and Equity-Commodity Linkages - International Monetary Fund€¦ · and Equity-Commodity Linkages...
Transcript of and Equity-Commodity Linkages - International Monetary Fund€¦ · and Equity-Commodity Linkages...
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Speculationpand
Equity-Commodity Linkages
Bahattin Büyükşahin
Michel Robe*
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h dDoes It Matters Who Trades?
Bahattin Büyükşahin
Michel Robe*
* T H I S P R E S E N T A T I O N I S B A S E D S O L E L Y O N P U B L I C L Y A V A I L A B L E D A T A . I T R E F L E C T ST H E O P I N I O N S O F I T S A U T H O R S O N L Y A N D N O T T H O S E O F A N Y G O V E R N M E N T T H E B A N KT H E O P I N I O N S O F I T S A U T H O R S O N L Y , A N D N O T T H O S E O F A N Y G O V E R N M E N T , T H E B A N KO F C A N A D A , T H E U . S . C O M M O D I T I E S F U T U R E S T R A D I N G C O M M I S S I O N ( C F T C ) , T H E U . S .S E C U R I T I E S A N D E X C H A N G E C O M M I S S I O N ( S E C ) , A N Y O F T H E C O M M I S S I O N E R S , O R A N YO T H E R S T A F F A T T H O S E I N S T I T U T I O N S .
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Background
Large investment money flows in commodity futures markets
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g y y Thousands of hedge funds, commodity index funds, etc.
Commodity assets under management (AUM): Peak in Fall 2011 at $425bn; inflows = $360+bn in prior decade (Barclays 2012) Peak in Fall 2011, at $425bn; inflows = $360+bn in prior decade (Barclays, 2012)
What could this development mean for…
Commodity Price Levels? Commodity Price Levels? Yes: Singleton (2013)
No: ITF Report (2008), Büyükşahin & Harris (2011), Hamilton (2011), Kilian & Murphy (2012)
Oil Market Volatility? Oil Market Volatility? No: Brunetti et al (volatility-regime switching, 2011), Boyd et al (herding, 2011)
Maybe: Büyükşahin, Haigh & Robe (extreme events, 2010), Cheng, Kirilenko & Xiong (“convection”, 2012)
Cross Market Linkages? Our focus today Cross-Market Linkages? Our focus today
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Background4
“As more money has chased (...) risky assets, correlations have risen. By the same logic, at moments when investors become risk averse and moments when investors become risk-averse and want to cut their positions, these asset classes tend to fall together. The effect can be particularly f g ff p ydramatic if the asset classes are small—as in commodities. (...) This marching-in-step has been d ib d ( ) ‘ k f ’ ”described (...) as a ‘market of one’.”
The Economist, March 8, 2007.
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The “Marching in Step” Observers had in Mind5
275GSCI (Commodity) July 1, 2008
200
225
250GSCI (Commodity)
DJUBS (Commodity)
DJIA (US equities)
July 1, 2008
125
150
175S&P 500 (US equities)
MSCI World Equities
75
100
125
25
50Base: Jan. 2, 2001 = 100
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“Marching in Step” since Lehman6
275GSCI (Commodity) L
200
225
250GSCI (Commodity)
DJUBS (Commodity)
DJIA (US equities)
Lehman
125
150
175S&P 500 (US equities)
MSCI World Equities
75
100
125
25
50Base: Jan. 2, 2001 = 100
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A “Market of One” – Really?
Pre-Lehman
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Büyükşahin, Haigh & Robe (JAI 2010): Look at return correlations, not index levels
Findings: On average, return correlations between passive commodity and
equity investments were about zero (pre Lehman)equity investments were about zero (pre-Lehman) No secular increase in dynamic conditional correlations (DCC)
• True at daily, weekly & monthly frequencies• True regardless of index choice (GSCI or DJ-UBS; S&P or DJIA)
Extreme-event correlations patterns changed in second-half of 2008
Post-Lehman? Post-Lehman?
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SP500 & GSCI Correlation (DCC), 1991-2011
DCC estimates average Ø – but fluctuate substantially over time
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0 6
0.8
1
DCC_MR_DJ_SP
DCC_MR_GSTR_SP
0.2
0.4
0.6DCC_MR_DJTR_SP
-0.2
0
Lehman collapse-0.6
-0.4Lehman
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Correlation Facts
How confident are we of the correlation pattern:
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p 0. Frequency?
