Abstract
The volatility became an important index in Investment Science. Since the volatility attracts attention, much effort has been put into adding the volatility forecast accuracys. The generalized autoregressive conditional heteroskedasticity (GARCH) models are used the volatility forecast widely. They rely on the assumption of distribution function, therefore the volatility forecast may be error if distribution function changes with time. By contrast, Taylor proposed method of volatility forecasts from conditional autoregressive value at risk (CAViaR) models in 2005. Those models need not assume the distribution function. Many kinds of CAViaR models are presented, however the volatility forecast from Asymmetric Slope CAViaR (ASCAViaR) model is the most accurately. In the existing study, ASCAViaR model has constant expected value. This study aimed at adding the volatility forecast accuracy, and proposed changed expected value ASCAViaR model. This model has changeability expected value. This study compared the volatility forecast accuracy from changed expected value ASCAViaR model with those from existing ASCAViaR model and GARCH model. This study used three stock indices (the Japanese JASDAQ, the Japanese TOPIX and the U.S. S & P). For all indices, there were two forecast periods (10days and 20days).
