Blame Game Continues:
Speculators v Regulators
Bahattin Buyuksahin
Senior Oil Market Analyst, IEA
© OECD/IEA - 2011
Regulatory Challenges
„ Financial Crisis—precipitated by mortgages
„ Commodities
„ Public (mis)Perceptions
„ Worldwide Markets—Cooperation
z IOSCO, OECD, IEA, etc.
„ Financial/Product Market Overlap
z SEC, EIA, FERC, Fed, etc.
z On Exchanges and OTC
„ Perspective—futures markets robust
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Dodd-Frank
„ Title VII—OTC Derivatives
z Increase transparency, efficiency
z Mitigate counterparty risk
z Mitigate systemic risk
„ Requirements
z Execution on swap execution facilities (SEFs)
z Central clearing
z Public reporting
z CFTC/SEC/Fed-defined universe
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Dodd-Frank (cont.)
„ Position limits
z Prevent excessive speculation
z Prevent manipulation
z Ensure market liquidity
z Ensure price discovery
„ Swap dealers
z Capital requirements
z Margin requirements
z CFTC/SEC/Fed-defined universe
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Sources of Commodity Price Changes
„ Uncertainty/Risk Management?
„ Animal Spirits/Excessive Speculation?
„ Traders?
z OTC swaps
z Speculators—”Massive Passives”
Š Commodity Index Traders
Š ETFs
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What We Know: Data Available
„ Large Trader Data—at CFTC.gov weekly
z End of day positions
„ Commercial
z Producer/Merchant
z Swap Dealers (Index Funds)
„ Non-commercial
z Managed Money (Hedge Funds)
z Others
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What We Know: Market Growth
„ Increased participation
z Hedge funds
z Swap Dealers
Š Commodity Index Funds
Š OTC swaps
z Exchange Traded Funds (ETFs—metals,
energy)
„ Relatively stable mix over time
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Economic Studies I: Inter-Commodity
Linkages
„ “Fundamentals, Trader Activity and Derivative
Pricing”
z Buyuksahin, Haigh, Harris, Overdahl, and Robe
„ Focus on Swap Dealer participation
z From commodity index trading in nearby futures
z From OTC positions in back-dated futures
„ Cointegration of Crude Oil futures prices
z Result in “better” pricing for hedgers in 1-year and
2-year contracts
„ Supports the notion that markets should
encourage broad participation
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NYMEX Crude Oil Futures
(WTI)
Nearby, 1-yr and 2-yr Prices: 2000-2011
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Trace Statistics
„ Different for shorter-dated contracts
Š Short-dated contracts cointegrated with nearby
much earlier
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Explaining Cointegration
„ Fundamentals matter
z Spare capacity & Slope
z Demand for all industrial commodities
„ Trading activity matters as well
z Commodity swap dealers in nearby contracts
Š Not further-out positions
z Financial traders in nearby and backdated
contracts
Š Hedge funds (MMT), others (NRP)
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A simple question
„ Is speculative activity destabilizing markets?
z Is speculative activity moving prices?
Theory: Stabilizing Speculation
Š Profitable speculation must involve buying when the price is
low and selling when the price is high (Friedman, 1953)
Š Speculators fill hedgers’ demand-supply imbalances and provide
liquidity to the market (Keynes, 1923)
Š Speculative activity reduces cost of hedging (Hirshleifer, 1990
and 1991)
Theory: Destabilizing Speculation
Š Shleifer and Summers (1990) note that herding can result from
investors reacting to common signals or overreacting to recent
news.
Š Long et al. (1990) show, rational speculators trading via
positive feedback strategies can increase volatility and
destabilise prices.
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Data
„ Non-commercials
z
Hedge Funds (MMT) includes Commodity Pool Operators
(CPOs), Commodity Trading Advisors (CTAs), Associated
Persons who control customer accounts, and other
Managed Money traders
z Floor Brokers & Traders (FBT)
z Non-Registered Participants (NRP) Traders not registered
under the Commodity Exchange Act (CEA) – mostly non
MMT financial traders
„
Commercials
z “Traditional”
Š Producers (AP)
Š Manufacturers (AM) (refiners, fabricators, etc.)
Š Dealers AD (wholesalers, exporter/importers,
marketers, shippers, etc.)
Š Others AO
z Commodity Swap Dealers (AS) (includes arbitrageurs)
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Economic Studies II: Herding and Positive
feedback trading
„ “The Prevalence, Sources and Effects of Herding”
z Buyuksahin, Boyd, Harris, Haigh
„ Test for herding by assessing the degree of
correlation across hedge funds and/or FBTs in
buying and selling of futures.
