Download buffer vs. speculation: a review on the role of crude oil inventory

BUFFER vs. SPECULATION:
A REVIEW ON THE ROLE OF
CRUDE OIL INVENTORY
By the Brain Korea 21 PLUS Project (No. 21A20130012821)
Soohyeon Kim, Eunnyeong Heo
Energy, Environmental, and Engineering Economics
Seoul National University
40th IAEE International Conference, Singapore 2017
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2014 – 2016 Reality vs. Academia
600
120
500
100
400
80
300
60
200
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“A positive speculative demand
will shift the demand for aboveground oil inventories, causing ...
the level of inventories and the
real price of oil to increase on
impact.”
100
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-Kilian and Murphy (2010, 2014)
0
0
Jan-13
Jul-13
Jan-14
Jul-14
U.S. ending stocks excl. SPR (Mb)
Jan-15
Jul-15
Jan-16
WTI price (USD, 2005=100)
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Research Question
“Crude oil inventories,
buffer or speculation?”
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To Positive Demand Shock
(i.e., excess demand)
Speculation
Buffer
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To Positive Supply Shock
(i.e., oversupply)
Speculation
Buffer
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Literature Review
 Ample studies for inventories, but be biased to either buffer or
speculation.
Buffer
Teisberg (1981) ,Hubbard and Weiner (1986), Considine
(1997), Zhang et al. (2009), Cho and McDougall (1990), Chen
et al. (2006), Geman and Ohana (2009)
Kucher and Kurov (2012), Jaffe and Soligo (2002)
Speculation
Kilian and Murphy (2014), Kilian and Lee (2014),
Beidas-Strom and Pescatori (2014), Lin and Li (2015),
Chen et al. (2016), Juvenal and Petrella (2015),
Kaufmann and Ullman (2009), Kaufmann (2011)
 No study has been asking whether oil inventories are buffer or
speculative.
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Purpose
 To examine the role and behavior of crude oil inventories by
detecting whether oil inventories react to oil demand and supply
shocks as the buffer to the market or as the facilitation of
speculative trading.
 For two distinct periods (2003:M1–2008:M6 and 2009:M7–2016:M2),
sign-restricted structural vector autoregressive (SVAR) model is
applied.
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Settings
 To positive demand shock (i.e., excess demand)
• If inventories increase (+): speculative
• If inventories decrease (-): buffer
 To positive supply shock (i.e., oversupply)
• If inventories increase (+): buffer
• If inventories decrease (-): speculative
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Empirical Framework
 Structural vector autoregressive (SVAR) model with sign restrictions
to allow economic interpretation of idiosyncratic demand and supply shocks
A0yt = Ayt-1+Bεt,
εt~N(0, Σε)
yt=[productiont, economic activityt, oil inventoryt, oil pricet]’
εt=[supply shockt, demand shockt, storage shockt, residual shockt]
 Restrictions for identification
– Positive supply shock (i.e., oversupply ) decreases oil prices
– Positive demand shock (i.e., excess demand) increases oil price
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Empirical Framework
 Bayesian approach
•
Sign restriction algorithm follows Rubio-Ramirez et al. (2010)’s rejection method,
assuming a uniform Haar prior
•
For VAR parameters, Normal Inverted-Wishart posterior
 After estimating parameters of SVAR
1) Impulse response functions (IRFs)
•
To measure how the inventories react over time to demand and supply shocks
2) Forecast error variance decompositions (FEVDs)
•
To compare the relative contribution of each demand and supply shocks on oil
inventories in percentage unit
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Data
Period
P1 (2003:M1–2008:M6) P2 (2009:M7–2016:M2)
Supply
World crude oil production (EIA)
World economic activity
Demand
: Weighted average of industrial production in major oil consuming
countries (OECD)
1 Global
Inventory
: Global commercial crude oil inventory proxy (IEA, EIA, OECD)
2 U.S.
: U.S. crude oil inventory excluding SPR (EIA)
Price
WTI futures real prices (EIA)
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2003:M1-2008:M6
Demand shock
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Supply shock
9
6
6
3
3
IRF
0
0
1
5
10
-3
-6
FEVD
1
5
10
-3
SPEC(5.7)
BUFF(-1.4)
30%
-6
SPEC(-1.8)
BUFF(2.6)
24%
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2009:M7-2016:M2
Demand shock
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3
IRF
3
0
0
1
5
10
-3
-6
FEVD
Supply shock
6
1
5
10
-3
SPEC(0.4)
BUFF(-1.0)
19%
-6
SPEC(-4.4)
BUFF(4.1)
23%
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“Crude oil inventories have Janus’ two faces”
 Time-varying behavior from speculation to buffer
 Speculation vs. buffer depends on periods and market
conditions.
• [2003-2008] speculative behavior was dominant to demand shock,
• [2009-2016] supply shock strongly induced speculative and buffer
behaviors of inventories.
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Discussion
 This study have a contribution in examining both
speculative and buffer roles from a neutral perspective.
 The impact of oil inventories need to be determined with
prudence, considering its varying role with period and
