Docstoc

Price Discovery_ Volatility Spillovers and Adequacy of speculation in cheese spot and future market .pptx

Document Sample
Price Discovery_ Volatility Spillovers and Adequacy of speculation in cheese spot and future market .pptx Powered By Docstoc
					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


                                           1
Motivation: Volatility in Dairy Sector




                                         2
    Motivation: How to Model Agricultural Prices




3
Motivation: How to Model Speculative Influence?




4
Volatility in the Dairy Sector: Why?

   Price

                 S




                     D′



                 D

                              Quantity

                                         5
Volatility in the Dairy Sector: Why?




                                   6
      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          7
         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?



                                                8
Pricing Milk in the U.S. :
     1. Government




                             9
       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.

                                           10
                        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
                                                                    11
Pricing Milk in the U.S. :
3. CME Futures Market




                             12
         Class III Milk Futures:
Comparing mid-October liquidity 2000-2011




                                       13
Functions of the futures market: Price
              Discovery




                                     14
           Questions of interest
• How do futures and cash market for cheese
  interact?
  – Price discovery
  – Volatility spillovers
• Impact of speculation on dairy futures




                                              15
     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
                                                      16
             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




                                                                      17
              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
                                                        18
Implied Cheese Futures




                         19
Implied vs. observed cheese futures




                                      20
Creating Nearby Futures Price Series




                                       21
   Unit root tests of cheese cash and
           futures time series
1. Augmented Dickey-Fuller (Said and Dickey,
   1984)


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


Null: alpha=0, rh 1
                                                     22
Unit Root Tests Results: Cash Cheese




                                       23
Unit Root Tests Results: Cheese Futures




                                     24
Devil is in the details: accounting for
  past lagged differenced futures




                                          25
Unit Root Tests Results: Cheese Futures




                                     26
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            27
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
                                                     28
  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

                                                 29
Making sense of unit root results:
2. Time Series Modeling Exercise




                                     30
     Making sense of unit root results:
2. Time Series Modeling Exercise - Results
1. Martingale Property within each contract




2. Nearby series not a martingale


                                              31
        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)
                                                             32
    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?



                                               33
     Modeling information flows
Causality in mean




Second-order causality (causality in variance)



                                                 34
     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
                                                     35
GARCH-BEKK and second-order non-
           causality




                                   36
           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!                                    37
GARCH-MEX




            38
GARCH-MEX




            39
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

                                                    40
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.
                                                  41
Results: Information flows in mean




                                 42
Results: Information flows in mean




                                 43
Results: Information flows in mean




                                 44
Results: Information flows in mean




                                 45
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




                                                   46
   Results: volatility spillovers




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




                                 48
                 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)

                                                     49
50

				
DOCUMENT INFO
Shared By:
Categories:
Tags:
Stats:
views:5
posted:2/12/2014
language:English
pages:50