Forecasting Volatility with Encompassing and Regime Dependent GARCH Models

Journal of Financial Management and Analysis, Vol. 18, No. 2, 2005

Posted: 5 Aug 2008

See all articles by Larry Bauer

Larry Bauer

Memorial University of Newfoundland (MNU) - Faculty of Business Administration

Steve Beveridge

Independent

Abstract

This paper develops the Regime Dependent Generalized Autoregressive Conditional Heteroskedasticity (RD-GARCH) model and applies it to a daily index of returns on U.S. equities. The RD-GARCH model is different from previous models in that it combines Hentschel's single specification that nests several of the more popular extensions to the GARCH model with a general approach that allows model parameters to vary across periods of differing unconditional volatility. The out-of-sample forecasting performance of the RD-GARCH methodology is found to be superior to a number of alternative models. The sensitivity of forecast accuracy to the distributional assumption (normal, Student-t, generalized error) and to the length of model calibration period is also evaluated.

Keywords: GARCH, Regime dependent, Forecasting, Forecast comparisons, Non-normal distribution

JEL Classification: C16, C22, C52, C53, E32, E37, N12

Suggested Citation

Bauer, Larry and Beveridge, Steve, Forecasting Volatility with Encompassing and Regime Dependent GARCH Models. Journal of Financial Management and Analysis, Vol. 18, No. 2, 2005, Available at SSRN: https://ssrn.com/abstract=635241

Larry Bauer (Contact Author)

Memorial University of Newfoundland (MNU) - Faculty of Business Administration ( email )

St. John's, NL A1B 3X5
Canada
709-864-8512 (Phone)

Steve Beveridge

Independent

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Abstract Views
942
PlumX Metrics