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

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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. Motivation: Volatility in Dairy Sector. Motivation: How to Model Agricultural Prices. - PowerPoint PPT Presentation

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

Price Discovery, Volatility Spillovers and Adequacy of Speculation in Cheese Spot and Futures Markets

Price Discovery, Volatility Spillovers and Adequacy of Speculation in Cheese Spot and Futures MarketsMarin BozicUniversity of Minnesota-Twin Cities

NDSU Seminar, 10/28/2011

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Motivation: Volatility in Dairy Sector2

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Motivation: How to Model Agricultural Prices4

Motivation: How to Model Speculative Influence?Volatility in the Dairy Sector: Why?SDDQuantityPrice5Volatility in the Dairy Sector: Why?6

Dealing with High VolatilityPrice Support ProgramsMilk Income Loss Contract7

Catastrophic Insurance (LGM-Dairy)Market-based instruments: Dairy Futures & Options, OTCs

Herd Termination ProgramsSocial InsuranceSupply Management

Purpose of this paperWhere does the new information about prices originate?Are there volatility spillovers between dairy markets?Did speculators contribute to rising volatility in the market? 8Pricing Milk in the U.S. : 1. Government9

Spot market trades daily for 15 minutes each morning.No cash market for dry whey or milk.

10Pricing Milk in the U.S. : 2. CME Cash Market

Thin Slicing11

Markets are very thinUSDA reports results of daily trading as well as weekly averagePrices for cheese used as benchmark in setting prices in direct transactions across the nation

12Pricing Milk in the U.S. : 3. CME Futures MarketClass III Milk Futures: Comparing mid-October liquidity 2000-201113

Functions of the futures market: Price Discovery14

Questions of interestHow do futures and cash market for cheese interact?Price discoveryVolatility spilloversImpact of speculation on dairy futures15A typical modeling approachTest if cash and futures are stationaryIf yes: VARIf no: Co-integrationVolatility spillovers:If high-frequency: realized volatility/VARIf low-frequency: GARCHEffects of speculationIf high-frequency: additional regressor in VARIf low-frequency: BEKK-X, EGARCH-X

16VAR vs. co-integration17

Case 1: Variables of interest are stationary (no persistent shocks)Instruction: Build a vector autoregressive modelCase 2: Variables are non-stationary (some shocks are persistent)Instruction: Build a co-integration modelData limitationsCash market is thinClosing price may indicate unfilled bid/uncovered offerNo cash market for manufacturing grade milk or dry wheyFutures marketCheese futures market did not exist until 07/2010Data on speculative positions available only weekly18Implied Cheese Futures

19Implied vs. observed cheese futures20

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Creating Nearby Futures Price SeriesUnit root tests of cheese cash and futures time seriesAugmented Dickey-Fuller (Said and Dickey, 1984)

Null: : (unit root present; no drift)2. Phillips-Perron (1988):

Null: alpha=0, rh 1

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Unit Root Tests Results: Cash Cheese23

Unit Root Tests Results: Cheese Futures

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Devil is in the details: accounting for past lagged differenced futures25

Unit Root Tests Results: Cheese Futures

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Making sense of unit root results: 1. Economic TheoryCash price analysis based on production theoryPerfect competition: zero long-run economic profit for the marginal producerProfit margin will be a mean-reverting time seriesIf long-run industry average cost curve is flatPermanent shifts in demand temporary shifts to cash pricesPermanent changes in input prices structural changeIf supply is inelastic in short run high persistency of shocksIf long-run AC curve is sloped Permanent shifts in demand permanent shocks to cash price series27

Making sense of unit root results: 1. Economic TheoryFutures price analysis based on finance theoryEfficient market prices in a single contract will be martingales if the marginal risk premium is zero; submartingales (downward biased) if marginal risk premium is positiveSupermartingales (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

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Making sense of unit root results: 2. Time Series Modeling ExerciseWhat if there was a market in which cash price was indeed second-order stationaryIf 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 premium29Making sense of unit root results: 2. Time Series Modeling Exercise

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Making sense of unit root results: 2. Time Series Modeling Exercise - Results31Martingale Property within each contract

Nearby series not a martingale

Making sense of unit root results: 2. Time Series Modeling Exercise -What would Unit Root Tests Show?32Cash Prices: 1) Null would likely be rejectedFutures prices: 2) for a single contract, null would likely not be rejected3) Null more likely to be rejected for n-th than for n+1 nearby series4) More obs. between rollover periods null less likely to be rejected (reducing data frequency increases likelihood of rejecting the null)

Unit Root Tests: ConclusionsCash Cheese is mean revertingNearby cheese futures are nonlinearUnit-root processes within each contractMean-reverting at contract rollover

Next: How to model this?

33Modeling information flowsCausality in mean

Second-order causality (causality in variance)34

Second order non-causalityGranger 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

35GARCH-BEKK and second-order non-causality36

Adding speculatorsThe key problem is how to preserve positive definiteness of conditional variance matrixAdding another term?

Sign of the impact of additional regressor is restricted to be positive but we must have flexibility!

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GARCH-MEX

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GARCH-MEX

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Measuring Adequacy of SpeculationBased on Working (1960) Workings TThe 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

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Measuring Adequacy of SpeculationLikewise, 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.

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Results: Information flows in mean42

Results: Information flows in mean43

Results: Information flows in mean44

Results: Information flows in mean45

Results: Information flows in meanConclusion: 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 market46Results: volatility spillovers

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

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ConclusionsNot 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)

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