Download Oil Price Uncertainty in a Small Open Economy Yusuf Soner BAŞKAYA Timur HÜLAGÜ

WORKING PAPER NO: 13/09
Oil Price Uncertainty in a Small Open
Economy
February 2013
Yusuf Soner BAŞKAYA
Timur HÜLAGÜ
Hande KÜÇÜK
© Central Bank of the Republic of Turkey 2013
Address:
Central Bank of the Republic of Turkey
Head Office
Research and Monetary Policy Department
İstiklal Caddesi No: 10
Ulus, 06100 Ankara, Turkey
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The views expressed in this working paper are those of the
author(s) and do not necessarily represent the official views of the
Central Bank of the Republic of Turkey. The Working Paper Series
are externally refereed. The refereeing process is managed by the
Research and Monetary Policy Department.
Oil Price Uncertainty in a Small Open Economy
Yusuf Soner Başkaya
Timur Hülagü
Hande Küçük1
Abstract
We analyze business cycle implications of oil price uncertainty in an oil-importing small
open economy, where oil is used for both consumption and production. In our framework,
higher volatility in oil prices works through two main channels. On the one hand, it makes
the marginal product of capital riskier, creating an incentive to substitute away from capital.
On the other hand, it increases the demand for precautionary savings, which might imply
higher capital accumulation in response to a rise in oil price uncertainty depending on
whether agents have access to an alternative asset, international bond in our model. We
show that the fall in investment following a rise in the volatility of real oil prices in the case
of financial integration is more than twice the fall in investment observed under financial
autarky. Moreover, the interaction between shocks to the level and volatility of oil prices
is quantitatively important: initial responses of investment, output and consumption to a
rise in oil prices are almost doubled, when there is a simultaneous rise in the volatility of
oil prices.
Keywords: Oil price, stochastic volatility, financial market integration.
JEL classification: E20; E32; F32; F41; Q43.
1
Introduction
One of the challenges faced by the world economy is the highly volatile nature of prices
of energy items, such as oil (Figures 1.a, 1.b, 1.c). In particular, during events like the
Iranian revolution in 1979, collapse of OPEC oil cartel in 1986, the first Gulf War in 1991
and more recently the global financial crisis, not only the level but also the volatility of oil
prices have changed, implying higher uncertainty regarding the future path of oil prices.2
Such periods are also associated with heightened concerns by policy makers, politicians or
1
Research and Monetary Policy Department, Central Bank of Turkey, Ankara, 06100, Turkey. e-mail:
[email protected], [email protected], [email protected]. We are grateful to the
editor Pierre-Olivier Gourinchas, two anonymous referees of the IMF Economic Review and our discussant
Gianluca Benigno for insightful comments and suggestions that significantly improved our paper. We also
thank Harun Alp, Martin Andreasen, Bianca De Paoli, Refet S. Gürkaynak, Koray Kalafatçılar, Hakan Kara,
Ayhan Köse, Stefan Laseen, Juan Rubio-Ramirez, Kostas Theodoridis, Deren Ünalmış, Canan Yüksel as well
as the participants at the IMF/CBRT conference on ”Policy Responses to Commodity Price Movements”;
and the participants at seminars held at the Central Bank of Turkey and Sveriges Riksbank for valuable
comments and discussions. All errors are our own. The views expressed in this paper are those of authors
and do not necessarily reflect the official views of the Central Bank of Turkey.
2
Implied volatility based on oil futures is a more direct measure of uncertainty compared to realized
volatility which is depicted in Figures 1.b and 1.c. We preferred to use the realized volatility measure
1
international institutions about the possible detrimental effects of high volatility of oil prices
on macroeconomic performance. For example, the former British prime minister Gordon
Brown and former French president Nicolas Sarkozy states in a joint statement published
on the Wall Street Journal on July 8, 2009:
“For two years the price of oil has been dangerously volatile, seemingly defying
the accepted rules of economics. First it rose by more than $80 a barrel, then
fell rapidly by more than $100, before doubling to its current level of around
$70...The oil market is complex but such erratic price movement in one of the
world’s most crucial commodities is a growing cause for alarm...The risk is that
a new period of instability [in oil prices] could undermine confidence just as we
are pushing for recovery...”
More recently, on January 30, 2012, OPEC’s Chief General Abdalla S. El-Badri warned
about the adverse effects of oil price volatility caused by the political tensions with Iran:3
“Volatility is bad for investment. If there is conflict [in Iran] we really cannot
invest, there will be clouds all over the investment projects.”
In this paper, we investigate the implications of exogenous changes in uncertainty about
oil prices for oil importing small open economies. Our aim is to offer a simple framework,
which allows us to answer how the dynamics of key macroeconomic variables such as output,
investment, consumption and net foreign assets are affected by exogenous shifts in the
uncertainty about oil prices. This analysis is important because there is not much work
addressing these questions, despite the widespread attention to this topic.
While the recent literature offers a number of empirical studies mostly relying on timeseries analysis,4 there is little work on the effects of oil price volatility on macroeconomic
performance in a general equilibrium setting. We aim at filling this gap by building a small
open economy RBC model allowing for exogenous shifts not only in the level of oil prices
but also the volatility of oil prices. Following Mendoza (1995), we specify a model economy
with both tradable and non-tradable goods sectors, which also engages in trade in a noncontingent international bond denominated in tradable good. We extend this model to
include oil in consumption and production along the lines of Kim and Loungani (1992) and
because implied volatility series OVX (The CBOE Crude Oil ETF Volatility Index) starts only from May
2007. The correlation between OVX and our measure of realized volatility is around 0.90. Thus, we use
volatility, risk and uncertainty interchangeably throughout the paper
3
Source:http://online.wsj.com/article/BT-CO-20120131-712362.html. See also Başçı(2011).
4
For example, Ferderer (1996), Guo and Kliesen (2005) and Elder and Serletis (2010) show the negative
effect of higher oil price volatility on investment and output growth for the U.S. economy.
2
Blanchard and Gali (2007).5 In our framework, both level and volatility of the real price of
oil as well as the total factor productivity in tradable and non-tradable sectors are subject to
exogenous changes. Then, we calibrate our model considering four oil importing developed
small-open economies - Australia, Netherlands, New Zealand and Sweden - to explore the
macroeconomic implications of changes in oil price uncertainty.
To the best of our knowledge, the only previous attempt to study business cycle implications of variations in oil price volatility in a general equilibrium setting is Plante and Traum
(2012), who analyze the effects of oil price uncertainty for the U.S. economy, abstracting
from open economy questions like how real exchange rate and external debt adjust to higher
oil price uncertainty. Unlike most of the empirical studies, Plante and Traum (2012) argue
that an increase in the uncertainty about oil prices may lead to an expansion in the economy
mainly by increasing investment in physical capital due to higher demand for precautionary
savings.
Our analysis points at different results regarding the transmission of oil price volatility
shocks, mainly due to the openness to international financial markets. In particular, we show
that increased demand for precautionary savings due to higher uncertainty has different
effects on output and investment depending on whether households can save in international
bonds or not. In the case of financial integration, the decline in investment due to a rise
in the volatility of real oil prices is more than twice the decline observed under financial
autarky. What is more, the rise in uncertainty in the former setup has its maximum effect
on impact, while in the latter it takes about five quarters for the shock to have its peak
effect. This is mainly because the existence of an alternative saving instrument, i.e. an
internationally traded non-contingent bond, makes it possible for the agent to substitute
away from physical capital, which becomes more risky following an increase in oil price
volatility. In a parallel manner, we find that the net foreign asset accumulation increases
as agents channel their precautionary savings to the international bond.6
To shed light on the channels of transmission, we also conduct a sensitivity analysis
with respect to the different values of parameters governing the degree of risk aversion,
the elasticity of substitution between oil and other inputs in the production process and
the elasticity of substitution between tradable and non-tradable goods in consumption. If
agents are more risk averse, the desire to substitute away from capital is stronger in the face
of uncertainty shocks. Hence, we find that higher degrees of risk aversion are associated with
larger falls in physical investment, GDP and consumption following a rise in uncertainty,
5
See also Backus and Crucini (1998) and Bodenstein et al. (2011) who make similar modeling assumptions
regarding oil use in the economy. However, they analyze the implications of shocks that change the level of
oil prices in a two-country framework.
6
Küçük (2011) also shows the importance of the degree of financial market integration for the extent of
precautionary savings conditional on income uncertainty shocks focusing on the dynamics of real interest
rates, real exchange rate and foreign exchange risk premium in a two-country setup.
3
although differences are quantitatively small. On the other hand, higher substitutability
of oil in production leads to milder responses in output, consumption and investment as it
becomes relatively easier to substitute away from the input that has become more risky.
We also find that exogenous rise in the level and volatility of oil prices have similar
qualitative implications, although they affect the economy through different channels. While
the dominant channel in the case of level shocks is the rise in the marginal cost of capital
due to higher oil prices, the interaction between higher demand for precautionary savings
and increased riskiness of capital matter in the case of volatility shocks. Quantitatively, the
distinct effect of volatility shocks are much smaller than the effect of level shocks, reflecting
the fact that the former only appears as an higher order term. However, volatility shocks
become much more important when they are considered in conjunction with level shocks.
We find that the initial responses of investment, output and consumption to an exogenous
oil price increase are almost doubled when there is also a simultaneous rise in the volatility
of oil prices.
