Performance Analysis of Hybrid Forecasting models with Traditional ARIMA Models - A Case Study on Financial Time Series Data

Abstract

ARIMA and GARCH models in their various flavors are frequently used in modeling of real world financial time series. Often, those models do not produce the best possible results in terms of modeling and forecasting. Of late, researchers across the world have gone for hybrid models. In principle, hybrid models bring the best out of both worlds. The modeling and forecasting ability of ARFIMA-FIGARCH model is investigated in this study. It is widely agreed that financial time series data like stock index exhibit a pattern of long memory. Short term and long term influences are also observed. Empirical investigation has been made on ten such stock indices comprising of various segments of Indian stock data. The obtained results clearly illustrate the modeling power of ARFIMA-FIGARCH. The performance of this model is compared with traditional Box and Jenkins ARIMA models. The results obtained illustrate the need for hybrid modeling. ARFIMA-FIGARCH is compared with seven different flavors of ARIMA and ARFIMAFIGARCH emerges as the clear winner.