Blame Game Continues: Speculators v Regulators Bahattin Buyuksahin Senior Oil Market Analyst, IEA

Transcription

1 Blame Game Continues: Speculators v Regulators Bahattin Buyuksahin Senior Oil Market Analyst, IEA

2 Regulatory Challenges Financial Crisis precipitated by mortgages Commodities Public (mis)perceptions Worldwide Markets Cooperation IOSCO, OECD, IEA, etc. Financial/Product Market Overlap SEC, EIA, FERC, Fed, etc. On Exchanges and OTC Perspective futures markets robust 2

3 Dodd-Frank Title VII OTC Derivatives Increase transparency, efficiency Mitigate counterparty risk Mitigate systemic risk Requirements Execution on swap execution facilities (SEFs) Central clearing Public reporting CFTC/SEC/Fed-defined universe

4 Dodd-Frank (cont.) Position limits Prevent excessive speculation Prevent manipulation Ensure market liquidity Ensure price discovery Swap dealers Capital requirements Margin requirements CFTC/SEC/Fed-defined universe

5 Sources of Commodity Price Changes Uncertainty/Risk Management? Animal Spirits/Excessive Speculation? Traders? OTC swaps Speculators Massive Passives Commodity Index Traders ETFs 5

6 What We Know: Data Available Large Trader Data at CFTC.gov weekly End of day positions Commercial Producer/Merchant Swap Dealers (Index Funds) Non-commercial Managed Money (Hedge Funds) Others

7 What We Know: Market Growth Increased participation Hedge funds Swap Dealers Commodity Index Funds OTC swaps Exchange Traded Funds (ETFs metals, energy) Relatively stable mix over time 7

8 Economic Studies I: Inter-Commodity Linkages Fundamentals, Trader Activity and Derivative Pricing Buyuksahin, Haigh, Harris, Overdahl, and Robe Focus on Swap Dealer participation From commodity index trading in nearby futures From OTC positions in back-dated futures Cointegration of Crude Oil futures prices Result in better pricing for hedgers in 1-year and 2-year contracts Supports the notion that markets should encourage broad participation

9 NYMEX Crude Oil Futures (WTI) Nearby, 1-yr and 2-yr Prices:

10 Trace Statistics Different for shorter-dated contracts Short-dated contracts cointegrated with nearby much earlier

11 Explaining Cointegration Fundamentals matter Spare capacity & Slope Demand for all industrial commodities Trading activity matters as well Commodity swap dealers in nearby contracts Not further-out positions Financial traders in nearby and backdated contracts Hedge funds (MMT), others (NRP)

12 A simple question Is speculative activity destabilizing markets? Is speculative activity moving prices? Theory: Stabilizing Speculation Profitable speculation must involve buying when the price is low and selling when the price is high (Friedman, 1953) Speculators fill hedgers demand-supply imbalances and provide liquidity to the market (Keynes, 1923) Speculative activity reduces cost of hedging (Hirshleifer, 1990 and 1991) Theory: Destabilizing Speculation Shleifer and Summers (1990) note that herding can result from investors reacting to common signals or overreacting to recent news. Long et al. (1990) show, rational speculators trading via positive feedback strategies can increase volatility and destabilise prices.

13 Data Non-commercials Hedge Funds (MMT) includes Commodity Pool Operators (CPOs), Commodity Trading Advisors (CTAs), Associated Persons who control customer accounts, and other Managed Money traders Floor Brokers & Traders (FBT) Non-Registered Participants (NRP) Traders not registered under the Commodity Exchange Act (CEA) mostly non MMT financial traders Commercials Traditional Producers (AP) Manufacturers (AM) (refiners, fabricators, etc.) Dealers AD (wholesalers, exporter/importers, marketers, shippers, etc.) Others AO Commodity Swap Dealers (AS) (includes arbitrageurs)

14 Economic Studies II: Herding and Positive feedback trading The Prevalence, Sources and Effects of Herding 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.