„ Also, we test for positive feedback trading by
looking at the demand and past performance of
futures product.
„ Finally, we test for excess demand and price
changes.
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Herding Measure (LSV (1992))
For a given futures market, i, and day, t, the herding measure developed by LSV
(1992) and applied to futures markets here is as follows:
H(i, t) =| p(i, t) − p(t)| −AF(i, t)
where
B (i, t )
[S ( i , t ) + B ( i , t ) ]
p (i, t ) =
,
and
i= N
p (t ) =
∑
it
B (i, t )
i=1
⎡
⎢
⎣
i= N
∑
i=1
it
S (i, t ) +
i= N
∑
i=1
it
⎤ ,
B (i, t ) ⎥
⎦
and
AF(i, t) = E{| p(i, t) − p(t)|}
,
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Herding Measure (LSV (1992))
where
S (i, t ) = the number of traders that are going short in futures
market i on day t
B (i, t ) = the number of traders that are going long in futures
market i on day t
p (i, t ) = fraction of active futures traders going long in
futures market i on day t
p (t ) = total number of future traders going long on day t
relative to the total number of futures traders active on
day t across all 32 futures markets
N it = volume of futures contracts traded by futures market
participants on day t
AF (i, t ) = adjustment factor that accounts for the fact that
under the null hypothesis of no herding the expected value
of | p(i, t ) − p(t ) | is greater than zero.
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Herding: Empirical Findings
„ Overall herding measure for nearby contract is
„
„
„
„
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0.07 for hedge fund and 0.06 for FBTs (for nearby
and first deferred it is 0.09 for hedge funds and
.07 for FBTs).
In general, the buy herding is much higher than
the sell herding.
The livestock contract exhibits the highest degree
of herding while financial contracts exhibit the
least degree of herding.
Level of herding is higher for hedge funds than for
FBTs for most of the contracts (26 out of 32).
In general, the level of herding is lower in rollperiods, which is counter to what we would
expect.
Feedback Strategies: Measurement
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Economic Studies III: Role of Financial
Players
“ […] hedge funds are exploiting recently deregulated energy
trading markets to manipulate energy prices. […] speculative
purchases of oil futures contracts added as much as $20-$25
per barrel to the current price of oil.” “
Tyson Slocum, Capital Hill Hearing Testimony, July 11, 2008
“These swap dealers […] convinced institutional investors that
commodity futures were an asset class that would deliver
‘equity like returns’ […] as a result a new and more damaging
form of speculator was born […] the result has been a titanic
wave of speculative money that has flowed into the
commodities futures markets and driven up prices
dramatically.”
Adam K. White, Capital Hill Hearing Testimony, July 10, 2008
© OECD/IEA - 2011
Observations
„ More investment money in commodity futures
markets
z Thousands of hedge funds, commodity index funds, etc.
z Assets under Management (AUM):
now exceed $400bn, inflows = $350bn in 10 years
(Barclays, Nov. 2011)
„ What could this development mean for…
z Energy Price Levels? Buyuksahin and Harris (2011)
z Oil Market Volatility? Buyuksahin, Brunetti and Harris
(2009, 2010)
z Cross-Market Linkages? Buyuksahin and Robe (2010,
2011)
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Data and Findings
„ For each category we consider:
z Level of Net Futures Position
z Change in Net Futures Position
z Level of Net Total Position (Futures plus futures
equivalent options)
z Change in Net Total Position
„ Trading Activity is measured at
z Daily and multiple day intervals
¾ What we found:
z Speculative activity does not Granger-cause prices
z In general, on the other hand, we find the reverse
causality to hold, i.e. position change is Granger caused
by price change.
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Prices and Realized Volatility
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Impulse Responses: Crude Oil
Response of Volatility to Merc hants
Response of Volatility to Floor Brokers
Response of Volatility to Swap Dealers
Response of Volatility to Hedge Funds
.04
.04
.04
.04
.04
.02
.02
.02
.02
.02
.00
.00
.00
.00
.00
-.02
-.02
-.02
-.02
-.02
-.04
-.04
-.04
-.04
-.04
1 2 3 4 5 6 7 8 9 10
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Response of Volatility to Manufacturers
1 2 3 4 5 6 7 8 9 10
1 2 3 4 5 6 7 8 9 10
1 2 3 4 5 6 7 8 9 10
1 2 3 4 5 6 7 8 9 10
Multivariate Granger Causality Findings
„ Returns are not Granger-caused by positions
(including those of swap dealers and hedge
funds)
„ Hedge fund activity
z does not cause any variable in the system
z is caused by all the variables in the system
z reacts to market conditions and provides liquidity
z Reduces volatility
„ Swap dealer activity
z Generally reduces volatility
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Contemporaneous Effects
N
RVi,t = α i + β i , jTPi , j ,t + ∑ ς i , s RVi ,t − s + ε i ,t
s =1
„ Endogeneity Î IV Î change in number of
reporting traders in each market each day
„ Stock and Yogo (2005):
z Limited information Maximum Likelihood better than
two-stage least squares
z The validity of the instruments is tested via an F-test
using their critical values
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IV Estimation Position Changes and
Volatility
Coeff.
