Research on Realized Kernel Method in China Stock Markets

This paper studies the estimated accuracy of RK and RV methods on the basis of simulated path using Monte Carlo method. Simulation shows that RK method can eliminate the influence of noise effectively and the result of estimation is closer to true volatility. Moreover, the RK method and ARFIMA model are combined to estimate and forecast the volatility in China Stock Markets based on the high-frequency data and modified algorithm of fraction order difference. The results show that RK method has better applicability in China Stock Markets with better predictive validity compared with RV method.