Bayesian Forecasting on Intraday Volatility with Nonlinear GARCH Models
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초록
The purpose of this paper is to apply a Bayesian method to simulate the modified nonlinear GARCH models. In addition, the paper specifies the forecast performances on several GARCH models. The main findings and implications are as follows:First, forecast performances on most of GARCH models are not significantly different across the experiments with different sample sizes, however, the performances are different across forecast points. This finding indicates that if one has enough observations then she needs not extend observation to forecast intraday volatilities. Second, the forecast power of all models is different across forecast time points, which represents that the forecast power of the GARCH models are not stable. The price volatility in UHF data is non-stationary so that a proper forecast model may depend on the forecast time points. Third, the more complicated model, the more required computing time. However, the difference of computing time across models is not significant so that t-distribution models can be applied to the forecast of intraday volatility.
Abstract
The purpose of this paper is to apply a Bayesian method to simulate the modified nonlinear GARCH models. In addition, the paper specifies the forecast performances on several GARCH models. The main findings and implications are as follows:First, forecast performances on most of GARCH models are not significantly different across the experiments with different sample sizes, however, the performances are different across forecast points. This finding indicates that if one has enough observations then she needs not extend observation to forecast intraday volatilities. Second, the forecast power of all models is different across forecast time points, which represents that the forecast power of the GARCH models are not stable. The price volatility in UHF data is non-stationary so that a proper forecast model may depend on the forecast time points. Third, the more complicated model, the more required computing time. However, the difference of computing time across models is not significant so that t-distribution models can be applied to the forecast of intraday volatility.
참고문헌 (20)
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