Volatility Forecasting Using Threshold Heteroskedastic Models of the Intra-day Range
Cathy W. S. Chen
Feng Chia University - Department of Statistics; Graduate Institute of Statistics & Actuarial Science, Feng Chia University
Richard H. Gerlach
University of Sydney
Edward M.H. Lin
Graduate Institute of Applied Statistics, Feng Chia University
May 27, 2009
An effective approach for forecasting return volatility via threshold nonlinear heteroskedastic models of the daily asset price range is provided. The return is defined as the difference between the highest and lowest log intra-day asset price. A general model specification is proposed, allowing the intra-day high-low price range to depend nonlinearly on past information, or an exogenous variable such as US market information. The model captures aspects such as sign or size asymmetry and heteroskedasticity, which are commonly observed in financial markets. The focus is on parameter estimation, inference and volatility forecasting in a Bayesian framework. An MCMC sampling scheme is employed for estimation and shown to work well in simulation experiments. Finally, competing range-based and return-based heteroskedastic models are compared via out-of-sample forecast performance. Applied to six international financial market indices, the range-based threshold heteroskedastic models are well supported by the data in terms of finding significant threshold nonlinearity, diagnostic checking and volatility forecast performance under various volatility proxies.
Number of Pages in PDF File: 25
Keywords: size and sign asymmetry, volatility model, conditional autoregressive range (CARR) model, threshold variable, Bayes
JEL Classification: C11, C15, C22, C51, C52working papers series
Date posted: May 28, 2009
© 2013 Social Science Electronic Publishing, Inc. All Rights Reserved.
This page was processed by apollo2 in 0.359 seconds