PERFORMANCE ASSESSMENT OF AGRICULTURAL FUTURES MARKETS IN INDIA
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Transcript of 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 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