predicting wheat futures prices in india · raushan kumar department of economics delhi school of...

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

4

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

7

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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