Remove 87’,90’-91’ & ’08-’09 Events
0.11
1.41
DeGARCH(1,1) Daily Series-by-Series
0.00
0.04
0.04
1.35
Lag 1
3-Month Oil Shock
Lag 1
Lag 2
Lag 3
Lag 4
Lag 5
3-Month Oil Shock
sm
Dependent Variable: ∆D(t+1,t+12)
Table IV – Continued (a)
Lag 6
Lag 1
Lag 2
Lag 3
Lag 4
Lag 5
3-Month Market Return
Lag 6
20.0%
10.1%
27.9%
16.3%
33.1%
2
Radj
15
14
13
12
0.10
2.13
0.12
2.51
0.11
1.96
0.11
2.15
b
t(b)
b
t(b)
b
t(b)
b
t(b)
Cnst.
-0.02
-0.01
-0.38
-0.21
-0.17
-0.10
0.11
0.06
dpt
-7.78
-1.77
-4.43
-1.38
-3.69
-0.86
-7.68
-1.64
deft
1.77
0.99
1.35
0.77
1.41
0.75
1.72
0.93
termt
0.000
0.02
-0.04
-0.32
-0.06
-0.92
0.00
0.00
Lag 1
Lag 3
Lag 4
Lag 5
0.00
-0.07
-0.01
-0.16
-0.02
-0.42
-0.004
-0.54
-0.16
-1.64
-0.11
-1.71
-0.11
-2.23
-0.24
-3.42
-0.004
-0.51
-0.005
-0.84
Lee Shock
-0.22
-3.70
JDH Shock
-0.11
-1.97
-0.006
-0.96
-0.22
-2.34
-0.05
-0.90
Oil Price (%) Increase
-0.01
-0.13
Oil Price (%) Change
Lag 2
3-Month Oil Shock
-0.004
-0.79
-0.14
-1.05
0.00
-0.07
-0.02
-0.57
Lag 6
0.08
1.44
Lag 1
Lag 3
Lag 4
Lag 5
0.09
1.62
0.07
1.03
0.04
0.82
-0.02
-0.32
Oil Price (%) Decrease
Lag 2
3-Month Oil Shock
sm
Dependent Variable: ∆D(t+1,t+12)
Table IV – Continued (b)
-0.05
-1.00
Lag 6
Lag 1
Lag 2
Lag 3
Lag 4
Lag 5
3-Month Market Return
Lag 6
12.2%
21.1%
16.8%
11.6%
2
Radj
mean(b)
t(b)
mean(b)
t(b)
mean(b)
t(b)
1
2
3
predictor.
-0.05
-1.58
-0.04
-1.11
-0.03
-0.75
Cnst.
0.005
1.65
0.005
1.46
0.005
1.49
MCti
-0.013
-1.51
-0.013
-1.48
-0.012
-1.36
B / M ti
-0.31
-3.01
ε opposite
-0.26
-2.20
ε opposite
-0.04
-0.31
-0.08
-0.65
ε (it,opposite
ε (it,opposite
− 2,t )
−5,t −3)
i
Dependent Variable: RX(t+1,t+6)
0.04
1.50
R(it − 2,t )
0.05
1.59
R(it −5,t −3)
21.1%
13.0%
8.8%
2
Radj
stock-market returns. I also control for industries’ past returns, R(it − 2,t ) , commensurate with the lag structure of the novel
both oil-market-specific and economy-wide shocks, identified by the sign of the correlation between oil price changes and
ratio. The novel predictor ε (it,opposite
− 2,t ) could be interpreted as change in the price of oil during months {t-2,t-1,t} that are due to
is the excess return for the ith out of the 49 Fama and French industries, accrued during months t+1 up to and including
t+6. The controls MCti and B / M ti are the natural logarithm of the average firm’s market capitalization and book-to-market
The table shows average slopes and their t-statistics from monthly cross-section regressions to predict 6-month industries’
stock excess returns, over the risk-free rate. I estimate the variance-covariance matrix of the coefficient estimates from the
time-series, using a multivariate version of Newey and West (1987), with 12 lags entering. The dependent variable, RX (it +1,t + 6) ,
+ β 2ε (it,opposite
RX (it +1,t + 6) =
α + δ CONTROLSt + β1ε (it,opposite
− 2,t )
− 5,t −3) + u( t +1,t + 6)
Table V
0.04
1.14
1.28
0.03
1.01
0.03
1.16
0.04
1.27
0.03
1.15
0.03
1.26
b
t(b)
b
t(b)
t(Σb)
b
t(b)
t(Σb)
b
t(b)
t(Σb)
b
t(b)
t(Σb)
b
t(b)
t(Σb)
1
2
3
4
5
6
Cnst.