We finally consider a simple extension that allows us to consider a simultaneous increase
or decrease in world demand alongside a rise in oil price or its volatility. This exercise is
motivated by the recent literature arguing that the effects of oil price shocks can change depending on whether they are global-demand driven or supply-driven. The extended model
implies that the initial fall in consumption and investment following a rise in oil prices is
about 40 percent smaller when the shock is accompanied by a simultaneous increase in world
demand as opposed to a simultaneous decrease in world demand, arguably corresponding
to the case of negative oil supply shocks. We also consider a situation where higher uncertainty regarding the path of commodity prices is accompanied with changes in the level
of global economic activity. We show that higher oil price volatility observed along with
a simultaneous decline in global economic activity can lead to a sizable contraction. In
contrast, a simultaneous expansion in the global economy can mitigate the contractionary
effects of higher oil price volatility.
Our analysis can be viewed as part of the vastly growing recent literature on the macroeconomic implications of uncertainty shocks.For example, Bloom (2009) shows that the
shocks to uncertainty about firm-level productivity can have a negative effect on output via
causing investment delays. Fernandez-Villaverde et al. (2011a) find that increases in the
volatility of real interest rate can trigger recessions in emerging markets, while FernandezVillaverde et al. (2011b) and Born and Pfeiffer (2011) focus on the adverse effects of
higher uncertainty about taxes and fiscal expenditures on economic activity. Using a model
calibrated for Chilean economy, Pfeiffer et al. (2012) show that higher terms of trade uncertainty may lead to significant decline in output. While having important methodological
and conceptual similarities with these studies, our study differs from them with its particular emphasis on the transmission of oil price uncertainty shocks in a small developed open
4
economy.
The rest of the paper is structured as follows. We introduce our model economy in the
next section. The solution method, model calibration and numerical results are presented
in Section 4. Section 5 concludes the paper.
2
The Model
We set up a small open economy model with incomplete asset markets in line with Mendoza
(1995). The economy consists of a representative agent, and perfectly competitive firms in
tradable and non-tradable good sectors that use labor, capital and imported oil in the production process. We introduce oil as in Blanchard and Gali (2007), where both firms and
households demand imported oil at an exogenously determined price. A particular feature
of our model is that the real price of oil displays stochastic volatility. The representative household supplies labor, consumes the composite final consumption good made up of
tradable and non-tradable goods and allocates their savings across domestically produced
physical capital and internationally traded foreign bond. The trade in international bond
is subject to convex portfolio adjustment costs, the level of which also governs whether the
economy is operating in an environment with financial integration or financial autarky.
2.1
Households
The expected lifetime utility of the representative household is given by:
E0
∞
X
t=0
1
β
1−σ
t
h1+η
Ct − t
1+η
!1−σ
,
(1)
where β is the discount factor satisfying 0 < β < 1, σ is the risk aversion parameter, Ct is
the final good consumption, Ct , is composed of tradable, CtT , and nontradable goods, CtN ,
and ht is the total hours worked, which is the sum of hours worked in the tradable and
non-tradable sectors, i.e. hTt and hN
t , respectively. The representative household has GHH
preferences, introduced by Greenwood et al. (1988), which implies that there is no wealth
effect on labor supply and that the marginal rate of substitution between consumption and
leisure depends only on the real wage. These preferences are frequently used in small open
economy models due to its ability to improve the models’ performance in matching certain
business cycle facts.7 η corresponds to the inverse of the (Frisch) wage elasticity of labor
supply.
The representative household’s final good consumption, Ct , is composed of tradable,
7
For example, see Mendoza (1991), Neumeyer and Perri (2005) and Fernandez-Villaverde et al. (2011a).
5
CtT , and nontradable goods, CtN , shown as
Ct = γ
1
κ
CtT
κ−1
κ
+ (1 − γ)
1
κ
CtN
κ−1
κ
κ
κ−1
.
(2)
In (2), γ is the weight of tradable goods in consumption while κ is the elasticity of substitution between CtT and CtN . The price of the final good is Pt , while the prices of tradable and
non-tradable goods are PtT and PtN , respectively. Similarly, the tradable good is composed
of oil, CtO , and a non-oil good, CtQ , with the CES aggregator
CtT = ν
1
ξ
CtQ
ξ−1
ξ
+ (1 − ν)
1
ξ
ξ−1
O ξ
ξ
ξ−1
Ct
,
(3)
where ν is the weight of non-oil good in tradable goods consumption while ξ is the elasticity
of substitution between oil and non-oil goods. The inclusion of oil in tradable consumption
basket reflects the fact that oil is an imported commodity and constitutes an important
household consumption item for transportation and heating.8 The prices of final consumption good and the tradable consumption good are given by:
h
Pt = γ
and
PtT
1−κ
PtT
+ (1 − γ)
1−κ
PtN
i
1
1−κ
1
1−ξ
1−ξ
Q
O 1−ξ
= ν Pt
+ (1 − ν) Pt
.
(4)
(5)
The price of the non-oil tradable good is normalized, i.e. PtQ = 1, while price of oil follows
an exogenous stochastic process presented in the proceeding section.9
The flow budget constraint faced by the representative household is given by:
It +
ΦK
ΦB
PN
(Kt+1 − Kt )2 + CtT + tT CtN + Bt = wt ht + rt Kt + Rt Bt−1 −
(Bt − B̄)2 , (6)
2
2
Pt
where
It = Kt+1 − (1 − δ)Kt .
(7)
It is investment and δ is the depreciation rate on physical capital, Kt . We specify quadratic
adjustment costs for capital as in Mendoza (1991), governed by a parameter ΦK . We
8
For example, according to the OECD data, the shares of energy items in CPI, (which constitute ‘electricity, gas and other fuels’ and ‘fuels and lubricants for personal transport’) for Netherlands, New Zealand
and Sweden have been 9.2 percent, 10.4 percent and 9.1 percent in 2011, respectively.
9
Note that the price of tradables, PT , which is composed of oil prices that follow an exogenous stochastic
process and non-oil tradable prices which are normalized to 1, is exogenously determined in the model, as
can be observed in Equation (5).
6
also assume costly capital mobility to maintain a stationary net foreign assets equilibrium
following Turnovsky (1985).10 wt and rt are the wage rate and the rental rate of capital,
respectively.11 All variables except non-tradable consumption are expressed in units of the
tradable good. Hence, to express non-tradable consumption in units of the tradable good,
we multiply it with the relative price of non-tradable goods, P N /P T , which corresponds
to the real exchange rate in our model. This definition implies that an increase in the real
exchange rate is a real domestic appreciation.
Bt is the international bond level with an associated steady state value of B̄, where
the gross return from holding one unit of foreign assets, i.e. the world interest rate, is
exogenous and given by Rt > 1.12 The degree of portfolio adjustment costs is determined
by the parameter ΦB , which can be interpreted as a measure of financial market integration,
as in Sutherland (1996) and Benigno (2009). The higher the value of ΦB , the less integrated
is the economy with international financial markets. In the limiting case of ΦB → ∞, the
economy operates under financial autarky.
Finally, combining household budget constraint in Equation (6) with profit functions of
firms and the non-tradable good market clearing condition, we obtain the current account
equation:
CtT + It + Bt +
2.2
ΦK
ΦB
PO
(Kt+1 − Kt )2 +
(Bt − Bt−1 )2 = Rt Bt−1 + YtT − tT (OtT + OtN ). (8)
2
2
Pt
Firms and Production
Tradable (T) and non-tradable (NT) sector firms operate in perfectly competitive markets
and have the following production functions respectively:
1
θ
YtT = a1 ATt (hTt )
YtN
1
φ
= a2
αT
(KtT )
1−αT
θ−1
N
N αN
AN
(KtN )1−α
t (ht )
10
θ
φ−1
φ
+ (1 − a1 )
1
θ
+ (1 − a2 )
1
φ
θ−1
T
θ
θ
θ−1
Ot
OtN
φ−1
φ
,
(9)
φ
φ−1
,
(10)
Specifying convex portfolio adjustment costs is one way of ensuring stationarity in incomplete market
models, as discussed by Schmitt-Grohe and Uribe (2003) for small open economies.
11
We assume perfect labor mobility among sectors, leading to equal wage rates across sectors.
12
In the baseline calibration, we take the world interest rate as constant, i.e. Rt = R. This choice is
mainly driven by the length of the time it takes to solve and simulate the model with interest rate shocks.
Without interest rate shocks the model already has six state variables. Adding interest rate shocks as a
seventh state variable increases the computation time considerably given the third-order solution algorithm
we use (see Section 2.5). Because we solve the baseline model many times under different parameter values
and shock structures (with and without foreign demand shocks/TFP volatility shocks), we wanted to keep
the baseline model computationally more manageable. Appendix B shows the results for the case when the
world interest rate follows the stochastic process.
7
where Ait is the non-oil factor productivity in sector i = T, N . In a similar manner, Kti ,
hit and Oti are the amount of capital, labor and oil used in the production in sector i. αi is
the share of labor in non-oil factors used in sector i. The parameters a1 and a2 determine
the shares of oil and non-oil factors in the production of tradable and non-tradable sectors.
Finally, θ and φ represent the elasticities of substitution between oil and non-oil factors of
production in tradable and non-tradable sector, respectively.
The problem of the representative firm in sector i (i = T, N ) can be summarized as
choosing hit , Kti and Oti in order to maximize profit
Πit = Yti − wt hit − rt Kti −
PtO i
O.
