1. PERFORMANCE ASSESSMENT OF AGRICULTURAL FUTURES MARKETS IN INDIA JATINDER BIR SINGH NCDEX Institute of Commodity Markets and Research (NICR )

2. Need for study???  Almost three and half years of futures trading in agricultural commodities  Agricultural futures are more relevant in an agrarian economy like India  To know clearly how have they fared? Do they need some change? What are their effects on prices?  What ails futures trading in agricultural commodities?

3. EMPIRICAL QUESTIONS  To look into the inter-relationship between the spot and futures markets for the agricultural commodities.  Study the role of the different participants in the futures market and the inter- relationship between the commercial hedgers and the speculators.  Forecasting ability of futures prices  Hedging efficiency of agricultural futures markets  Price influence on physical markets.

4. DATA  Daily futures prices and spot prices of agricultural commodities-pepper, soya oil, jeera, chana, guar seed, mentha oil, castorseed, wheat, from NCDEX, MCX and NMCE  Also production and imports data to look at total supply  Hedging limits utilized by hedgers  Primary spot price data in maturity month

5. Hypothesis Convergence of spot and futures  Arbitrage should force convergence and basis should approach zero at expiration. So no basis risk and no need to predict convergence.  If perfect convergence doesn’t exist it means existence of delivery options and costs of arbitration

6. Methodology Predictability of Basis Return to short hedger=X(B2-B1) Convergence of SP and FP at maturity of contract B t = + B1 t +  t Regress change in basis (B2-B1) on initial basis (B1)—slope=-1, intercept=0 Initial basis (B1) =basis immediately after the expiry of preceding contracts Final basis(B2) =1 st trading day of expiration month and delivery day

7. Arbitrage Principle-Mispricing  X t,T =[F t,T -S t e (r+s-d)(T-t) ] is difference between FP and theoretical spot price  Departure of X t,T outside a range means lack of arbitrage capital.  Is Mispricing time dependent on maturity

8. Likely explanation--?  Futures prices quote higher than fundamental values (acc g to stock-to-use ratio)  Wrong polling methods-not representative spot prices  Prices quoted refer to different quality  Cartel among traders

9. Futures Prices as Forecasts F t+i = + F t +  t+i where Ft is forecast and Ft+i is actual price realization Or alternatively F t+i - F t = + (-1)F t +  t+i where i can be maturity or can be before maturity. This is forecast evaluation tool. Week-form efficiency: = -1=0 Current price is the best estimate of coming price and has no ability to forecast prices.

10. Forecasting Efficiency Evaluation equation P t+i - P t = + (F t -P t )+  t+i (1) or P t+i = + F t -P t +  t+i (2) Where =1-  The evaluation eqn.(2) have large R 2 but may have little or no ability to forecast price change. Same with basis equation (1)

11. Forecasting Efficiency-technical issues  Appraisal of efficiency of different maturity months separately vs. judging efficiency of all pooled contracts in one equation.  The effects of outliers  The nature of price distribution including the possibility of nonstationary series  The possibility of bias of OLS estimator in fitting models with a lagged endogeneous variables If Jeera actual December 2007 prices are underestimated by October futures Prices, doesn’t mean market is inefficient.

12. HEDGING EFFICIENCY

14. Hedging effectiveness

18. PRICE INFLUENCE  Hypothesis: Futures prices influences inventory decisions and hence stabilize effect on spot prices  Methodology: n it M it =(  (p itj –p itj-1 ) 2 / n it ) 1/2 j=1 where M it is the volatility of month i in year t (monthly volatility of weekly changes), n it are number of the weeks in month i in year t and p itj is the price in week j in month i in year t.  Normalized variance V it = M it / p it where p it is average monthly price.

19. PRICE INFLUENCE 11  In V t =a + b InP t +c j d jt +a*D*+ b*(D*In P t )+ j=1 11 +  c j *(D* d jt ) +  j=1 where d j are monthly dummy variables, where j=1,2,3,………11 denote the eleven dummies for 12 months of the season. D* stand for the dummy for the period 2004-2007. Use model selection criteria-AIC, SBC

20. Price Inluence  d j can tell us about seasonal nature of volatility---- Does futures trading help changing intra-seasonal volatility.  Most important issue is inter-year price variability. Futures markets encourage rate of storage & hence stabilize spot prices. We can also look at intertemporal price relationships to know the effects of futures markets on allocating inventories with in a year. Improvements- We can use time varying volatility; changing the length of estimation window. Can use rolling estimation-extending estimation by one period. To take time-dependence we use exponentially weighted volatility estimates.

21. THANK-YOU