15 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: where Hit (, ) = pit (, ) pt () AFit (, ) and p ( i, t) = B ( i, t) [ S ( i, t) B ( i, t) ] +, p ( t ) i = N B ( i, t ) i = 1 = i = N it i = N it i = 1 i = 1 it S ( i, t ) + B ( i, t ), and AF(, it) = E{ pit (, ) pt () },

16 Herding Measure (LSV (1992)) where Sit= (, ) the number of traders that are going short in futures market i on day t B(, it ) = the number of traders that are going long in futures market i on day t p(, it ) = 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(, it ) = adjustment factor that accounts for the fact that under the null hypothesis of no herding the expected value of p( it, ) pt ( ) is greater than zero.

17 Herding: Empirical Findings Overall herding measure for nearby contract is 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.

18 Feedback Strategies: Measurement

19 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

20 Observations More investment money in commodity futures markets Thousands of hedge funds, commodity index funds, etc. Assets under Management (AUM): now exceed $400bn, inflows = $350bn in 10 years (Barclays, Nov. 2011) What could this development mean for Energy Price Levels? Buyuksahin and Harris (2011) Oil Market Volatility? Buyuksahin, Brunetti and Harris (2009, 2010) Cross-Market Linkages? Buyuksahin and Robe (2010, 2011)

21 Data and Findings For each category we consider: Level of Net Futures Position Change in Net Futures Position Level of Net Total Position (Futures plus futures equivalent options) Change in Net Total Position Trading Activity is measured at Daily and multiple day intervals What we found: Speculative activity does not Granger-cause prices In general, on the other hand, we find the reverse causality to hold, i.e. position change is Granger caused by price change.

22 Prices and Realized Volatility

23 Impulse Responses: Crude Oil Response of Volatility to Merchants Response of Volatility to Manufacturers Response of Volatility to Floor Brokers R e s p o n s e o f Vo la tility to Sw a p D e a le r s Response of Volatility to Hedge Funds

24 Multivariate Granger Causality Findings Returns are not Granger-caused by positions (including those of swap dealers and hedge funds) Hedge fund activity does not cause any variable in the system is caused by all the variables in the system reacts to market conditions and provides liquidity Reduces volatility Swap dealer activity Generally reduces volatility

25 Contemporaneous Effects RV i,t = α + β i N i, jtpi, j, t + ς i, srvi, t s + ε i, t s= 1 Endogeneity IV change in number of reporting traders in each market each day Stock and Yogo (2005): Limited information Maximum Likelihood better than two-stage least squares The validity of the instruments is tested via an F-test using their critical values

26 IV Estimation Position Changes and Volatility Crude Oil Coeff. Merchant 2.71e-4** (1.01e-4) Producer/ Manufacturer 6.18e-5 (2.05e-4) Broker 5.41e-4** (2.73e-4) Swap Dealer -1.20e-4 (9.17e-5) Hedge Fund -2.88e-4** (8.31e-5) F-Stat Natural Gas 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 Corn 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

27 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

28 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.

29 The Marching in Step Observers Had in Mind S&P 500 Index (1991=100) Dow Jones Industrial Index (1991=100) S&P GS Commodity Total Return Index (1991=100) DJ_AIG Commodity Total Return Index(1991=100)

30 The Marching in Step after Lehman

31 A Market of One Really? Büyükşahin, Haigh & Robe (JAI 2010): Not so fast: Let s look at return correlations, not price levels General result? Yes On average, return correlations between passive equity and energy investments were about zero (1991 to August 2008) 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) And yet

32 SP500 & GSCI Correlation (DCC), DCC estimates average close to Ø, fluctuates substantially over time Egypt protests Lehman collapse

33 Cross-Commodity Correlations Same for Cross-Commodity correlations? Not for Industrial Metals Structural break? If so, it predates financialization

34 Cross-Commodity Correlations Have Ag prices started moving with Energy or Metals? Not really

35 Cross-Commodity Correlations How about Livestock? Quite the opposite

36 Findings Co-movements Time variations in correlations, but no upward trend till crisis Extreme-events analysis: commodity umbrella leaks Speculation in cross-section of energy paper mkts Increase in speculation + hedge fund activity + crossmkt activity Impact of hedge funds in energy markets Hedge fund activity helps link markets Market stress matters, too Interaction contagion through wealth effects? Information on OI composition is payoff-relevant CFTC decision to disaggregate more

37 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 Consumer Protection