Irrelevant – Similar patterns at daily, weekly & monthly frequencies
1. Specific to one commodity?Nope – Similar for energy, metals, grains
2. Does it matter how we estimate correlations?Yep – Very different patterns with rolling correlations
3. What about cross-commodity correlations?Differences – Ags or Livestock vs. industrial commodities
Similar – Post-Lehman behavior
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1. Equity Returns vs. Energy & Other Commodities
Equity Returns vs. Energy (Top) or Diversified Commodity Portfolio
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2. DCC Analysis
Dynamic Conditional Correlation (Engle, JBES 2002)
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y g 2-stage estimation:
First stage - n univariate GARCH(1 1) estimates are obtained (simultaneously) n univariate GARCH(1,1) estimates are obtained (simultaneously),
producing consistent estimates of time-varying variances (Dt). Second stage - The correlation part of the log likelihood function is maximized The correlation part of the log-likelihood function is maximized,
conditional on the estimated Dt from the first stage.
Advantages:
Takes into account the time-varying nature of the relationship between equity and commodity returns
Accounts for changes in return volatilitiesg Important – see Forbes & Rigobon (JF 2002) for emerging mkts
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Without accounting for time-varying volatility…
… we’d mis-estimate how much & when correlations change
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Without accounting for time-varying volatility…
Even worse problem with the MSCI World Equity Index
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Vs. accounting for time-varying volatility…
Using DCC, we find no visible trend before Lehman
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3. Cross-Commodity Correlations
Same for Cross-Commodity correlations? Not for Industrial Metals…
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0.625
0.75
DCC_MR_GSIMTR_GSENTR
DCC_MR_GSNETR_GSENTR
Structural break? If so, itpredates financialization
0.375
0.5
0.125
0.25
-0.125
0
-0.25
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Cross-Commodity Correlations
How about Livestock? Quite the opposite…
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0 4
0.5
0.6DCC_MR_GSLVTR_GSAGTR
DCC_MR_GSLVTR_GSIMTR
0.2
0.3
0.4
0 1
0
0.1
-0.3
-0.2
-0.1
-0.4
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I Thi PI. This Paper
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Thinking about Commodity-Equity Linkages
As the DCC graphs show…
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Equity-commodity DCC estimates do fluctuate substantially over time This paper: can we predict those fluctuations? Macroeconomic / physical fundamentals? “Excess” speculation? Both? Macroeconomic / physical fundamentals? Excess speculation? Both?
Extreme-event correlations do exist (Shanghai Feb.’07, Lehman Sept.’08, …)
This paper: does financial stress increase correlations? This paper: does financial stress increase correlations?
This paper: how (through what channel) does stress affect distributions?
O f Our focus
Equity-commodity co-movements
Why?
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II. Trading FactsII. Trading FactsFinancialization of Commodity Futures Markets
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A. Position Data
Data for this presentation: Public data
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Data for this presentation: Public data CFTC Commitments of Traders (COT) Reports (2000-2010)
Weekly (Tuesday) end-of-day positions Two broad trader types
• “Commercials” • “Non-Commercials”
Limitations Heterogeneity within two broad trader categories (CFTC 2009) Hedge Funds vs. other speculatorsg p Swap Dealers vs. Traditional Commercials
Aggregated across all contract maturities
U id l b d d b Upside: our results can be reproduced by anyone
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What Does the Public Data Show?
1. Importance of Financial Traders
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p fo “Excess” speculation is up, 2000-2010
o “Excess” ≠ Excessive
o “Excess” = index of spec activity beyond net hedging demand
o Hedge Funds & Swap Dealers (incl. CITs) are up, 2006-2013
Contract maturity(ies)? o Contract maturity(ies)?
2. Heterogeneity within the Broad Categorieso Good idea to break out Swap Dealers & Hedge Funds (2009)
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Generalizing to all GSCI Commodities
We would like
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Position data for all futures contracts in the GSCI index
Unfortunatelyy
Some contracts are non-US no data (e.g., Gas oil; Brent)
Position data for RBOB gasoline are available only after 2006
Bottom line
We have data for 17 U.S. commodity futures markets7 y
Examples: Energy = WTI crude + Henry-Hub nat’l gas + No.2. heating oil
Weights:
Time-varying GSCI weights, scaled to account for “missing” contracts
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Gauging Speculative Activity
Working’s T (1960):23
Goal: measure the extent to which speculative positions exceed the net hedging demand in a given futures market i
Intuition: long and short hedgers do not trade simultaneously or in the same quantity; speculators satisfy this unmet hedging demand in the marketplace – but there may be more spec activity than that bare minimum.
Formally: 1
1
where is the magnitude of the short positions held in the aggregate by all non-commercial
traders; stands for all non-commercial long positions; and, stands for all non-commercial
long positions and stands for all long hedge positions.
