Marin Bozic University of Minnesota-Twin Cities NDSU Seminar, 10/28/2011

1. Price Discovery, Volatility Spillovers and Adequacy of Speculation in Cheese Spot and Futures Markets Marin Bozic University of Minnesota-Twin Cities NDSU Seminar, 10/28/2011

2. Motivation: Volatility in Dairy Sector

3. Motivation: How to Model Agricultural Prices

4. Motivation: How to Model Speculative Influence?

5. Volatility in the Dairy Sector: Why? Price S D′ D Quantity

6. Volatility in the Dairy Sector: Why?

7. Dealing with High Volatility Price Support Programs Milk Income Loss Contract Herd Termination Programs Social Insurance Supply Management Catastrophic Insurance (LGM-Dairy) Market-based instruments: Dairy Futures & Options, OTCs

8. Purpose of this paper • Where does the new information about prices originate? • Are there volatility spillovers between dairy markets? • Did speculators contribute to rising volatility in the market?

9. Pricing Milk in the U.S. : 1. Government

10. Pricing Milk in the U.S. : 2. CME Cash Market Spot market trades daily for 15 minutes each morning. No cash market for dry whey or milk.

11. Thin Slicing • Markets are very thin • USDA reports results of daily trading as well as weekly average • Prices for cheese used as benchmark in setting prices in direct transactions across the nation

12. Pricing Milk in the U.S. : 3. CME Futures Market

13. Class III Milk Futures: Comparing mid-October liquidity 2000-2011

14. Functions of the futures market: Price Discovery

15. Questions of interest • How do futures and cash market for cheese interact? • Price discovery • Volatility spillovers • Impact of speculation on dairy futures

16. A typical modeling approach • Test if cash and futures are stationary • If yes: VAR • If no: Co-integration • Volatility spillovers: • If high-frequency: realized volatility/VAR • If low-frequency: GARCH • Effects of speculation • If high-frequency: additional regressor in VAR • If low-frequency: BEKK-X, EGARCH-X

17. VAR vs. co-integration Case 1: Variables of interest are stationary (no persistent shocks) Instruction: Build a vector autoregressive model Case 2: Variables are non-stationary (some shocks are persistent) Instruction: Build a co-integration model

18. Data limitations • Cash market is thin • Closing price may indicate unfilled bid/uncovered offer • No cash market for manufacturing grade milk or dry whey • Futures market • Cheese futures market did not exist until 07/2010 • Data on speculative positions available only weekly

19. Implied Cheese Futures

20. Implied vs. observed cheese futures

21. Creating Nearby Futures Price Series

22. Unit root tests of cheese cash and futures time series • Augmented Dickey-Fuller (Said and Dickey, 1984) Null: : (unit root present; no drift) 2. Phillips-Perron (1988): Null: alpha=0, rh1

23. Unit Root Tests Results: Cash Cheese

24. Unit Root Tests Results: Cheese Futures

25. Devil is in the details: accounting for past lagged differenced futures

26. Unit Root Tests Results: Cheese Futures

27. Making sense of unit root results: 1. Economic Theory • Cash price analysis based on production theory • Perfect competition: zero long-run economic profit for the marginal producer • Profit margin will be a mean-reverting time series • If long-run industry average cost curve is flat • Permanent shifts in demand  temporary shifts to cash prices • Permanent changes in input prices  structural change • If supply is inelastic in short run  high persistency of shocks • If long-run AC curve is sloped  Permanent shifts in demand  permanent shocks to cash price series

28. Making sense of unit root results: 1. Economic Theory • Futures price analysis based on finance theory Efficient market • prices in a single contract will be martingales if the marginal risk premium is zero; • submartingales (downward biased) if marginal risk premium is positive • Supermartingales (upward biased) if marginal risk premium is negative - In any case: efficient futures prices will be non-stationary, i.e. all shocks to futures prices are permanent

29. Making sense of unit root results: 2. Time Series Modeling Exercise • What if there was a market in which cash price was indeed second-order stationary • If there was a futures contract designed to cash-settle against such a spot price, what would be the characteristics of that time series? • For simplicity, assume no marginal risk premium

30. Making sense of unit root results: 2. Time Series Modeling Exercise

31. Making sense of unit root results: 2. Time Series Modeling Exercise - Results • Martingale Property within each contract • Nearby series not a martingale

32. Making sense of unit root results: 2. Time Series Modeling Exercise -What would Unit Root Tests Show? Cash Prices: 1) Null would likely be rejected Futures prices: 2) for a single contract, null would likely not be rejected 3) Null more likely to be rejected for n-th than for n+1 nearby series 4) More obs. between rollover periods  null less likely to be rejected (reducing data frequency increases likelihood of rejecting the null)

33. Unit Root Tests: Conclusions • Cash Cheese is mean reverting • Nearby cheese futures are nonlinear • Unit-root processes within each contract • Mean-reverting at contract rollover Next: How to model this?

34. Modeling information flows Causality in mean Second-order causality (causality in variance)

35. Second order non-causality • Granger non-causality: knowing the futures price does not help us predict cash (and vice versa). • Second-order non-causality: knowing the futures price history may or may not help you predict the cash price level, but it does not influence the magnitude of cash price forecast conditional variance • Non-causality in variance: Granger non-causality and second-order non-causality combined

36. GARCH-BEKK and second-order non-causality

37. Adding speculators • The key problem is how to preserve positive definiteness of conditional variance matrix • Adding another term? • Sign of the impact of additional regressor is restricted to be positive  but we must have flexibility!

38. GARCH-MEX

39. GARCH-MEX

40. Measuring “Adequacy” of Speculation • Based on Working (1960) – “Working’s T” • The idea is that when hedgers are net long, long speculative position is not really ‘necessary’. But if it is there, it may “grease up” the market, or may be indicative of excessive speculation if T is too high. So, if

41. Measuring “Adequacy” of Speculation • Likewise, if hedgers are net short, then only long speculative positions are needed to balance the market. Having long speculators may help, but too much of it may be “excessive”. So, if • Key assumption: how to treat unreportables.

42. Results: Information flows in mean

43. Results: Information flows in mean

44. Results: Information flows in mean

45. Results: Information flows in mean

46. Results: Information flows in mean • Conclusion: Using daily close prices at either daily or weekly frequency, using either nominal or log prices, and either control for heteroskedasticity or not – we always find that adjustment to spread between cash and futures is done in the cash market

47. Results: volatility spillovers In a model where only GARCH-BEKK is added to error-correction model for mean, we find bi-directional volatility spillovers.

48. Results: Speculative Influence

49. Conclusions • Not likely that speculators increased volatility in dairy futures; if anything, speculative presence seems to be below what is deemed required for liquid market. • GARCH-MEX has a potential for allowing flexible functional form, but restriction on correlation coefficient may flip the sign (and reduce the likelihood)