Pti t
(11)
subject to production technologies, factor prices and the stochastic productivity processes:
log ATt+1 = (1 − ρT ) log ĀT + ρT log ATt + µT εTt+1 ,
(12)
N
N
N
N
N
log AN
t+1 = (1 − ρ ) log Ā + ρ log At + µN εt+1 ,
(13)
where 0 ≤ ρT < 1, 0 ≤ ρN < 1, εTt and εN
t are identically and independently distributed
2
2
with N (0, 1). The variances of log ATt and log AN
t are given as µT and µN , respectively.
2.3
Oil Prices
Considering an oil-importing small open economy, we take oil prices as exogenous. In our
model, the real price of oil, defined as the log difference between the imported oil price,
PtO , and the consumer price index, Pt , has the following process which displays stochastic
volatility:
log
O
Pt+1
Pt+1
!
P
= ρ log
PtO
Pt
+ µP,t+1 t+1 ,
(14)
where t is identically and independently distributed disturbance term with N (0, 1), affecting the level of real price of oil. As a key characteristic of our model, the conditional
volatility of oil prices can stochastically change over time for a given level of prices, as µP,t
follows an autoregressive process:
log µP,t+1 = (1 − ρµP )µ̄P + ρµP log µP,t + ζP,t+1 ,
(15)
where ζP,t is identically and independently distributed with N (0, ψP2 ). Finally, 0 ≤ ρµP < 1
governs the persistence of log(µP,t ).
8
2.4
Calibration
We calibrate the model at a quarterly frequency considering four oil-importing developed
small open economies, namely Australia, Netherlands, New Zealand and Sweden.13 The
calibration of the parameters are presented in Table 1.
2.4.1
Parameters Related to Use of Oil in Production and Consumption
The parameters governing the elasticities of substitution and the share of oil in the economy
are calibrated using existing estimates on the Australia, Netherlands, New Zealand and
Sweden and the official data on share of oil consumption in these countries. The parameters
for elasticities of substitution between oil and non-oil goods in tradable and non-tradable
production, i.e. θ and φ respectively, are taken to be 0.23 using the average value of the
estimates of Cooper (2003) for price elasticity of crude oil in Australia, Netherlands, New
Zealand and Sweden. We also take ξ, i.e. the elasticity of substitution between oil and
non-oil goods in tradable consumption, as 0.23 in the absence of an empirical estimate on
this parameter.
We take the share of oil input in both production and consumption as 6 percent, using the
data on energy intensity ratios by countries, i.e. the ratio of gross inland energy consumption
to the GDP, and the share of crude oil and petroleum products in gross inland energy
consumption. The 2006-2009 average values of PPP-adjusted energy intensity ratios are 13
percent for Netherlands, 16 percent for Sweden, 17 percent for New Zealand and 18 percent
for Australia.14 Using the data on share of crude oil and petroleum products in gross inland
energy consumption over the same years, we find the share of oil use in GDP as 4.7 percent
for Netherlands, 5.8 percent for Sweden, 5.6 for New Zealand and 7.5 percent for Australia,
giving an average value around 6 percent.15 Assuming that the steady-state share of oil
in output is equal across sectors, this requires setting a1 =a2 = 0.94. The share of oil
consumption in tradable goods, 1 − ν, is set as 0.0986 so that the share of oil consumption
in total consumption is 6 percent.
2.4.2
Other Parameters
Parameters other than the ones regarding the use of oil in production and consumption
are calibrated in line with the widely-used values in literature. The discount factor β is
taken as 0.9975, which corresponds to an annual world interest rate of 4 percent. We set
σ equal to 2 so that the intertemporal elasticity of substitution is 0.5, which is consistent
13
Together with Canada, these countries make up the list of developed small open economies analyzed by
Neumeyer and Perri (2005). We exclude Canada from this list as it is a net oil exporter.
14
See Global Energy Statistical Yearbook, 2011.
15
The data sources are Eurostat (Netherlands and Sweden), The Bureau of Resources and Energy Economics (Australia) and Ministry of Economic Development (New Zealand).
9
with the general practice in real business cycle literature. The parameter governing the
wage elasticity of labor, η, is set to 1, which falls inside the range of Frisch elasticity values
suggested by Chetty et al. (2011) as consistent with macroeconomic models.
The parameter governing the weight of tradable goods in total consumption, γ, is taken
to be 0.47, considering the average share of tradable goods in final consumption for Australia, Netherlands, New Zealand and Sweden in Kravis et al. (1982). The elasticity of
substitution between tradable and non-tradable goods, κ, is set at 0.44 in line with the
estimate of Stockman and Tesar (1995). More recent papers that incorporate both tradable
and non-tradable goods sector, such as Benigno and Thoenissen (2008), also use similar
values for these parameters.16
The labor share in the tradable sector, αT , is taken as 0.64, while capital share in the
non-tradable sector is set to zero, implying αN = 1. The depreciation rate of capital, δ, is
set at 0.0279 to match the investment-output ratio of 0.22, which is the 1980-2010 average
for the economies that we consider. The capital adjustment cost parameter, ΦK = 0.015,
is calibrated to match 3.6, which is the average value of volatility of investment in terms of
output in the data for these four countries (Table 2).
The cost of adjusting foreign bonds, ΦB , is taken as 0.01 in the baseline calibration.
As ΦB also scales the degree of financial market integration, our baseline calibration corresponds to a financially open small open economy. When analyzing the alternative scenario
associated with financial autarky, we set this parameter large enough such that there is no
foreign bond trade in equilibrium. Capital and bond adjustment costs are both zero at the
steady-state. The steady-state ratio of foreign bonds to GDP is -0.25, implying that our
small open economy is a net debtor at the steady-state with a small trade balance-to-GDP
ratio of around 0.51 percent, consistent with Mendoza (1995).
We calibrate the process for real oil prices in line with Plante and Traum (2012), who estimate a stochastic volatility model for real oil prices using monthly U.S. data. Accordingly,
we set the persistence of real oil prices equal to 0.90 and the average standard deviation of
real oil price shocks to 0.06.17 Finally, we set the persistence of oil price volatility shocks to
0.78 and the standard deviation equal to 0.21 again according to Plante and Traum (2012)
estimates.18 The persistence of TFP shocks are set to 0.88, which is close to the values
commonly used in the international business cycle literature. The standard deviation of
tradable and non-tradable TFP shocks are set as 0.0095 and 0.007 to match the volatility
16
The range of values used in the literature for the elasticity of substitution between tradable and nontradable goods varies between 0.44 and 1.2. We carry out sensitivity analysis regarding this parameter.
17
Fitting an OLS to quarterly real oil prices for Australia, Netherlands, New Zealand and Sweden, we find
an average persistence parameter of 0.95 and an average standard deviation of 0.06.
18
Pfeifer et al.(2012) estimate a stochastic volatility model for monthly Chilean terms of trade and find the
persistence within two months to be around 0.87 and the average standard deviation to be around 0.033 for
level shocks. As for volatility shocks, they report a persistence parameter of 0.93 and a standard deviation
of 0.27.
10
of GDP, while keeping the relative size of tradable and non-tradable shocks in line with
Mendoza (1995).
2.5
Model Solution
We solve the model using a third-order approximation to the policy and transition functions
around the deterministic steady-state. In particular, Fernandez-Villaverde et al. (2011a)
show that a third-order approximation to the model is needed to analyze the direct effects
of exogenous changes in volatility while keeping the level of the exogenous process constant.
In our model, this corresponds to computing the effects of a shock to the standard deviation
of real oil price ζP,t+1 when the shock to the level of real oil price level is set to zero, i.e.
t+1 = 0 (See Equations (14) and (22).19 In a first-order approximation, time-varying
volatility has no direct or indirect effect due to certainty equivalence. In a second-order
approximation, where the solution becomes a function of the cross-product of volatility and
level shocks, i.e. t+1 ζP,t+1 , volatility shocks have an indirect effect that works through level
shocks. Volatility shocks enter the solution directly only in a third-order approximation.20
Due to the non-linear structure of our model, we rely on Monte Carlo methods to
compute business cycle moments and impulse responses. Because simulations based on
higher order solutions tend to explode, we follow the pruning approach suggested by Kim
et al. (2008), which rules out spurious terms in the higher-order solution when obtaining the
simulations.21 This is in line with the recent literature that introduces stochastic volatility
in DSGE models including Fernandez-Villaverde et al. (2011a).
To calculate the business cycle moments, we simulate the model for 2000 periods and
use the last 1000 observations to calculate the second moments of the model variables. We
repeat this exercise 1000 times drawing a different sequence of shocks each time, and report
the average across different simulations. To compute impulse responses, we first run the
baseline simulations using a sufficiently long sample path of the state vector, then compute
a second simulation by adding x standard deviation to the nth shock at period t. The
percentage difference between the two simulations gives the impulse response to the nth
shock. We repeat this exercise 10000 times and report the average impulse response across
different simulations.22
19
Andreasen (2012), Born and Pfeifer (2011), Pfeiffer et al (2012) and Mumtaz and Theodoridis (2012) are
among recent papers that follow the methodology used in Fernandez-Villaverde et al. (2011a) in analyzing
the effects of stochastic volatility in various DSGE models.
20
Benigno et al.(2010) propose an alternative approach, where volatility shocks have a separate and distinct
effect in a second-order accurate solution provided that exogenous state variables follow conditionally linear
processes with a time-varying conditional standard deviation or a conditional variance.