market conditions.
 Expanding regional scope of inventories to non-OECD can
provide richer results.
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Reference
Beidas-Strom, S., and Pescatori, A. 2014. Oil Price Volatility and the Role of Speculation. IMF Working Paper No. 14/218.
Chen, Y., Yu, J., and Kelly, P. 2016. Does the China factor matter: What drives the surge of world crude oil prices? The Social Science Journal. 53(1): 122-133.
Cho, D. W., and McDougall, G. S. 1990. The supply of storage in energy futures markets. Journal of Futures Markets. 10(6): 611-621.
Considine, T. J. 1997. Inventories under joint production: An empirical analysis of petroleum refining. Review of Economics and Statistics. 79(3): 493-502.
Geman, H., and Ohana, S. 2009. Forward curves, scarcity and price volatility in oil and natural gas markets. Energy Economics. 31(4): 576-585.
Hubbard, R. G., and Weiner, R. J. 1986. Inventory optimization in the US petroleum industry: empirical analysis and implications for energy emergency policy. Management Science. 32(7): 773-790.
Jaffe, A. M., and Soligo, R. 2002. The role of inventories in oil market stability. The Quarterly Review of Economics and Finance. 42(2): 401-415.
Juvenal, L., and Petrella, I. 2015. Speculation in the oil market. Journal of Applied Econometrics: 30(4), 621-649.
Kilian, L., and Lee, T. K. 2014. Quantifying the speculative component in the real price of oil: The role of global oil inventories. Journal of International Money and Finance. 42: 71-87.
Kilian, L., and Murphy, D. P. 2014. The role of inventories and speculative trading in the global market for crude oil. Journal of Applied Econometrics. 29(3): 454-478.
Kucher, O., and Kurov, A. 2014. Business cycle, storage, and energy prices. Review of Financial Economics. 23(4): 217-226.
Lin, B., and Li, J. 2015. The determinants of endogenous oil price: Considering the influence from China. Emerging Markets Finance and Trade. 51(5): 1034-1050.
Teisberg, T. J. 1981. A dynamic programming model of the U.S. strategic petroleum reserve. The Bell Journal of Economics. 12(2): 526-546.
Zhang, J., Yedlapalli, P., and Lee, J. W. 2009. Thermodynamic analysis of hydrate-based pre-combustion capture of CO2. Chemical Engineering Science. 64(22): 4732-4736.
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Imposing sign restrictions on SVAR
 A white noise (et) in VAR is linear combination (B) of structural shocks (εt) in
SVAR
et=Bεt
 Sign restrictions (Q) are imposed ex-post on the IRFs of εt
 et=BQεt, such that QQ’=Q’Q=I
 Generate IRFs of εt by Cholesky decomposition of Σe and, Draw Q following
Rubio-Ramirez et al. (2010)
 Randomly draw matrix X of NID(0, 1), and obtain Q by QR decomposition.
 More generalized method than using Givens rotation matrix
 If the IRFs of Q X (IRFs of εt) meet the restrictions, save them, unless drop
them.
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Impulse Response Functions
 How the inventories react over time to demand and supply shocks
 X: months after a shock (x=0), Y: impulse response of inventories
 The IRFs of SVAR are derived from the structural moving average
representation
yt=Σψiεt-i
 where ψi measures the response of yt+i to εi. The sequence of {ψ0, ψ1,…}
forms the IRFs.
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Confidence Level 68%
 The 68% confidence bands are plotted for the IRFs, following the
convention from Sims and Zha (1999) and Uhlig (2005), the benchmark
literature of SVAR approach.
 The 68% confidence level is widely used in economic IRFs, especially,
of SVAR studies, due to uncertainties arising from identification
procedures.
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Inventory Data: Global Crude Oil
 A proxy inventory is used to address the lack of open and accurate
data.
 Proxy: the U.S. crude oil inventories from EIA are scaled by the ratio of
OECD commercial petroleum inventories from EIA over U.S. petroleum
inventories from IEA (Kilian and Murphy, 2014).
 Several limitations: the exclusion of non-OECD nations and the
omission of hedge funds effect.
 Alternative: global above-ground crude oil inventory compiled by
Energy Intelligence Group (EI), but proprietary!
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Inventory Data: U.S. Crude Oil

Because of the high liquidity in the U.S. oil market, the U.S. inventories are highly
likely to reveal the distinctive patterns of speculative-buffer responses to oil
demand and supply shocks compared to inventories from other countries, which
thus best suits for our research purpose.

In addition, considering the significant role that the U.S. plays in the global oil
market, oil producers and traders around the globe pay particular attention to
changes in the level of U.S. inventories and hence warrants the use of the variable
in our modeling.

U.S. Strategic Petroleum Reserve (SPR) is not included in our target oil inventory.
Since the SPR is emergency storage to mitigate possible supply disruptions in the
oil market, it is highly likely to show only its buffer behavior. The SPR level tend to
be determined by political decisions, remaining stable mostly, which makes it
improper to be analyzed in relation with fluctuant economic variables.
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