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PERFORMANCE ASSESSMENT OF AGRICULTURAL FUTURES MARKETS IN INDIA

PERFORMANCE ASSESSMENT OF AGRICULTURAL FUTURES MARKETS IN INDIA. JATINDER BIR SINGH NCDEX Institute of Commodity Markets and Research (NICR ). Need for study???. Almost three and half years of futures trading in agricultural commodities

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PERFORMANCE ASSESSMENT OF AGRICULTURAL FUTURES MARKETS IN INDIA

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  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. HypothesisConvergence 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 Bt= + B1t+ 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) =1st trading day of expiration month and delivery day

  7. Arbitrage Principle-Mispricing • Xt,T=[F t,T-Ste(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 (accg 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 Ft+i = + Ft+ t+i where Ft is forecast and Ft+i is actual price realization Or alternatively Ft+i- Ft = + (-1)Ft+ 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 Pt+i- Pt = + (Ft-Pt )+ t+i (1) or Pt+i= + Ft-Pt + t+i (2) Where =1-  The evaluation eqn.(2) have large R2 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

  13. HEDGING EFFICIENCY

  14. Hedging effectiveness

  15. Hedging effectiveness

  16. Hedging effectiveness

  17. PRICE INFLUENCE • Hypothesis: Futures prices influences inventory decisions and hence stabilize effect on spot prices • Methodology: nit Mit =(  (pitj –p itj-1)2 / nit )1/2 j=1 where Mit is the volatility of month i in year t (monthly volatility of weekly changes), nit are number of the weeks in month i in year t and pitj is the price in week j in month i in year t. • Normalized variance Vit = Mit / pit where pit is average monthly price.

  18. PRICE INFLUENCE 11 • In Vt=a + b InPt +cjdjt +a*D*+ b*(D*In Pt)+ j=1 11 +  cj*(D* djt) +  j=1 wheredjaremonthly 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

  19. Price Inluence • dj 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.

  20. THANK-YOU

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