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C. Financialization in Pictures
Overall speculation is up
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p p Averaged from 10-15% “excess” spec before 2003
rises to 30-40% after 20053 4 5
Commodity Index TradingS D l iti t f b t % f f t OI Swap Dealer positions account for about 35% of futures OI
in a growing market (2006-2013)
Hedge Funds 25-30% of the open interest after 2006
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Spec Activity
Working’s T, January 2000 to March 2010
25
0 4
0.5 WSIA (rescaled Working T -- all maturities)Oct. 6, 2008
0.3
0.4
0.1
0.2
0
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III M i Q iIII. Main Question
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Does Trader Identity Matter?27
Does the composition of trading activity (i.e., who trades) tt f t i i ?matter for asset pricing?
Theoretical reasons to believe trader identity matters
Models show that less-constrained traders link asset markets e.g., Basak & Croitoru (JFE 2006)
During financial stress periods, contagion or retrenchment? E.g., Kyle & Xiong (JF 2001), Pavlova & Rigobon (REStud 2008)g , y & o g (J 00 ), o & gobo ( S 008)
Who is a “candidate” for enhancing linkages?
Traditional “commercial” traders, Long-term hedgers, etc.? Unlikely Traditional commercial traders, Long term hedgers, etc.? Unlikely
Hedge funds? More likely Enter/exit markets frequently trade across markets to exploit perceived mis-pricings/opportunities
Büyükşahin & Robe – IMF 2013
trade across markets to exploit perceived mis pricings/opportunities• Levered + subject to borrowing limits/wealth effects + value-arb across markets
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A Dependent Variable (LHS):A. Dependent Variable (LHS):Equity-Commodity Correlations
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Return Correlations
Our focus – returns on:
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Investible passive commodity indices
GSCI (now S&P GSCI), DJ-AIG (now DJ-UBS)
Benchmark passive equity indices
S&P 500 (also, DJIA and MSCI)
i i d Time period January 1991 to March 2010
Prices Tuesday settlement prices (weekly analysis)
Similar results at different frequencies (daily)
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DCC Analysis
Dynamic Conditional Correlation (Engle, 2002)
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Dynamic Conditional Correlation (Engle, 2002) 2-stage estimation:
First stage, i i t GARCH( ) ti t bt i d hi h n univariate GARCH(1,1) estimates are obtained, which
produces consistent estimates of time-varying variances (Dt). Second stage, correlation part of the log likelihood function is maximized correlation part of the log-likelihood function is maximized,
conditional on the estimated Dt from the first stage.
Advantages: Takes into account the time varying nature of the relationship
between variables Accounts for changes in volatility
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Correlations between SP500 & GSCI Returns
Fig.1B: DCC average Ø, fluctuate substantially +… Lehman!
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g g y
0.8
1
0.2
0.4
0.6
-0.4
-0.2
0
-0.8
-0.6
4DCC_MR_SP_MXWO
DCC_MR_SP_GSTR
DCC_MR_MXWO_GSTR
Arab Spring
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B What Predicts Correlations:B. What Predicts Correlations:Trader Positions or Fundamentals?
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1. Trading
We would like
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Detailed position data for all futures contracts in the GSCI index
UnfortunatelyS f h S d ( G il & d ) Some of the contracts are non-US no data (e.g., Gas oil & Brent crude)
Position data for RBOB gasoline are available only after 2006
Bottom lineBottom line We have trader-level data for 17 contracts
Energy example: WTI crude oil, Henry Hub natural gas, No.2. heating oil, etc.
Weights:
time-varying weights from S&P
Rescaling to account for “missing” contracts
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2. Economic Fundamentals?
Inflation?
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o Business cycles / economic climate? They ought to matter
Erb & Harvey (FAJ 2006), Gorton & Rouwenhorst (FAJ 2006) Kilian & Park (IER 2009)
Appropriate measurement level? US economic activity?
• ADS (Aruoba-Diebold-Scotti, JBES 2009) Available at high frequency
World economy?• Shipping freight rates? (Kilian, AER 2009)• Non-exchange-traded commodity prices? (Korniotis, FRB 2009)g y p
Less likely that those price fluctuate with spec activity
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Worldwide Economic Activity & DCC
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Figure 3: SHIP negatively related with DCC after 1997?
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3. Market Stress?36
a. Financial Stress? Financial stress should matter: Financial stress should matter:
Bond-equity returns extreme linkages in G-5 countries Hartmann, Straetmans & de Vries, REStat 2004
International equity market correlations increase in bear markets Longin & Solnik, JF 2001
Commodity-equity linkages went up in Fall 2008 Buyuksahin, Haigh & Robe, JAI 2010
Financial shocks are propagated internationally through channels such as bank lending (e.g., van Rijckeghem & Weder, JIE 2001) international mutual funds (e.g., Broner et al, JIE 2006)
Our measure: TED Spread Robustness: VIX Robustness: VIX
b. Hedge fund or spec activity or cross-market traders?
a+ b: Do these effects interact?
