The nonparametric GARCH model estimation using intraday high-frequency data

Abstract

Most of nonparametric GARCH models typically employ daily frequency data to forecast the returns, correlations, and risk indicators of financial assets, without incorporating alternative frequency data. As a result, valuable financial market information may remain underutilized during the estimation process. To partially mitigate this issue, we introduce the intraday high-frequency data to enhance the estimation of the volatility function in a nonparametric GARCH model. To achieve this objective, we introduce a nonparametric proxy model for volatility. Under mild assumptions, we derive the asymptotic bias and variance of the estimator and further investigate the impact of various volatility proxies on estimation accuracy. Our findings from both simulations and empirical analysis indicate a considerable improvement in the estimation of the volatility function through the introduction of high-frequency data.