Application of Time Series Models (ARIMA, GARCH, and ARMA-GARCH) for Stock Market Forecasting

摘要

This paper examines efficacy and limitations of time series models, namely ARIMA, GARCH, and ARMA-GARCH for stock market returns forecasting. First, the paper assesses the unique features of financial data, particularly volatility clustering and fat-tails of the return distribution, and addresses the limitations of using autoregressive integrated moving average (ARIMA) models in financial economics. Secondly, it examines the application of ARMA-GARCH models for forecasting of both conditional means as well as the conditional variance of the returns. Finally, using the standard model selection criteria such as AIC, BIC, SIC, and HQIC the forecasting performance of various candidate ARMA-GARCH models was examined. Using excess returns of MSCI World Index and excess returns from Fama-French 3-factor-model, it was found that an ARMA (1,0) + GARCH (1,1) consistently yields best results in-sample for the same period across both datasets, while showing some forecasting limitations out-of-sample.