2.71e-4**
(1.01e-4)
Producer/
Manufacturer
6.18e-5
(2.05e-4)
F-Stat
113.1
46.08
9.948
321.5
16.38
Coeff.
1.76e-3*
(9.73e-4)
-1.26e-4
(2.54e-3)
-2.94e-4
(7.63e-4)
-6.43e-4
(5.19e-4)
-8.29e-06**
(3.60e-5)
F-Stat
34.40
17.72
8.67
117.67
43.11
Coeff.
1.37e-5
(1.66e-4)
-5.11e-4
(7.55e-4)
2.95e-4
(2.84e-4)
-1.45e-4
(1.72e-4)
-3.57e-5
(1.53e-4)
F-Stat
33.38
12.276
14.08
70.70
10.09
Merchant
Crude
Oil
Natural
Gas
Corn
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Broker
Swap
Dealer
Hedge
Fund
5.41e-4**
(2.73e-4)
-1.20e-4
(9.17e-5)
-2.88e-4**
(8.31e-5)
Findings
„ Hedge funds are reacting to market conditions and
providing liquidity to the market; i.e. there is a
uni-directional causation from change in price to
change in MMT’s position
„ Interestingly, Swap dealers change in position is
preceded by change in prices
„ More transparent information on composition of
open interest is needed to have better
understanding of role of different market
participants on prices and observed high volatility
in commodity derivatives markets
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Economic Studies IV: Cross-Market
Linkages
“As more money has chased (...) risky assets,
correlations have risen. By the same logic, at
moments when investors become risk-averse and
want to cut their positions, these asset classes
tend to fall together. The effect can be
particularly dramatic if the asset classes are
small—as in commodities. (...) This marching-instep has been described (...) as a ‘market of
one’.”
The Economist, March 8, 2007.
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The “Marching in Step” Observers Had in
Mind
500
400
300
200
100
0
92 93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08
S & P 500 Index (1991=100)
Dow Jones Indus trial Index (1991=100)
S&P GS Commodity Total Return Index (1991=100)
DJ_AIG Commodity Total Return Index(1991=100)
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The “Marching in Step” – after Lehman
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A “Market of One” – Really?
„ Büyükşahin, Haigh & Robe (JAI 2010):
z Not so fast:
Š Let’s look at return correlations, not price levels
ƒOn average, return correlations between passive
equity and energy investments were about zero (1991
to August 2008)
ƒNo secular increase in dynamic conditional
correlations (DCC)
z General result?
Š Yes
ƒTrue at daily, weekly & monthly frequencies
ƒTrue regardless of index choice (GSCI or DJ-UBS;
S&P or DJIA)
Š And yet…
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SP500 & GSCI Correlation (DCC), 19912011
DCC estimates average close to Ø, fluctuates
substantially over time
Egypt protests
Lehman collapse
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Cross-Commodity Correlations
Same for Cross-Commodity correlations? Not for
Industrial Metals…
Structural break? If so, it
predates financialization
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Cross-Commodity Correlations
Have “Ag” prices started moving with Energy or
Metals? Not really…
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Cross-Commodity Correlations
How about Livestock? Quite the opposite…
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Findings
„ “Co-movements”
z Time variations in correlations, but no upward trend
till crisis
z Extreme-events analysis: commodity umbrella leaks
„ “Speculation” in cross-section of energy paper
mkts
z Increase in speculation + hedge fund activity + cross-
mkt activity
„ Impact of hedge funds in energy markets
z Hedge fund activity helps link markets
z Market stress matters, too
z Interaction – contagion through wealth effects?
„ Information on OI composition is payoff-relevant
z CFTC decision to disaggregate more
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Dodd-Frank Challenges
„ Rule writing/enforcement burden
„ Square peg/round hole with OTC markets?
„ Position limits—expand Federal role, enter
politics?
„ CFTC/SEC/Fed coordination
z Consumer Protection
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