21
For the third-order solution and the pruning algorithm, we use the matlab codes
that accompany Andreasen (2012),
which are published in the author’s website at
https://sites.google.com/site/mandreasendk/home-1by
22
This corresponds to computing generalized impulse responses (Koop et al.,1996). Fernandez-Villaverde
11
3
Numerical Results
In this section, we first assess how our model performs in matching the business cycle moments for oil-importing developed small open economies. Then, we analyze the transmission
of shocks to the volatility of oil prices highlighting the role of financial market openness and
precautionary savings. This section also investigates how key model parameters, such as
the degree of risk aversion, elasticity of substitution between oil and other inputs in the
production process and the degree of substitutability/complementarity between tradable
and non-tradable goods, affect the channels of transmission. Third, we compare the effects
of exogenous changes in level and volatility of real oil prices and discuss the implications
when both shocks hit at the same time. Finally, we consider an extension to the baseline
model where we introduce, in reduced form, world demand for the goods produced by the
small open economy to shed light on how the responses to oil price shocks would be affected
if they were accompanied by changes in world demand.
3.1
Business Cycle Moments
Table 2 reports average business cycle moments calculated using the data for Australia,
Netherlands, New Zealand and Sweden and the moments generated by our model. Column
2 displays the simulated moments of our model under the benchmark calibration in the
presence of level shocks in sectoral TFP’s and both level and volatility shocks in real oil
prices. In overall, the model does a fair job in matching the key features of the data.
23
Similar to other small open economy models such as Fernandez-Villaverde et al. (2011a),
our model generates lower volatility of consumption relative to GDP and lower volatility
of net exports to GDP ratio compared to the data.24 Our model also predicts a smaller
relative volatility of hours worked, which is common across a wide range of RBC models.25
Consumption, investment, real exchange rate and hours are procyclical in the model as in
the data, as shown by panel c. On the other hand, net exports are countercyclical as in the
data, which is a feature that small open economy models like Mendoza (1995) generally fail
to match.
et al. (2011a) obtain impulse responses by calculating the ergodic mean of the variables first and subtracting
this from the series obtained by perturbing the ergodic mean by an x standard deviation to the nth shock.
They find no significant difference between their approach and the generalized impulse response approach.
We also followed their approach and obtained similar impulse responses.
23
The size of the TFP shocks and the capital adjustment cost parameter are calibrated so that the
volatilities of GDP and investment-to-GDP are close to the data counterparts.
24
Including real interest rate shocks, i.e. making the return on bonds stochastic, increases the relative
volatility of consumption to output as well as the volatility of net exports to GDP ratio as shown in Appendix
B. Given the size of our model, including one more state variable to allow for interest rate shocks increases
the computation time significantly. Hence, we abstract from interest rate shocks in the baseline simulation
and leave it to the appendix.
25
For example, see King and Rebelo (1999), Neumeyer and Perri (2005) and Corsetti et al (2008).
12
3.2
Transmission of Oil Price Volatility Shocks
This section presents the key channels in the transmission of oil price shocks. In particular,
we assess the importance of international financial market structure and available savings
technologies as well as the degree of aversion and the substitutability of the oil with other
factors of production in determining the responses of our model economy to the changes in
the oil price volatility.
3.2.1
The International Financial Market Structure and Available Savings
Technology
A key feature of our model is that higher oil price volatility induces an increase in the
savings of the households due to a precautionary savings motive. While the strength of this
motive depends on factors such as the degree of risk aversion and the substitutability of oil,
the way these savings are transmitted through the economy is determined by the financial
market structure and the available saving technologies.
Figure 2 plots the model’s responses to a rise in oil price volatility for the case of financial
integration (FI) and financial autarky (FA).26 In the case of financial integration, agents
can trade an international risk-free bond at a fairly small cost scaled by Φ, whereas under
financial autarky agents can only save by accumulating domestic capital since there is no
trade in international bonds. The baseline calibration with financial integration is depicted
by the solid lines in Figure 2. In this setup, agents can save both by accumulating physical
capital and buying international bonds. Given that the marginal product of capital becomes
more risky following the increase in the volatility of oil prices, agents substitute away from
physical capital and channel their precautionary savings to international bonds instead. As
a result, physical investment decreases while foreign bond holdings and net exports increase.
GDP response indicates a slowdown in overall economic activity following an increase in
oil price uncertainty. Output in both sectors fall, but non-tradable sector faces a larger decline compared to the tradable sector. In our baseline calibration, oil enters the production
function of both sectors in a similar way, with the same share in total value added and same
the elasticity of substitution with other factors of production. The main difference between
the production technologies of the two sectors is the share of capital in output, which is
zero in the non-tradable sector. Hence, it might seem puzzling that tradable sector, which
utilizes more of the riskier factor of production, is affected less by a rise in oil price volatility.
This is mainly due to precautionary savings that are channeled into foreign bonds. The
economy has to run a trade surplus to afford higher foreign savings, which limits the extent
26
Impulse responses show the effects of a three standard deviation rise in oil price volatility normalized
by the standard deviation of the shock. See section 2.5 for a description of the way impulse responses are
calculated.
13
of the fall in tradable good production relative to non-tradable sector.27
As the non-tradable output shrinks, the relative price of non-tradable goods rises, which
can be seen by combining the equations describing the optimal choices for tradable and nontradable goods given in Equation (23) of Appendix A:
CtT
γ
=
1−γ
CtN
PtN
PtT
κ
.
(16)
Equation (16) links the changes in the real exchange rate, PtN /PtT , to changes in the
ratio of tradable to non-tradable consumption. Non-tradable consumption falls more than
tradable consumption in line with the differences in sectoral output responses following the
volatility shock. According to (16), the relative price of non-tradable goods should be higher
to support such an allocation.
Total hours decline as a response to falling real wages. The appreciation of the real
exchange rate contributes to the fall in total hours as well since the labor supply decision
also depends on the relative price of tradable goods with respect to the CPI, which is
proportional to the inverse of the real exchange rate (see Equation (24) in Appendix A).
This is because, for a given real wage, an appreciation (depreciation) decreases (increases)
the relative price of the foreign savings in terms of the tradable consumption unit, hence
requires lower (higher) hours of work to afford a given level of foreign savings.
The dashed lines in Figure 2 depict the model responses to higher oil price volatility
in the case of financial autarky, where the current account always has to be in balance.
Within the context of our model, this corresponds to letting the parameter that scales
portfolio adjustment costs, Φ, go to infinity. In this case, physical investment slightly
increases on impact before starting to decline. Moreover, it stays well-above the path it
would take under financial integration. This is due to the fact that increased demand for
precautionary savings following a rise in uncertainty can only be channeled into domestic
capital in the absence of an alternative asset. Thus, although the marginal product of
capital itself becomes more risky, investment does not decline as much as it would under
financial integration.
This is reflected as a smaller and slightly delayed contraction in GDP, which is evenly
distributed across both sectors, unlike in the previous case where non-tradable sector output
was affected more than tradable sector output due to the desire to acquire foreign savings
by running a trade surplus. Accordingly, the rise in the relative price of non-tradable goods
is more limited compared to the case of financial integration. Consistent with the path of
investment and output, firms’ demand for labor and oil are also higher in the case of financial
autarky following a rise in oil price volatility. The fact that savings are channeled to physical
27
Accordingly, the demand for labor and oil by tradable sector firms falls to a lesser extent. Detailed
impulse responses that include sectoral breakdown of factors of production are available upon request.
14
capital in the case of financial autarky also limits the decline in the overall consumption,
as it implies a higher level of output compared to the case of financial openness.
3.2.2
The Role of Risk Aversion
In order to understand how the precautionary savings motive affects the transmission of
the oil price volatility shocks, we assess the model responses under the case of financial
integration for different degrees of risk aversion. The solid lines in the top row of Figure 3
correspond to σ = 2, i.e. the baseline calibration. Higher degrees of risk aversion, σ = 5 and
σ = 10, are associated with larger declines in the investment in physical capital, GDP and
consumption, although the differences are quantitatively small as also reported by Benigno
et al. (2011). Finally, higher degree of risk aversion induces higher precautionary savings
demand, which is reflected as higher level of net foreign asset holdings.
3.2.3
The Role of Substitutability Between Oil and Non-Oil Factors of Production
The middle row of Figure 3 shows the model’s responses to higher oil price volatility for
different values of the elasticity of substitution between oil and non-oil inputs in tradable
sector production. We compare the impulse responses obtained under the baseline calibration θ = 0.23, with those obtained with higher degrees of substitutability, i.e. θ = 0.40,
the value used by Bodenstein et al. (2011) for the U.S. economy, and θ = 0.90, the highest
value we consider for this exercise.28 A higher degree of elasticity of substitution is associated with a milder responses in output, consumption and investment. In addition, the
demand for foreign savings shows a more moderate increase in case of a higher elasticity of
substitution, reflecting a weaker degree of precautionary saving motive, as it is relatively
more easy for the households to substitute away from the input that has become more risky
with higher volatility in oil prices.
3.2.4
The Role of Substitutability Between Tradable and Non-tradable Goods
We finally analyze how the degree of substitutability/complementarity between tradable
and non-tradable goods matter for the transmission of the oil price volatility shocks. For
this exercise, keeping all other parameters at their baseline values, we consider three values
for κ. Namely, we take κ = 0.44 in our baseline scenario as in Stockman and Tesar (1995),
and two alternative values, i.e. κ = 0.74 and κ = 1.2, which correspond to the estimate by
Mendoza (1992) and the upper bound of the estimate by Stockman and Tesar (1995).
28
We do not report the results for values of θ > 1 because we do not think it is reasonable to assume that
firms can easily substitute oil with other factors of production.
