Raushan Kumar
Department of Economics
Delhi School of Economics
University of Delhi
March 15, 2019
Predicting Wheat Futures Prices in India
“Derivatives are an extremely efficient tool for risk management”
“Derivatives are financial weapons of mass destruction”
-Warren Buffett
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Motivation
• Futures price, spot price, future spot price
• Economic reforms and price risk
• Two important functions of futures market
Price discovery & hedging
• Commodity futures trading in India
• Benefits to farmers
Cropping pattern & investments
Maximisation of income realisation
Stronger spatial integration between spot markets3
Contd.
• Efficient market features
No free lunch
Market reveals all available information
Movement of prices is random
No profitable trading strategy
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Forecasting futures prices
• Fundamental view
Supply & demand factors
Intrinsic value
Probability of price movement
Highly skilled professional traders
• Technical view
Studies the market itself
Ignores supply & demand
Brokers, common people5
Forecasting futures prices
• If futures market highly efficient - fundamental trading &
technical trading are worthless
• Market partially efficient
Traders drive market close to efficiency
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Contd.
• Forecasting futures price, not spot price
• Traders, mill owners, speculators make their expectation about
futures prices
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Literature on futures market forecasting
• Bassembinder and Chan (1992)
• Miffre (2001b) - Whether agricultural & metal futures can be
predicted
• Konstantinidi & Skiadopoulos (2011) - Whether VIX futures
can be predicted
• Existing literature focuses on US and few other developed
markets8
• Futures markets in India are different from agricultural futures
market of developed economies
Indian agricultural futures markets are at a nascent stage
Weaknesses in spot markets
Government intervention in agricultural futures markets
Unlike China, there is no compulsory delivery of agricultural
commodities on expiry of futures contracts
Evidence on financialization of agricultural commodity
markets of developed countries
Contd.
Research Question
Whether wheat futures prices per se can be predicted
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Economic model
• 𝐹𝑃𝑤𝑡 = 𝛼0 + 𝛼1𝐹𝑃𝑤(𝑡−1) + 𝛼2𝐹𝑃𝑔 𝑡−1 + 𝛼3𝑖𝑡−1 + 𝛼4𝑏𝑎𝑠𝑖𝑠𝑡−1 +
𝛼5𝐹𝑃𝑈𝑆_𝑤(𝑡−1) + 𝛼6𝑅𝑡−1 + 𝜀𝑡
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Variable Definition Expected sign
𝐹𝑃𝑤 Futures price of wheat
𝐹𝑃𝑔 Futures price of gram +/-
𝑖 Real rate of interest -
𝐵𝑎𝑠𝑖𝑠 Basis (difference between spot and futures
prices)
-
𝐹𝑃𝑈𝑆_𝑤 Futures price of wheat in USA +
𝑅 Ratio of high price to low price of wheat futures +
Robustness check
• Random walk (RW) - bench mark
• ARMA(1,1)
• ARMA(1,2)
Two best fitted model among ARMA(P,Q)
• Neural Network – same explanatory variables as economic
model
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0
2
1
5
4 8
7
6
Input layer Hidden layer Output layer
3
9
Artificial Neural Network
𝐼7
𝐼6
𝐼8
𝐼5
𝐼4
𝐼3
𝐼2
𝐼1
𝐼0 𝐼6 = 𝑊06𝑂0 +𝑊16𝑂1 +𝑊26𝑂2 +𝑊36𝑂3 +𝑊46𝑂5 +𝑊56𝑂5
𝐼9
𝑂5
𝑂4
𝑂3
𝑂2
𝑂1
𝑂0
𝑂8
𝑂6
𝑂7 𝑂9
𝑂6 =1
1 + 𝑒−𝐼6
𝜃9
𝐼9 = 𝑊69𝑂6 +𝑊79𝑂7 +𝑊89𝑂8 +𝜃9
𝑂9 =1
1 + 𝑒−𝐼9
Forecast
• Out of sample forecast preferred
• Recursive forecast
Sample period from 21 May 2009 to 28 August 2014
Estimate using sample observations spanning 21 May
2009 to 4 March 2014, and obtain the ‘first’ out-of-sample
forecast for 5 March 2014
Subset from 5 March 2014 to 28 August 2014, is used for
the out-of-sample forecast evaluation
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Data
• Daily data
Deflated & deseasonalised
Nearby contract
• Data sources
Futures price of wheat and gram – MCX & NCDEX
Rate of interest – RBI
Futures price of wheat in USA – Bloomberg data base
CPI of US - FRED
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Forecasting with the economic variables model
𝐹𝑃𝑤𝑡 = 𝛼0 + 𝛼1𝐹𝑃𝑤(𝑡−1) + 𝛼2𝐹𝑃𝑔 𝑡−1 + 𝛼3𝑖𝑡−1 + 𝛼4𝑏𝑎𝑠𝑖𝑠𝑡−1 + 𝛼5𝐹𝑃𝑈𝑆_𝑤(𝑡−1) + 𝛼6𝑅𝑡−1 + 𝜀𝑡
Variable Coeff.
