Modeling and forecasting of stock index volatility with APARCH models under ordered restriction

Milton Abdul Thorlie, Lixin Song, Muhammad Amin and Xiaoguang Wang

Statistica Neerlandica, 2015, vol. 69, issue 3, 329-356

Abstract: type="main">

This article examines volatility models for modeling and forecasting the Standard & Poor 500 (S&P 500) daily stock index returns, including the autoregressive moving average, the Taylor and Schwert generalized autoregressive conditional heteroscedasticity (GARCH), the Glosten, Jagannathan and Runkle GARCH and asymmetric power ARCH (APARCH) with the following conditional distributions: normal, Student's t and skewed Student's t-distributions. In addition, we undertake unit root (augmented Dickey–Fuller and Phillip–Perron) tests, co-integration test and error correction model. We study the stationary APARCH (p) model with parameters, and the uniform convergence, strong consistency and asymptotic normality are prove under simple ordered restriction. In fitting these models to S&P 500 daily stock index return data over the period 1 January 2002 to 31 December 2012, we found that the APARCH model using a skewed Student's t-distribution is the most effective and successful for modeling and forecasting the daily stock index returns series. The results of this study would be of great value to policy makers and investors in managing risk in stock markets trading.

Date: 2015
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