Modelling Long Memory Volatility in Agricultural Commodity Futures Returns
This paper estimates the 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), FIEGACH model of Bollerslev and Mikkelsen (1996), 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.
|Keywords||agricultural commodity futures, asymmetric, conditional volatility, fractional integration, long memory|
|JEL||C22, Time-Series Models; Dynamic Quantile Regressions (jel), C51, Model Construction and Estimation (jel), Q11, Aggregate Supply and Demand Analysis; Prices (jel), Q14, Agricultural Finance (jel)|
|Publisher||Erasmus School of Economics (ESE)|
Tansuchat, R, Chang, C-L, & McAleer, M.J. (2009). Modelling Long Memory Volatility in Agricultural Commodity Futures Returns (No. EI 2009-35). Report / Econometric Institute, Erasmus University Rotterdam (pp. 1–34). Erasmus School of Economics (ESE). Retrieved from http://hdl.handle.net/1765/17298