Oynamic Integration Method For Volatility Estimation

In financial markets,researchers have proposed stochastic diffusion models for the purpose of describing,pricing,and managing financial derivatives under different conditions.In the given model,the selection of model parameters is considered as an important factor directly related to asset pricing,securities adjustment and risk management.The volatility function,as a basic parameter in stochastic diffusion model,is used to measure the return of investment or the fluctuation degree of asset price.The research on it is of great significance to financial markets.In this paper,the basic knowledge and theoretical basis,including the meaning,research significance and the current situation of volatility and the basic theory and properties of local regression method are introduced.Then the volatility function of stochastic diffusion model is studied,and the nonpaxametric estimation technique is introduced into the stochastic diffusion model to improve the estimation methods of time domain and state domain estimators.The estimation methods of single do-main estimators and their concrete forms are discussed in detail respectively and the asymptotic statistical properties of the single domain estimators and the asymptot-ic independence of the two domain estimators are established under the reasonable assumptions.Based on these,the improved dynamic weight selection method is proposed,and the dynamic integrated volatility estimator of time domain and state domain is proposed.Finally,the numerical simulation and empirical analysis show that the proposed method has a better performance than the previous method.