15
Under our baseline calibration, the elasticity of substitution across tradable and nontradable goods does not matter for the transmission of oil price volatility shocks. This is due
to the fact that oil enters the production of tradable and non-tradable goods with the same
weight and the same elasticity parameter, i.e. a1 = a2 and θ = φ. Thus, to analyze the role
of κ in the transmission of volatility shocks, we differentiate the weight of oil in tradable and
non-tradable goods production, i.e. set a2 = 0.99 while keeping a1 = 0.94, which implies a
larger dependence on oil in the production of tradable goods. Impulse responses depicted in
the bottom row of Figure 3 show the effects of changing κ under this calibration. Because
the non-tradable sector does not require oil as much the tradable sector, oil price shocks
affect the non-tradable sector less adversely. Hence in this case, higher substitutability
between tradable and non-tradable goods leads to a smaller contraction in the non-tradable
sector compared to the tradable sector. This is reflected as a smaller decline in total output
and consumption.
3.3
Comparing Shocks to the Level and Volatility of Oil Prices
In this section, we describe how the economy responds to changes in the level of real oil
prices keeping the volatility of oil price shocks constant. We compare the implications of
level and volatility shocks. We also consider joint shocks to the level and volatility of oil
prices and analyze how time-varying volatility affects the adjustment of the small open
economy to higher oil prices.
We know that some episodes are associated with a rapid surge in uncertainty regarding
the future path of oil prices even if the increase in current oil prices remains limited. Accordingly, we find it useful to allow for larger shocks to volatility in computing the impulse
responses, i.e. x standard deviation shock to volatility and y standard deviation to level,
where x¿y. To have an idea about how to scale volatility shocks relative to level shocks,
we calculate changes in the level and volatility of oil prices with respect to their respective
last-five-year-averages and divide these by their respective standard deviations calculated
for the same five-year period. We observe that there are a few years where the volatility of
oil prices increased considerably more than the level of oil prices. A case in point is the year
2007, in which the increase in the volatility of oil prices was almost three times higher than
the increase in the level. In line with this example, Figure 4 plots the impulse responses to a
1 standard deviation level shock along with the impulse responses to a 3 standard deviation
volatility shock under the baseline calibration with small portfolio adjustment costs.
The solid lines in Figure 4 show the response of the economy to a one standard deviation
rise in real oil prices in the baseline calibration with small portfolio adjustment costs.
Following a rise in real oil prices, investment plummets as the marginal cost of capital rises.
Firms’ demand for oil declines, which also contributes to the fall in investment as oil and
16
capital are complements in production. Hours worked and total output also fall but to a
lesser extent compared to the fall in investment and oil input.
Under our baseline calibration, the economy gives a current account surplus in response
to a rise in real oil prices. This is mainly because of the relatively large fall in investment
following an oil shock. As the fall in tradable output remains much more limited, this leads
to a rise in foreign savings (see Equation (8)). Oil consumption declines as a result of higher
oil prices, leading to a fall in the value of oil imports and contributing to the improvement
in the current account. With regards to the sectoral effects of oil price shocks, both tradable
and non-tradable sector output shrink as imported oil enters both production functions in
a similar way. The fall in the non-tradable sector output is relatively larger, leading to a
rise in the relative price of non-tradable goods following a rise in real oil prices.29
Comparing the solid lines with the dashed lines show that an exogenous rise in the level
and volatility of oil prices have very similar qualitative implications although they affect
the economy through different channels as explained above. To summarize, the dominant
channel in the former is the cost channel - marginal cost of capital rises with higher oil prices,
leading to a fall in investment and output. On the other hand, the determining factor in the
latter is the interaction between risk and precautionary savings. Capital becomes more risky
and agents want to increase precautionary savings to hedge against this risk. Provided that
there is an alternative asset to direct savings, investment falls, leading to a fall in output.
Quantitatively, the distinct effect of volatility shocks are much smaller compared to that
of level shocks. This is because the distinct effect of volatility shocks only appear at a thirdorder approximation whereas level shocks have a first-order effect. However, this does not
mean that oil price volatility shocks are unimportant. The dotted lines in Figure 4 show
the impulse responses to a simultaneous shock to the level and volatility of oil prices, where
shocks are scaled as before.30 This joint shock has a much larger effect compared to the
combined effect of separate shocks to level and volatility, which shows the importance of the
interaction between the level and volatility of oil prices in affecting the business cycles. The
initial responses of investment, output and consumption to a rise in oil prices are almost
doubled when there is simultaneous rise in the volatility of oil prices. What is more, the
difference between the solid and dotted lines are much larger than what can be accounted
by the dashed lines. That is, the quantitative effects of volatility shocks become much more
29
Comparing the impulse responses to an oil price level shock under financial integration (baseline calibration) with that under financial autarky shows that the degree of financial market openness does not matter
as much as it does for the transmission of volatility shocks. The figure that shows this comparison is omitted
from the main text to save space but it is available from the authors upon request.
30
In the data, the level and volatility of real price of oil have a tendency to move together. The correlation
coefficient between the level and volatility of the real price of oil, as measured by 90-day moving averages,
is 68 percent. In the terminology used by commodity and energy pricing model, this case corresponds to
so-called inverse leverage effect. See Eydeland and Wolyniec (2003) and Geman (2005) for a more detailed
analysis of inverse leverage effect.
17
important when they are considered in conjunction with level shocks. This is because the
cross-product of level and volatility shocks appear in the policy function in the high-order
solution to the model.
3.4
Incorporating World Demand Shocks
Recent research emphasizes that sources of fluctuations in oil prices are important in determining how an open economy responds to a rise in oil prices. If the rise in oil prices are
associated with strong world demand, possible adverse effects of high oil prices on economic
activity might be contained due to higher demand for exports coming from the rest of the
world.31
Figure 5 plots real price of oil and the world GDP in annual percentage changes. The
figure suggests that the rise in real oil prices in 2001-2007 period has been accompanied by
strong world GDP, as also supported by the empirical evidence provided in Kilian (2009).
This observation motivates us to explore how the transmission of oil price shocks in our
model would be affected by the comovement between the oil prices and world aggregate
demand.
Hence, it is natural to ask how the transmission of exogenous shocks to oil prices would
change if we consider the possibility that the rise in oil prices are driven by buoyant economic
activity in the rest of the world. Our model setup is not the most natural one to answer this
question, as we do not specify the rest of the world explicitly following the long tradition in
the literature that includes Mendoza (1995) and Fernandez-Villaverde et al. (2011a) among
many. We could specify the rest of the world explicitly as in Gali and Monacelli (2005) or De
Paoli (2009) and make the oil price endogenous in the model to analyze the effect of higher
world demand on oil prices and on the net exports of the small open economy. However,
this would require us to specify the sources of fluctuations in oil price volatility, which is
beyond the scope of the paper. Nevertheless, we could consider a simple foreign block by
allowing the demand for small open economy’s tradable goods to be directly affected by
foreign output, which is given by an exogenous shock process. We then allow for joint
shocks to foreign output and real oil prices to proxy demand and supply driven rises in real
oil prices.32
The demand for the tradable goods produced by the small open economy, Ytd,T , could
31
See Bodenstein et al. (2011) and Unalmis et al. (2012) for detailed analyses on the transmission of
shocks that affect the world demand and supply of oil in the context of large and small open economies,
respectively.
32
We thank one of the anonymous referees for the suggestion.
18
then be expressed as:
Ytd,T
= CtT + CtT,F ,
T −κ
Pt
= γ
(Ct + Yt∗ ) ,
Pt
(17)
where CtT,F denotes the foreign demand for domestic tradable goods.
The world output, Yt∗ , follows an exogenous process with:
∗
log Yt+1
= ρY log Yt∗ + µY Yt+1 ,
(18)
where Yt is independently and identically distributed shock with N (0, 1) to the level world
economic activity. ρY ∈ (0, 1) and µ2Y represent the persistence and the variance of log Y ∗ .33
The foreign demand for exports, in turn, enters the tradable market clearing condition
through net exports in the following way:
Bt +
ΦB
(Bt − B̄)2 = Rt Bt−1 + N X t
2
where
N Xt = CtT,F + Y T − CtT − It −
ΦK
PO
(Kt+1 − Kt )2 − tT (OtT + OtN )
2
Pt
(19)
(20)
Here, the difference with respect to the baseline model is the presence of CtT,F in Equation (20).
Figure 6 compares responses to an increase in oil prices under two different scenarios
concerning the world aggregate demand.34 The solid lines in the figure represent responses
to an exogenous real oil price shock, while the dashed (dotted) lines show the case of a
simultaneous increase (decrease) in world output Y ∗ . The solid lines lie in between the
dashed and dotted lines as expected. Under our calibration, the response of the economy
to a joint shock does not differ much from the response to a separate oil price shock. That
is, a rise in oil price level leads to a fall in consumption, investment and output even when
it is accompanied by a simultaneous increase in world output. The instantaneous fall in
consumption in response to 6 percent rise in real oil prices is around 0.5 percent, while it is
Note that this formulation implicitly assumes P ∗ = P , which might only hold in a real model where
purchasing power parity holds. Still, we think that this simple exercise is useful as it constitutes a step
towards understanding how the transmission to an oil price shock would change if it was accompanied by a
simultaneous increase or decrease in world demand.
34
Because the standard deviation of world output is much smaller than the standard deviation of real
oil prices, we consider 2 standard deviation positive and negative shocks to world output together with a 1
standard deviation shock to real oil price to have a clear comparison. Based on the quarterly GDP series for
the OECD total between 1974:Q3 and 2012:Q1, we set the standard deviation of world output shocks equal
to 0.01 and the persistence parameter ρY to 0.90 using the estimates from an AR(1) regression to the series.
