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 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?

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.

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

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

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

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

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

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=0Current price is the best estimate of coming

price and has no ability to forecast prices.

Forecasting Efficiency

Evaluation equationPt+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)

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.

HEDGING EFFICIENCY

Objective function for the risk averse hedger, aiming to

reduce price variability, is:

)()(..

)(2)()var()(min0

,*2

ftfstH

ftstffftfsttH

PEXPEPts

PCovXXPVarXPPVar

Hedge Ratio-

)var(

)( ,*

f

fsf P

PPCovX

HEDGING EFFICIENCYSummary Statistics of Soya oil prices at NBOT, MCX and NCDEX

Mean Standard

Deviation

Ratio*

Spot soya oil prices 6.009347 .1001749

Futures prices at NBOT 6.013712 .0981364

Futures prices at MCX 6.012809 .0968719

Futures prices at NCDEX 6.00159 .0945086

NBOT Basis -.0020451 .0121541 .12

MCX Basis -.003462 .01416 .15

NCDEX Basis -.0026774 .0127346 .13

The closing futures prices of near-by futures contracts have been used.

* The ratio of standard deviation of basis to that of price.

Hedging effectiveness

ttt eFC Unconditional Hedging Effectiveness Regressions (one-week hedge): Results from Estimating

Equation, ttt eFC , Dec2003-Mar2007

(t-value) (t-value) Adjusted

R2

D.W.

NBOT .0010739 (0.94) .5131054* (21.93) 0.4162 1.828357

MCX .0007703 (0.78) .6121019* (26.22) 0.5049 1.935334

NCDEX .0008049 (0.58) .46626* (18.45) 0.3728 1.694452

CBOT .0004923 (0.59) 1.032738** (89.02) 0.8894 2.189292

* significant different from zero at 5% level

** coefficient is significantly equal to one.

autocorrelation removal though Cochran-Orcutt

Hedging effectivenessUnconditional Hedging Effectiveness Regressions (15 days hedge): Results from

Estimating Equation, ttt eFC , Dec2003-Mar2007

(t-value)

(t-value)

Adjusted R2 D.W.

NBOT .0004187

(0.15)

.5198985*

(21.36)

0.4101 1.943936

MCX .000288

(0.12)

.5843522*

(23.87)

0.4647 2.067550

NCDEX .0002317

(0.07)

.4403861*

(17.29)

0.3484 1.927049

CBOT .0011048

(0.74)

1.025935**

(86.98)

0.8856 2.361693

* significant different from zero at 5% level

** coefficient is significantly equal to one.

autocorrelation removal though Cochran-Orcutt

Hedging effectivenessUnconditional Hedging Effectiveness Regressions (One-month hedge): Results

from Estimating Equation, ttt eFC , Dec2003-Mar2007

(t-value)

(t-value)

Adjusted

R2

D.W.

NBOT .002406

(0.57)

.645622

(30.11)

0.5809 2.101581

MCX .0016056

(0.46)

.69868

(33.17)

0.6266 2.179002

NCDEX .0036912

(0.74)

.5824056*

(24.83)

0.5268 2.012097

CBOT .0025133

(0.73)

1.008466**

(1450.110

0.9995 2.451302

* significant different from zero at 5% level

** coefficient is significantly equal to one.

autocorrelation removal though Cochran-Orcutt

ttiitt eMonthsFC ,

Conditional Hedging Effectiveness Regressions (one-week): Results from estimating equation

ttitt eMonthsFC , , Dec2003-Mar2007

Independent Variable Coefficient NBOT MCX NCDEX

Ft,

(t-ratio)

.5190354*

(22.04)

.6201256*

(26.39)

.4780381*

(18.85)

Constant, -.0001002 .0002579 .000551

January, 1 -.0008066 -.0005387 -.0022342

February, 2 .0027813 .000908 .0003701

March, 3 .002015 .0016685 .0044483

April, 4 .0082014* .0071922* .0135175*

May, 5 .0036146 .0032763 -.0020453

June, 6 .0010036 .0000201 -.0000848

July, 7 .0021446 .001293 -.0007938

August, 8 .0004015 .0003392 -.0040329

September, 9 -.0030796 -.0029093 -.0040296

October, 10 -.0025495 -.0038365 -.0003766

November, 11 ! !

December, 12 -.0012237 -.0025452 -.0022773

Adjusted R2 0.4303 0.5211 0.4079

D-W 1.82 1.93 1.70

* significant different from zero at 5% level

! dropped due to collinearity

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.

PRICE INFLUENCE

11 In Vt=a + b InPt +cjdjt +a*D*+ b*(D*In Pt)+

j=1 11

+ cj*(D* djt) + j=1 where dj 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

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.

THANK-YOU