Modelling long memory in volatility for weekly jute prices in the Malda district, West Bengal

Abstract

In this study, the presence of long memory in the volatility process of weekly jute prices in the Samsi and Gajol markets of the Malda district (West Bengal) for the period of January 2009 to December 2022 has been investigated. For this objective, the ARCH-LM test and Hurst rescaled range (R/S) analysis are used to determine the ARCH effect and long memory in the volatility process for the series, and the results indicate the presence of the ARCH effect and long memory in conditional variance. Accordingly, the GARCH and FIGARCH models have been applied for modelling and forecasting the volatility of the jute prices. The wavelet method has been used to estimate the fractional difference parameter in the FIGARCH model. The AR (1)-GARCH (1, 1) and AR (1)-FIGARCH (1,0.270,1) models for the Samsi market and the AR (1)-GARCH (1,1) and AR (1)-FIGARCH (2,0.284,1) models for the Gajol market are found suitable at the training stage based on their minimum AIC and BIC. The forecasting performance of these models was evaluated in the validation period with the help of Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) criteria, and the residuals were examined to ensure that the fitted models were adequate. Finally, the AR (1)-FIGARCH (1,0.270,1) and AR (1)-FIGARCH (2,0.284,1) are found to be the best optimal models for forecasting the jute prices in the Samsi and Gajol markets, respectively.