0.55
0.98
0.48
0.92
0.52
0.94
0.45
0.85
1.07
2.21
2.43
BSt6
-1.78
-3.14
-1.21
-2.54
-1.64
-3.93
-1.01
-3.24
-1.20
-4.74
ε ( t−2,t )
-2.08
ε same
-2.93
ε same
-4.53
-0.36
-0.81
-0.46
-1.74
-0.56
-2.36
-1.35
0.05
0.12
-0.75
-1.16
-1.35
0.09
0.18
ε oil−specific
-0.61
-1.12
ε oil−specific
-1.05
-1.79
-0.90
-2.59
-1.07
-4.31
ε ( t−8,t−6)
ε same
ε ( t−5,t−3)
0.46
0.83
-0.27
-0.97
0.19
0.39
-0.67
-2.82
-0.74
-3.23
ε ( t −11,t −9)
-1.21
-3.16
-0.55
-1.76
ε ( t−2,t )
-2.79
-0.65
-1.79
-1.27
-1.85
-1.99
-0.55
-1.06
ε economy−wide
-1.20
-3.50
ε ( t−8,t−6)
ε economy−wide
ε ( t−5,t−3)
o
Dependent Variable: ∆ log P(t+1,t+6)
-0.18
-0.35
-0.99
-2.76
ε ( t −11,t −9)
0.27
1.99
0.29
2.03
∆ log P(ot−2,t )
0.05
0.19
0.07
0.27
∆ log P(ot−5,t −3)
-0.02
-0.13
-0.02
-0.13
∆ log P(ot−8,t −6)
-0.33
-1.39
-0.37
-1.52
∆ log P(ot−11,t −9)
16.9%
15.2%
15.7%
13.4%
12.7%
6.8%
2
Radj
This table shows statistics for predictive regressions of the 6-month % change in the price of spot oil. The sample period is 1983:04-2011:12. I calculate OLS
coefficients and t-statistics which are adjusted for heteroscedasticity and serial-correlation, following Newey and West (1987), with 12 lags entering in the
estimation of the variance-covariance matrix of the residual matrix. Very similar results are obtained using Hansen and Hodrick (1980) standard errors, with 6
lags entering. I also present t-statistics to test the hypothesis that the sum of the predictors of interest equal zero, and the R 2 , adjusted for degrees of freedom.
The spot oil % change, ∆ log P(ot+1,t + 6) , accrues during the months t+1 up to and including t+6. I control for (the negative of the) 6-month basis spread,
− wide
and ε (economy
can be interpreted as innovations in the price of spot oil, during the 3-month period {t-2,t-1,t}
=
BSt6 log Ft 6 − log Pt o . The novel predictors ε (oilt −−2,specific
t − 2,t )
t)
that are due to oil-market-specific and economy-wide shocks, respectively, identified by the sign of the correlation between oil price % changes and stockmarket (excl. oil-companies) returns. The predictor ε (same
t − 2,t ) is a composite index that incorporates the hypothesis that the effects of the aforementioned are of
o
same sign. I control for past oil-price changes, ∆ log P(t −2,t ) , commensurate with the lag structure of the novel predictors.
∆ log P(ot +1,t + 6) =
α + δ CONTROLSt + β1ε (same
+ β 2ε (same
+ β 3ε (same
+ β 4ε (same
+ u( t +1,t + 6)
t − 2,t )
t −5,t −3)
t −8,t − 6)
t −11,t − 9)
Table VI
0.04
1.30
0.04
1.39
0.04
1.22
0.04
1.30
0.03
1.18
0.03
1.26
b
t(b)
b
t(b)
t(Σb)
b
t(b)
t(Σb)
b
t(b)
t(Σb)
b
t(b)
t(Σb)
b
t(b)
t(Σb)
1
2
3
4
5
6
Cnst.