33
19
around 0.4 percent when world output also increases by around 2 percent.35 If we interpret
the dashed and dotted lines as responses to a demand- and supply-driven oil price increases
respectively, the model implies that the initial fall in consumption following a rise in oil
prices is about 40 percent smaller when the shock is demand-driven as opposed to supplydriven. Investment, output (mostly tradable sector output) and net exports are similarly
affected by the presence of world output shocks.
Finally, we analyze how a small open economy responds to the changes in oil price uncertainty observed contemporaneously with changes in the level of global economic activity.
Such a case can arguably be viewed as a resemblance to the recent global outlook, where
changes in the global economic outlook are observed jointly with changes in the uncertainty
regarding the path of commodity prices. Figure 7 presents the results of this exercise. The
solid lines present the responses to higher oil price volatility shock accompanied with no
change in global economic activity, while the dashed (dotted) lines depict the case of a
simultaneous increase (decrease) in global activity. We find that the implications of an increase in oil price volatility might be quite different depending on whether it is accompanied
by an increase or a decrease in global economic activity. When higher oil price volatility is
observed simultaneously with lower global economic activity, we observe a sizable decline
in output, consumption and investment. In contrast, the case of a simultaneous increase in
global economic activity is associated with higher investment and consumption, as well as
a smaller decline in output on impact, followed later by an increase.
3.4.1
How do oil price shocks compare with TFP shocks?
Given that TFP and oil enter the production process similarly, a particular question that
comes to mind is how the implications of oil price shocks would compare with those of
TFP shocks. We are particularly interested in the comparing the transmission of stochastic
volatility shocks to oil prices and TFP processes. For that purpose, we change the stochastic
processes for tradable and non-tradable sector TFP in a way to allow for time-varying
stochastic volatility:
log Ait+1 = (1 − ρi ) log Āi + ρi log Ait + µi,t+1 εit+1 ,
i = T, N,
(21)
where εit is identically and independently distributed with N (0, 1). The variances of TFP
in tradable and non-tradable terms follow:
log µi,t+1 = (1 − ρµi )µ̄i + ρµi log µi,t + ζi,t+1 ,
35
i = T, N,
(22)
Kilian (2009) finds that the U.S. output rises when the rise in oil prices is caused by an increase in world
demand. Our model can generate this result if we allow for a bigger shock to world output than to real oil
price.
20
where ζi,t is identically and independently distributed with N (0, ψi2 ). Finally, 0 ≤ ρµi < 1
governs the persistence of µi,t .
Increases in the conditional volatility of TFP shocks have qualitatively similar effects
as an increase in the conditional volatility of oil price shocks in the sense that they depress
consumption, investment, output and lead to higher net foreign asset accumulation (Figure 7). This is because volatility shocks mainly work through the precautionary savings
channel, which stems from the output uncertainty caused by these shocks. In other words,
all volatility shocks, whether sector specific or not, increase total uncertainty in the economy, hence have similar effects.36 In this setup, the main difference between oil and TFP
volatility shocks are that the former yields hump-shaped responses, whereas the latter has
its maximum impact in the initial period.37 On the other hand, oil price shocks have much
smaller effects compared to TFP shocks, stemming from the low weight of oil in production in our benchmark calibration. For a higher weight of oil in production, responses to
the volatility of oil prices become larger in magnitude than those of the volatility of TFP
shocks.38
4
Conclusion
In this paper, we present a dynamic general equilibrium model for an oil-importing small
open economy to analyze the transmission of oil price volatility shocks. Our analysis shows
that whether domestic households have access to international bond markets plays an important role in determining the response of the economy to higher oil price uncertainty.
While investment and economic activity can increase in response to higher volatility in the
case of financial autarky, where households can use only capital for precautionary savings,
the availability of internationally traded bonds as an extra asset can overturn this result,
making the implications of the model more in line with the data.
Our results also show that the decline in consumption and investment in response to
an increase in oil prices would almost be doubled if there is a significant rise in uncertainty
at the same time. In other words, the presence of time-varying volatility in oil prices can
significantly amplify the responses of an oil-importing small open economy to higher oil
prices.
36
In our model, adverse shocks to sector-specific TFP levels and oil prices have similar effects as they
affect the marginal product of capital in similar ways.
37
Note that in this version of the model with stochastic volatility in sectoral TFP processes, the impulse
responses to a rise in oil price volatility seem somewhat different than those obtained from the baseline
model with constant TFP variances. Impulse responses to oil volatility shocks are not hump-shaped in the
baseline model. Given that we calculate impulse responses using a highly non-linear solution, it is normal
to have a different path of adjustment following a rise in oil price volatility when the stochastic processes
that drive other shocks change.
38
Results with a higher weight of oil in production are available upon request.
21
We also assess how the transmission of the rise in level and volatility of oil prices matters
depending on whether these variations are observed contemporaneously with buoyant economic activity in the rest of the world. Using the model extended to incorporate exogenous
aggregate world demand, we find that a rise in oil prices bring about approximately 40 percent smaller decline in consumption and investment in the case of simultaneous increase in
aggregate world demand than the case of simultaneous decline in aggregate world demand.
Allowing for a simultaneous change in world output matters even more for the transmission
of volatility shocks, as it overturns the fall in investment and consumption, restricting the
fall in output in response to a rise in oil price uncertainty.
Finally, we find that an increase in the volatility of sectoral TFP processes affects the
small open economy in a similar way to an increase in the volatility of oil prices. This
is because increased uncertainty about productivity and oil prices work through the same
channels by making investment in physical capital more risky and inducing precautionary
savings.
5
Appendices
5.1
Appendix A: Equilibrium Conditions
For a given level of final good consumption Ct and prices, the optimal choices of CtT , CtN , CtQ
and CtO yield the following set of static efficiency conditions
CtT
CtQ
T −κ
P
= γ Ptt
Ct ,
−ξ
PQ
= ν PtT
CtT ,
t
CtN = (1 − γ)
CtO = (1 − ν)
PtN
Pt
−κ
PtO
PtT
−ξ
Ct ,
CtT .
(23)
where the appropriate consumption price index and tradable good price index are given
respectively by (4) and (5).
Given prices, the optimal choice of Ct , ht , Kt+1 and Bt satisfy this set of dynamic first
order conditions
h1+η
Ct − t
1+η
PtT
wt = hηt ,
Pt
(24)
!−σ
1 + ΦK (Kt+1 − Kt )
(
= βEt
h1+η
Ct+1 − t+1
1+η
!−σ
)
T Pt Pt+1
K
(1 − δ + rt+1 )
1 + Φ (Kt+2 − Kt+1 ) ,
Pt+1 PtT
(25)
22
and
h1+η
Ct − t
1+η
!−σ
(
1 + ΦB (Bt − B̄) = βEt
h1+η
Ct+1 − t+1
1+η
!−σ
T
Pt Pt+1
Rt+1
Pt+1 PtT
)
.
(26)
Equation (24) states that the marginal rate of substitution between final consumption good
and leisure is equal to real wages. Note that, since we assume free labor mobility across
sectors, wages in both sectors are equivalent, making households indifferent in choosing
which sector to work. On the other hand, Equation (25) equates marginal cost of foregone
consumption today by saving one unit of capital to expected marginal benefit of consuming
that tomorrow. Similarly, the left hand side of (26) is the marginal cost of consuming less
today by saving one unit of international bond while the right hand side is the expected
marginal benefit of consuming more one period later.
Taking prices as given, firms operating in both sectors equate marginal product of labor
to real wages, marginal product of capital to the rental rate of capital and marginal product
of oil to its price. In particular, tradable good producing firms’ optimality conditions are
given respectively by
θ−1
T
T θ−1
θ
(a1 YtT )1/θ αT ATt (KtT )1−α
(hTt )α θ −1 = wt ,
(27)
θ−1
T
T θ−1
θ
(a1 YtT )1/θ (1 − αT ) ATt (hTt )α
(KtT )(1−α ) θ −1 = rt
(28)
and
(1 − a1 )
YtT
=
OtT
PtT
PtO
−θ
.
(29)
Similar conditions for nontradable good producing firms are given by
(a2 YtN )1/φ αN
N 1−αN
AN
t (Kt )
φ−1
φ
−1
αN φ−1
φ
(hN
t )
= wt
PtT
,
PtN
φ−1
φ−1
PtT
φ
N αN
N (1−αN ) φ −1
(a2 YtN )1/φ (1 − αN ) AN
(h
)
(K
)
=
r
t N
t
t
t
Pt
and
YN
(1 − a2 ) tN =
Ot
PtN
PtO
(30)
(31)
−φ
.
(32)
O
2
∞
Given the sequence of exogenous state variables {ATt , AN
t , Rt , Pt /Pt , µP,t }t=0 described
respectively by (21), (13), (14) and (22), a stationary equilibrium is a sequence of endogenous variables {Ct , CtT , CtN , CtQ , CtO , ht , hTt , hN
t ,
T
N
O
∞
Kt , KtT , KtN , It , Bt , OtT , OtN , YtT , YtN }∞
t=0 and prices {Pt , Pt , Pt , Pt , rt , wt }t=0 such that
23
1. given prices, households optimize by (23) and (24)-(26),
2. given prices, firms optimize by (27)-(32),
3. good prices satisfy (4) and (5), and
4. market clearing conditions
CtT +It +Bt +
CtN = YtN ,
(33)
Kt = KtT + KtN ,
(34)
ht = hTt + hN
t ,
(35)
ΦK
ΦB
PO
(Kt+1 −Kt )2 +
(Bt −Bt−1 )2 = Rt Bt−1 +YtT − tT (OtT +OtN ) (36)
2
2
Pt
are satisfied.