Constant -7.209* (4.284)
𝐹𝑃𝑤(𝑡−1) 0.032 (0.035)
𝐹𝑃𝑔 𝑡−1 -0.033 (0.024)
𝑖𝑡−1 -0.086 (0.076)
𝑏𝑎𝑠𝑖𝑠𝑡−1 -0.129 (0.647)
𝐹𝑃𝑈𝑆_𝑤(𝑡−1) 3.162** (1.236)
𝑅𝑡−1 7.176* (4.206)
Obs. 800***, **, and * indicate statistical significance at the 1%, 5% and 10% levels, respectively.
Standard errors are reported in parentheses16
Forecasting with univariate ARMA
ARMA(1,1) ARMA(1,2)
𝐹𝑃𝑤𝑡 = 𝛼0 + 𝛼1𝐹𝑃𝑤(𝑡−1) + 𝜃1𝜀𝑡−1 + 𝜀𝑡 𝐹𝑃𝑤𝑡 = 𝛼0 + 𝛼1𝐹𝑃𝑤(𝑡−1) + 𝜃1𝜀𝑡−1 + 𝜃2𝜀𝑡−2 + 𝜀𝑡
𝛼0 0.032 (0.030) 𝛼0 0.316 (0.308)
𝛼1 0.680*** (0.250) 𝛼1 0.630** (0.301)
𝜃1 -0.636** (0.263) 𝜃1 -0.598** (0.302)
𝜃2 0.022 (0.041)
N 800 N 800
***, **, and * indicate statistical significance at the 1%, 5% and 10% levels, respectively.
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Some tests of forecast accuracy
RW Economic
Model
ARMA
(1,1)
ARMA
(1,2)
Artificial Neural
Network
RMSE 0.6948 0.7101 0.6975 0.6986 0.7169
MAE 0.4627 0.4766 0.4673 0.4678 0.4999
Theil’s U 1.022 1.003 1.005 1.031
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On the basis of RMSE and MAE, RW provides the most
accurate forecast
Diebold-Mariano test
𝐻0: RW & the model under consideration perform equally
well
𝐻1: RW outperforms the model
RW Economic
Model
ARMA
(1,1)
ARMA
(1,2)
Neural
Network
RMSE 0.6948 0.7101* 0.6975 0.6986* 0.7169*
MAE 0.4627 0.4766** 0.4673* 0.4678* 0.4999***
***, ** & * indicate statistical significance at the 1%, 5% and 10%
levels, respectively.
Conclusion and Policy Implications
• Wheat futures market is efficient (statistically), hence, can not
be forecast
• RW outperforms other models
• Addition to existing literature
Forecasted futures price of wheat
Neural network forecasting technique
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• Policy implications
Fundamental view holders will continue trading
Hedging against changes in spot prices
If 𝐹𝑡−1 < > 𝐹𝑡 , then buy (sell)
If 𝐹𝑡−1 = 𝐹𝑡 , then do nothing
• Future plan of action
Economic significance of forecast
Interval forecast
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Thank You
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