-0.55
-0.96
-0.51
-0.98
-0.58
-1.06
-0.55
-1.07
0.07
0.17
BSt6
-1.23
-2.13
-1.01
-1.95
-1.41
-2.85
-1.12
-3.24
-0.89
-4.17
ε ( t−2,t )
-1.62
ε same
-2.78
ε same
-3.49
-0.32
-0.68
-0.58
-2.07
-0.46
-2.27
-1.26
-0.15
-0.39
-0.42
-0.67
-0.79
0.02
0.04
ε oil−specific
-0.28
-0.56
ε oil−specific
-0.84
-1.43
-0.73
-2.16
-0.52
-2.26
ε ( t−8,t−6)
ε same
ε ( t−5,t−3)
0.40
0.75
-0.47
-1.70
0.23
0.49
-0.71
-2.88
-0.63
-3.36
ε ( t−11,t−9)
-1.35
-2.77
-1.02
-2.73
ε ( t−2,t )
-1.17
-1.81
-2.80
-1.73
-0.49
-0.86
-0.75
-1.83
ε economy−wide
-1.09
-3.63
ε ( t−8,t−6)
ε economy−wide
ε ( t−5,t−3)
0.03
0.05
-0.89
-2.21
ε ( t−11,t−9)
Dependent Variable: log P(ot+ 6) − log Ft 6
0.11
0.75
0.13
0.77
∆ log P(ot−2,t )
0.05
0.19
0.05
0.19
∆ log P(ot−5,t −3)
-0.08
-0.42
-0.10
-0.52
∆ log P(ot−8,t −6)
-0.39
-1.69
-0.41
-1.78
∆ log P(ot−11,t −9)
9.7%
9.5%
9.5%
7.5%
6.3%
-0.3%
2
Radj
This table shows statistics for predictive regressions of the risk-premium in 6-months (long) oil future contract. The sample period is 1983:04-2011:12. I
calculate OLS coefficients and t-statistics which are adjusted for heteroscedasticity and serial-correlation, following Newey and West (1987), with 12 lags
entering in the estimation of the variance-covariance matrix of the residual matrix. Very similar results are obtained using Hansen and Hodrick (1980) standard
errors, with 6 lags entering. I also present t-statistics to test the hypothesis that the sum of the predictors of interest equal zero, and the R 2 , adjusted for degrees
of freedom. The risk-premium, log P(ot+ 6) − log Ft 6 ,accrues during the months t+1 up to and including t+6. I control for (the negative of the) 6-month basisoil − specific
− wide
spread,=
and ε (economy
can be interpreted as innovations in the price of spot oil, during the 3-month period {tBSt6 log Ft 6 − log Pt o . The novel predictors ε ( t − 2,t )
t − 2,t )
2,t-1,t} that are due to oil-market-specific and economy-wide shocks, respectively, identified by the sign of the correlation between oil price % changes and
stock-market (excl. oil-companies) returns. The predictor ε (same
t − 2,t ) is a composite index that incorporates the hypothesis that the effects of the aforementioned
o
are of same sign. I control for past oil-price changes, ∆ log P(t −2,t ) , commensurate with the lag structure of the novel predictors.
+ β 2ε (same
+ β 3ε (same
+ β 4ε (same
+ u( t +1,t + 6)
α + δ CONTROLSt + β1ε (same
log P(ot + 6) − log Ft 6 =
t − 2,t )
t −5,t −3)
t −8,t − 6)
t −11,t −9)
Table VII
The blue dots are monthly correlations between oil price % changes and stock market returns (excl. Internationally Diversified OilCompanies (IDOC)), calculated with intra-month daily data. The red dots are correlations that are significantly different than zero at the 5%
rejection level. The solid-black line is the log-level of the spot price of crude oil (West Texas Intermediate at Cushing, OK). The grey-shaded
areas are NBER recessions and the horizontal lines are conspicuous oil-market-specific and economy-wide events.