Note that Equation (36), which describes the dynamics of the current account balance,
is derived from combining household budget constraint (6), profit functions of firms (11),
and the non-tradable good market clearing condition in Equation (33).
5.2
Appendix B: Interest Rate Shocks
In the baseline calibration, we take the world interest rate as constant. Here we specify a
stochastic process for the world interest rate as follows:
log Rt+1 = (1 − ρR ) log R̄ + ρR log Rt + µR εR
t+1 ,
(37)
2
where 0 ≤ ρR < 1. εR
t is i.i.d with N (0, 1), and the variance of log Rt is given as µR .
For the numerical analysis, we set the persistence of the international rate, ρR , equal
to 0.88 and the standard deviation of the international rate, µR , equal to 0.003, which are
close to the values used by Neumeyer and Perri (2005) who set ρR = 0.88 and µR = 0.006.
The rest of the parameters, except the capital adjustment cost parameter, are calibrated as
in Table 1. Capital adjustment costs are set such that the volatility of investment relative
to GDP is around 3.6 as in the data.39
The business cycle moments obtained from the model with world interest rate shocks
are reported in the third column of Table 3. The volatility of the net exports-to-GDP
ratio is almost tripled with the inclusion of interest rate shocks, coming close to the data
counterpart. On the other hand, the presence of world interest rate shocks makes net exports
to GDP acyclical rather than countercyclical as in the data and in the baseline model.
39
K
Φ
This requires setting ΦK = 0.15 in the case with interest rate shocks. In the baseline calibration with
= 0.015, the volatility of investment relative to GDP is 8.8.
24
Volatility of consumption becomes higher and the cross-correlation between investment and
GDP falls, which are more in line with the data.40
Figure (9) shows that our model can generate a fall in investment, employment, consumption and output following a rise in international interest rates. Investment displays
the largest fall, which is followed by the fall in consumption and output. Faced with an
increase in the return on the riskless asset, agents increase their foreign savings by running
a current account surplus, hence tradable output and net exports rise. These patterns are
broadly in line with the patterns documented in the empirical literature (Uribe and Yue,
2006) and also in line with other small open economy models such as Neumeyer and Perri
(2005).
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28
Table 1: Baseline Calibration
Parameter
β
σ
η
κ
ξ
γ
1−ν
1 − a1
1 − a2
αT
αN
θ
φ
δ
ΦK
ΦB
B/Y
ρT = ρN
µ̄T
µ̄N
ρP
µ̄P
ρµP
ψP
Description
Constant discount factor
Coefficient of constant relative risk aversion
Inverse of the Frisch elasticity of labor supply
Elasticity of substitution between tradables and nontradables
Elasticity of substitution between oil and non-oil
goods in tradable consumption
Weight on tradable goods in total consumption
Weight on oil in tradable goods consumption
Weight on oil in the production of tradable goods
Weight on oil in the production of nontradable goods
Labor share in tradable goods production
Labor share in non-tradable goods production
Elasticity of substitution between oil and non-oil
goods in tradable production
Elasticity of substitution between oil and non-oil
goods in non-tradable production
Depreciation rate of capital
Capital adjustment cost parameter
Portfolio adjustment cost parameter
Foreign bonds as a ratio of GDP
Persistence of trad. and non-trad. TFP shocks
Standard deviation of tradable TFP shocks
Standard deviation of non-tradable TFP
Persistence of real oil price shocks
Standard deviation of real oil price shocks
Persistence of oil price volatility shocks
Standard deviation of oil price volatility shocks
29
Baseline values
0.9975
2
1
0.44
0.23
0.47
0.0986
0.06
0.06
0.64
1
0.23
0.23
0.0279
0.015
0.01, 1000
-0.25
0.88
0.0095
0.007
0.90
0.06
0.78
0.21
Table 2: Business Cycle Moments: Data vs. Model
Panel (a)
GDP
Net Exports/GDP
Panel (b)
Consumption
Investment
Hours
Real Exchange Rate
Panel (c)
Consumption
Investment
Hours
Net Exports
Real Exchange Rate
Absolute Volatilites in Terms of
Standard Deviations
(1)
(2)
Average of 4
Baseline Model
Countries in Data
1.37
1.59
0.97
0.27
Standard Deviations Relative to GDP
1.29
0.65
3.57
3.75
1.87
0.52
0.64
0.91
Cross Correlations with GDP
0.63
0.74
0.73
0.90
0.81
0.83
-0.29
-0.39
0.58
0.47
Notes: Column (1) reports the average empirical moments for Australia, the Netherlands, New Zealand
and Sweden calculated by Neumeyer and Perri (2005). Column (2) reports the moments obtained by
simulating the model under the baseline calibration. Both empirical and model-generated series are in
natural logarithms and HP-filtered except net exports, which is not log-transformed. Net exports are defined
as net exports divided by GDP and are expressed in tradable goods units.
30
Table 3: Business Cycle Moments: Data vs. Model
Absolute Volatilites in Terms of Standard Deviations
(1)
(2)
(3)
Panel (a)
GDP
Net Exports/GDP
Panel (b)
Consumption
Investment
Hours
Real Exchange Rate
Panel (c)
Consumption
Investment
Hours
Net Exports
Real Exchange Rate
Average of 4
Countries in Data
1.37
0.97
1.29
3.57
1.87
0.64
0.63
0.73
0.81
-0.29
0.58
Baseline Model
Model with World
Interest Rate Shocks
1.59
0.27
Standard Deviations Relative to GDP
0.65
3.75
0.52
0.91
Cross Correlations with GDP
0.74
0.90
0.83
-0.39
0.47
1.56
0.95
0.69
3.74
0.50
0.92
0.76
0.61
0.83
0.09
0.48
Notes: Column (1) reports the average empirical moments for Australia, the Netherlands, New Zealand
and Sweden calculated by Neumeyer and Perri (2005). Columns (2) and (3) report the moments obtained
by simulating the model under the baseline calibration and in the case with world interest rate shocks.
Capital adjustment costs are set such that the volatility of investment relative to GDP is around the data
counterpart. ΦK =0.015 in the baseline calibration and ΦK =0.15 in the case with interest rate shocks. Both
empirical and model-generated series are in natural logarithms and HP-filtered except net exports, which is
not log-transformed. Net exports are defined as net exports divided by GDP and are expressed in tradable
goods units.
31
Figure 1: Oil Price Level and Volatility in Data
150
100
50
0
1985
1990
1995
2000
2005
2010
2005
2010
2005
2010
(a) Mean
25
20
15
10
5
0
1985
1990
1995
2000
(b) Standard Deviation
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
1985
1990
1995
2000
(c) Coefficient of Variation
Notes: Weekly WTI prices. Data source is Bloomberg.
32
Figure 2: Impulse Response to a Three Std. Dev. Adverse Oil Price Volatility Shock: The
Role of Financial Integration
−3
1
x 10
Cons.
−3
Invest
x 10
−3
Output
0.01
1
0
0
0
−1
−0.01
−1
−1
−2
−0.02
−2
−2
FI
FA
0
−3
0
10
−3
10
x 10
−0.03
20
0
−3Oil
NFA
2
0
0
10
−3
1
x 10
20
x 10
20
Input
1
−3
T
Y
1
−1
−1
−4
−2
−2
−6
0
10
−4
x 10
20
0
10
10
0
20
−3
20
4
0.5
3
0
2
−0.5
1
0
10
10
20
N
Y
0
10
20
Oil price vol.
1
−1
Labor
x 10
Oil price
0
−5
0
NX/Y
0
−0.5
−3
x 10
−3
−2
10
20
x 10
20
0
RER
10
10
−3
5
0
0
0
0.5
−1
−3
0
5
−5
10
1
20
0
0
10
20
Notes: FI denotes the case of financial integration (baseline calibration), while FA corresponds to the case financial
autarky where there is no trade in international bond.Impulse responses are normalized by the standard deviation of
the oil price volatility shock.
33
Figure 3: Impulse Response to a Three S.D. Adverse Oil Price Volatility Shock: Sensitivity
Analysis
−3
0
x 10
Cons.
−3
Invest
0
−0.5
−3
Output
0
−1
−0.01
−1
x 10
x 10
−3
Labor
10
−1
5
−2
0
x 10
NFA
−1.5
σ=2
σ=5
σ=10
−2
−3
0
10
−3
0
x 10
−3
θ=0.23
θ=0.40
θ=0.90
0
10
−4
−4
x 10
−0.03
20
0
Cons.
−1
−2
−0.02
10
−3
x 10
20
−3
0
10
−3
Output
x 10
−5
Labor
−1
−1
5
−0.02
−2
−2
0
10
20
−3
−8
−0.01
−10
κ=0.44
κ=0.74 −0.015
κ=1.2
−0.02
20
0
−0.5
10
10
−3
Invest
0
0
x 10
20
−3
0
−3
Output
0
10
10
x 10
20
−5
10
−0.5
5
−1.5
−1
0
−2
0
−1.5
20
0
10
10
10
x 10
0
20
−5
x 10
0
20
NFA
10
−3
Labor
−1
20
0
−3
−0.01
−0.03
20
0
0
20
0
Cons.
10
−2.5
20
0
Invest
−0.005
0
10
0
−6
−12
−2
20
NFA
10
20
Notes: Obtained under the baseline calibration with ΦB = 0.01. σ denotes the coefficient of relative risk aversion.
