MODELLING LONG MEMORY VOLATILITY IN AGRICULTURAL COMMODITY FUTURES RETURNS

Chia‐Lin Chang · Michael J. McAleer · Roengchai Tansuchat

34 citations0/0 files storedAnnals of Financial Economics

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Financial Risk and Volatility Modeling Stochastic processes and financial applications Market Dynamics and Volatility

Abstract

This paper estimates a long memory volatility model for 16 agricultural commodity futures returns from different futures markets, namely corn, oats, soybeans, soybean meal, soybean oil, wheat, live cattle, cattle feeder, pork, cocoa, coffee, cotton, orange juice, Kansas City wheat, rubber, and palm oil. The class of fractional GARCH models, namely the FIGARCH model of Baillie et al. (1996), FIEGARCH model of Bollerslev and Mikkelsen (1996), and FIAPARCH model of and FIAPARCH model of Tse (1998), are modelled and compared with the GARCH model of Bollerslev (1986), EGARCH model of Nelson (1991), and APARCH model of Ding et al. (1993). The estimated d parameters, indicating long-term dependence, suggest that fractional integration is found in most of agricultural commodity futures returns series. In addition, the FIGARCH (1, d, 1) and FIEGARCH (1, d, 1) models are found to outperform their GARCH (1, 1) and EGARCH (1, 1) counterparts.

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References

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