Forecasting Financial Volatility: An Exogenous Log-GARCH Model

• Activity Number: 396
• Type: Contributed
• Date/Time: Tuesday, August 5, 2014 : 2:00 PM to 3:50 PM
• Sponsor: Business and Economic Statistics Section
• Author(s): Ming Chen*+ and Qiongxia Song
• Companies: University of Texas at Dallas and University of Texas at Dallas
• Keywords: financial volatility ; log-GARCH ; exogenous variable ; semi-parametric regression ; spline ; quasi likelihood estimation

Abstract

In this article, we develop a new model for nancial volatility estimation and forecasting by including exogenous variables in a semi-parametric log-GARCH model.With additional information, we expect to gain an increased prediction power. We propose a quasi maximum likelihood procedure via spline smoothing technique. Consistent estimators and asymptotic normality are obtained under mild regularity conditions.Simulation experiments provide strong evidence that corroborates the asymptotic theories. Additionally, an application to S&P 500 index data demonstrates strong competitive advantage of our model comparing with GARCH(1,1) and log-GARCH(1,1)models.