θ denotes the elasticity of substitution between oil and non-oil inputs in tradable goods production. κ denotes the
elasticity of substitution between tradable and non-tradable goods consumption. Impulse responses are normalized
by the standard deviation of the oil price volatility shock.
34
Figure 4: Joint Shocks to the Level and Volatility of Oil Prices
Cons.
−3
Invest
0
0
0
−0.02
−0.005
−0.04
−0.01
0
10
−0.06
20
0
10
NFA
0
−0.01
0
10
−3
2
x 10
−4
−4
−6
−6
−8
−0.02
−2
−0.04
−4
−0.06
−6
−3
RER
3
x 10
0
10
−3
0
10
20
0
−2
0
−0.08
20
0
−3
Output
−2
Oil Input
0.02
0.01
20
x 10
−8
x 10
20
−8
YT
−0.005
0
NX/Y
10
−0.01
20
0
Oil price
0.8
0
1
0.04
0.4
−1
0
0.02
0.2
−1
0
10
20
0
0
10
10
20
Oil price vol.
0.08
0.6
20
20
Y
0.06
10
10
0
2
0
0
Labor
N
1
−2
x 10
20
0
0
10
20
Notes: Red solid line (black dashed line) represents impulse responses to a one (three) standard deviation shock to
the oil price level (volatility). Blue dotted dashed line shows impulse responses to a joint one standard deviation
level shock and a three standard deviation volatility shock.
35
Figure 5: Oil Prices and World Output Growth
80
2.5
Real Oil Price Index (2005=100), Annual % Change
OECD Output Growth, Annual % Change (right axis)
60
2.0
1.5
40
1.0
0.5
20
0.0
0
-0.5
-1.0
-20
-1.5
-40
-2.0
-2.5
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
-60
Notes: Oil price is the crude Oil (petroleum) price index (2005 = 100), simple average of three spot prices; Dated
Brent, West Texas, taken from the IMF database. World output is OECD total GDP at constant US dollars taken
from the OECD database.
36
Figure 6: Joint Shocks to the Level of Oil Prices and World Demand
−3
−2
x 10
Cons.
−3
Invest
0
−2
−3
−0.01
−2.5
−4
−0.02
−3
−5
−0.03
−3.5
−6
0
10
−0.04
20
0
10
NFA
20
−4
−0.01
0.01
−0.02
0
−0.03
−1.5
−3
Output
−1
x 10
Labor
−2
−3
0
10
−3
Oil Input
0.02
x 10
x 10
20
−4
T
0
10
−3
Y
0
−2
20
N
x 10
Y
0
10
−2
−2.5
−0.01
0
10
−3
2
x 10
−0.04
20
0
10
−3
RER
2
−4
−3
x 10
20
−3.5
0
NX/Y
10
20
−6
Oil price
Foreign output
0.08
0.04
1
1.5
0.06
0.02
0
1
0.04
0
−1
0.5
0.02
−0.02
−2
0
10
20
0
0
10
20
0
0
10
20
−0.04
20
0
10
20
Notes: Red solid line represents response to a one standard deviation shock to the oil price level. Black dashed line
(blue dotted dashed line) shows response to a one standard deviation shock to the oil price level accompanied by a
two standard deviations increase (decrease) in world output.
37
Figure 7: Joint Shocks to the Volatility of Oil Prices and World Demand
−4
5
x 10
−3
Cons.
5
x 10
−4
Invest
5
x 10
−4
Output
2
0
0
0
0
−5
−5
−5
−2
−10
0
10
−3
4
x 10
20
−10
0
10
−3Oil
NFA
2
2
x 10
20
−10
0
10
−4
Input
5
x 10
20
−4
x 10
0
10
−4
T
Y
5
1
0
0
0
−5
−5
Labor
20
N
x 10
Y
0
10
0
−2
−4
0
10
−4
5
x 10
20
−1
0
10
−4
RER
4
x 10
20
0
10
20
−2
0
10
10
20
−10
Oil price volatility
0
0
0
NX/Y
2
−5
−10
20
Foreign output
0.8
0.04
0.6
0.02
0.4
0
0.2
−0.02
0
0
10
20
−0.04
20
0
10
20
Notes: Red solid line represents response to a three standard deviation shock to the oil price volatility. Black
dashed line (blue dotted dashed line) shows response to a three standard deviation shock to the oil price volatility
accompanied by a two standard deviations increase (decrease) in world output.
38
Figure 8: Comparing Shocks to the Volatility of Oil Prices and TFP Processes
0
−3
x 10 Cons.
Invest
0
0
−0.005
−1
−3
x 10 Output
0
−0.5
−0.5
−1
−1.5
−1
0
10
−3
5
x 10
20
0
0
10
x 10
20
−4
RER
5
0
−2
0
10
20
−2
−3
x 10 Oil Input
0
10
−3
0
x 10
20
−1.5
0
10
20
N
YT
0
−1
−0.5
−2
−1
−3
x 10 Y
−2
−3
2
−0.01
NFA
0
−5
−3
x 10 Labor
0
10
20
−3
−4
x 10 NX/Y
10
20
−5
0
10
10
20
−1.5
0
Level shocks
0
0
0
20
4
0
2
0
10
20
20
Vol. shocks
1
−1
10
0
0
10
20
Notes: Red solid line, black dashed line and blue dotted dashed line represent responses to three standard
deviation shocks to the oil price volatility, tradable TFP and non-tradable TFP, respectively.
39
Figure 9: Response to World Interest Rate Shocks
Cons.
Invest
Output
Labor
0
5
0
−0.04
−0.2
0
−0.1
−0.06
−0.4
−5
−0.2
−0.08
−0.6
−10
−0.3
−0.1
−0.8
0
10
20
−15
0
NFA
10
20
−0.4
0
Oil Input
10
20
−0.12
Trad. Output
−0.1
0.4
0
30
−0.2
0.2
−0.2
20
−0.3
0
−0.4
10
−0.4
−0.2
−0.6
0
10
20
−0.5
0
RER
3
−0.1
2
−0.15
1
−0.2
0
0
10
20
NX/Output
−0.05
−0.25
10
20
−1
−0.4
0
10
20
Interest rate on bonds
1.5
1
0.5
0
10
20
0
0
40
10
10
20
Non−trad Output
40
0
0
20
−0.8
0
10
20
Central Bank of the Republic of Turkey
Recent Working Papers
The complete list of Working Paper series can be found at Bank’s website
(http://www.tcmb.gov.tr).
Consumer Tendency Survey Based Inflation Expectations
(Ece Oral Working Paper No. 13/08, February 2013)
A Literature Overview of the Central Bank’s Knowledge Transparency
(M. Haluk Güler Working Paper No. 13/07, February 2013)
The Turkish Approach to Capital Flow Volatility
(Yasin Akçelik, Erdem Başçı, Ergun Ermişoğlu, Arif Oduncu Working Paper No. 13/06, February 2013)
Market-Based Measurement of Expectations on Short-Term Rates in Turkey
(İbrahim Burak Kanlı Working Paper No. 13/05, February 2013)
Yurtiçi Tasarruflar ve Bireysel Emeklilik Sistemi: Türkiye’deki Uygulamaya İlişkin Bir Değerlendirme
(Özgür Özel, Cihan Yalçın Çalışma Tebliği No. 13/04, Şubat 2013)
Reserve Options Mechanism and FX Volatility
(Arif Oduncu, Yasin Akçelik, Ergun Ermişoğlu Working Paper No. 13/03, February 2013)
Stock Return Comovement and Systemic Risk in the Turkish Banking System
(Mahir Binici, Bülent Köksal, Cüneyt Orman Working Paper No. 13/02, February 2013)
Import Surveillance and Over Invoicing of Imports in Turkey
(Zelal Aktaş, Altan Aldan Working Paper No. 13/01, January 2013)
Nowcasting Turkish GDP Growth
(Hüseyin Çağrı Akkoyun, Mahmut Günay Working Paper No. 12/33, December 2012)
Rezerv Opsiyonu Mekanizması ve Optimal Rezerv Opsiyonu Katsayılarının Hesaplanması
(Doruk Küçüksaraç, Özgür Özel Çalışma Tebliği No. 12/32, Kasım 2012)
Finansal Krizlerin Belirleyicileri Olarak Hızlı Kredi Genişlemeleri ve Cari İşlemler Açığı
(Aytül Ganioğlu Çalışma Tebliği No. 12/31, Kasım 2012)
On the Sources and Consequences of Oil Price Shocks: The Role of Storage
(Deren Ünalmış, İbrahim Ünalmış, Derya Filiz Ünsal Working Paper No. 12/30, November 2012)
Mitigating Turkey's Trilemma Tradeoffs
(Yasin Akçelik, Orcan Çörtük, İbrahim M. Turhan Working Paper No. 12/29, October 2012)
Capital Regulation, Monetary Policyand Financial Stability
(Pierre-Richard Agénor, Koray Alper, L. Pereira da Silva Working Paper No. 12/28, October 2012)
Determinants of Precautionary Savings: Elasticity of Intertemporal Substitution vs. Risk Aversion
(Arif Oduncu Working Paper No. 12/27, August 2012)
An Analysis of Intraday Patterns and Liquidity on the Istanbul Stock Exchange
(Bülent Köksal Working Paper No. 12/26, August 2012)
Short Run Import Dynamics in Turkey
(Altan Aldan, İhsan Bozok, Mahmut Günay Working Paper No. 12/25, August 2012)