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a+ b: Do these effects interact?
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C Wh R ll M ?C. What Really Matters?ARDL RegressionsARDL Regressions
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B. Explaining Commodity-Equity DCC
Regress the DCC estimate on…
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…trader position data Each trader category entered separately Short-dated (< 3 months) vs. Far-dated (> 3 months) positions
All traders in a category vs. only commodity-equity cross-mkt traders …real-sector variables …market stress proxies
and interaction terms
Technical issue Some series are I(0), others I(1); also, endogeneity? ARDL model, Pesaran-Shin (1999) approach Lagged values of variables to deal with AC and endogeneity
One cointegrating vector OK
Büyükşahin & Robe – IMF 2013
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Economic Activity & Market Stress Matter
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2000-2010 1991-2010 2000-2010 1991-2010
Constant -0.0425855 -0.0456055 -.00925942 -0.0193913
(0.1139) (0.07643) (0.05749) (0.04863)
ADS 0.136424 -0.0784245 0.153715 * 0.00826134S 0. 36 0.0 8 5 0. 53 5 0.008 6 3
(0.1530) (0.06634) (0.08115) (0.04729)
SHIP -0.785661 ** -0.249104 -0.596757 *** -0.251052 **
(0 3811) (0 1790) (0 1880) (0 1165)(0.3811) (0.1790) (0.1880) (0.1165)
UMD 0.126140 0.0924424 0.0760120 0.0692592
(0.1070) (0.07331) (0.05278) (0.04678)
TED 0 630212 ** 0 240228 * 0 334082 ** 0 111721TED 0.630212 ** 0.240228 * 0.334082 ** 0.111721
(0.3125) (0.1410) (0.1368) (0.08770)
DUM 0.485022 *** 0.486330 ***
(0 1232) (0 1243)
Büyükşahin & Robe – IMF 2013
(0.1232) (0.1243)
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But Speculative Activity Matters, as well!
40
2000-2010
2000-2010
Constant -3.85024 *** -2.08797 **
(1.328) (0.9959)ADS 0.103863 0.132858 *
(0 09492) (0 07009)(0.09492) (0.07009)
SHIP -0.933864 *** -0.693805 ***
(0.2664) (0.1905)UMD 0.0893486 0.0712289
(0.06653) (0.04622)
TED 6.24366 ** 4.27775 **
(3.120) (2.102)
Excess Spec 3 07302 *** 1 64474 **Excess Spec. 3.07302 1.64474
(1.048) (0.8008)
INT_TED_WSIA -4.37543 * -2.96711 *
(2.261) (1.533)
Büyükşahin & Robe – IMF 2013
(2.261) (1.533)
DUM 0.391434 ***
(0.1273)
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Notice the Differential Impact under Stress
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2000-2010
2000-2010
Constant -3.85024 *** -2.08797 **
(1.328) (0.9959)ADS 0.103863 0.132858 *
(0 09492) (0 07009)(0.09492) (0.07009)
SHIP -0.933864 *** -0.693805 ***
(0.2664) (0.1905)UMD 0.0893486 0.0712289
(0.06653) (0.04622)
TED 6.24366 ** 4.27775 **
(3.120) (2.102)
Excess Spec 3 07302 *** 1 64474 **Excess Spec. 3.07302 1.64474
(1.048) (0.8008)
INT_TED_WSIA -4.37543 * -2.96711 *
(2.261) (1.533)
Büyükşahin & Robe – IMF 2013
(2.261) (1.533)
DUM 0.391434 ***
(0.1273)
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VI C l iVI. Conclusion
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Findings
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“Co-movements” Ti i i i l i b b i d ill i i Time variations in correlations, but no obvious trend till crisis
Extreme-events analysis: commodity umbrella leaks
“Speculation” in cross-section of commodity markets Speculation in cross section of commodity markets Increase in “excess” speculation
Predictive power of spec positions in commodity marketsp p p y Spec activity helps link markets Market stress matters, too Interaction – contagion through wealth effects?Interaction contagion through wealth effects?
Information on OI composition should be payoff-relevant disaggregation
Büyükşahin & Robe – IMF 2013
d sagg egat o
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Further Work
Disaggregation44
What has been happening post-Lehman?
Th ? Wh t h ld l ti l k lik Theory? What should correlations look like
Büyükşahin & Robe – IMF 2013