Figure 1
Figure 2
Changes in the price of spot oil that are due to Oil-Market-Specific (top panel) or
Economy-Wide (bottom panel) shocks. The shocks are identified by the sign of the intramonth daily correlation between oil-price %-changes and the US stock-market returns
(excl. oil-companies). If the correlation is negative (positive) then the monthly change in
the price of oil is classified as being due to the former (latter) shock.
Figure 3
Combined predictive variables which are interpreted as changes in the spot price of oil that
are due to both Oil-Market-Specific and Economy-Wide shocks. The top panel incorporates
the hypothesis that a given change in the price of oil has opposite macroeconomic effects,
depending on which shock originated it. The bottom panel incorporates the hypothesis that
a given change in the price of oil has the same effects on select oil-market-specific variable of
interest, independent on which shock originated it. The top panel could also be interpreted
as fitted stock-market returns using contemporaneous oil price changes, scaled by the relative
variance of oil price changes and stock market returns.
Predictive regression of 12-month US excess stock market return using four lags of the novel 3-month oil shock predictor.
The blue and red lines are the realized and predicted excess stock market returns, or aggregate risk-premium in equities. The
horizontal event-dates are shifted to match the corresponding variation in the risk-premium, while the grey-shaded areas are
NBER recessions.
Figure 4
Figure 5
From top to bottom: (1) spot price of oil % change (log-difference); (2) novel oil-marketspecific shock proposed in this paper; (3) oil price increases defined as the log difference
between oil prices, if it is positive, and zero otherwise, (4) net-oil-price-increase defined as the
log difference in oil prices, if the most recent price is above the previous 36-months high, and
zero otherwise, (5) orthonormalized oil price % changes from a GARCH(1,1) model with four
monthly autoregressive terms for the mean.
Explanatory regressions of quarterly (annualized) real GDP growth rate, realizations of which are plotted in blue. I control for
four autoregressive lags as a comparison benchmark, of which the black broken-line is the predicted value. The red line is the
predicted value of a regression that adds a contemporaneous, plus two quarterly lags, of the novel oil-market-specific and
economy-wide shocks, of which the red line is the predicted value.
Figure 6
Predictive regression of 12-month US stock market dividend growth rate using four lags of the novel 3-month oil shock predictor. Dividends
are summed over 12 months and divided by the lagged 12 months dividends, year-over-year. Within the month, dividends are reinvested in the
market portfolio. The blue and red lines are the realized and predicted excess stock market returns, or aggregate risk-premium in equities. The
horizontal event-dates are shifted to match the corresponding variation in the risk-premium, while the grey-shaded areas are NBER recessions.
Figure 7
Predictive regression of 6-month spot oil % price change using four lags of the novel 3-month oil shock predictor. The blue and red
lines are the realized and predicted values. The horizontal event-dates are shifted to match the shock and corresponding predictable
variation, while the grey-shaded areas are NBER recessions.
Figure 8
Predictive regression of the excess return from investing long in 6-month oil futures, held to maturity, using four lags of the
novel 3-month oil shock predictor. The blue and red lines are the realized and predicted values. The horizontal event-dates are
shifted to match the shock and corresponding predictable variation, while the grey-shaded areas are NBER recessions.
Figure 9
Illustration of the oil-sector’s inconvenience cost function.
Figure 10
Figure 11
Illustration of the effects of irrational (long-side) speculation on the spot oil market.
Impulse response functions of macroeconomic and oil-market-specific variables to an adverse Oil
Supply shock (Oil Production Flow).
Figure 12
Impulse response functions of macroeconomic and oil-market-specific variables to an adverse
Precautionary Demand Shock of Type I (News about Future Production Flow).
Figure 13
Shock of Type II (Uncertainty of Production Flow).
Impulse response functions of macroeconomic and oil-market-specific variables to an adverse Precautionary Demand
Figure 14
Impulse response functions of macroeconomic and oil-market-specific variables to a long-side
Speculation Shock (I.I.D Wedge).
Figure 15
Impulse response functions of macroeconomic and oil-market-specific variables to an adverse
Economy-Wide Shock (Productivity).
Figure 16