Evaluating and improving GARCH-based volatility forecasts with range-based estimators

Jui-Cheng Hung, Tien-Wei Lou, Yi-Hsien Wang and Jun-De Lee

Applied Economics, 2013, vol. 45, issue 28, 4041-4049

Abstract: This article investigates the feasibility of using range-based estimators to evaluate and improve Generalized Autoregressive Conditional Heteroscedasticity (GARCH)-based volatility forecasts due to their computational simplicity and readily availability. The empirical results show that daily range-based estimators are sound alternatives for true volatility proxies when using Superior Predictive Ability (SPA) test of Hansen (2005) to assess GARCH-based volatility forecasts. In addition, the inclusion of the range-based estimator of Garman and Klass (1980) can significantly improve the forecasting performance of GARCH- t model.

Date: 2013
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DOI: 10.1080/00036846.2012.748179

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