Volatility Forecasting Using Threshold Heteroskedastic Models of the Intra-day Range

Abstract

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.