Research on multiresolution recognition of risk and portolio

This paper presents a new method for multiresolution Betas estimation based on wavelet multiresolution analysis, which decomposes the variance of one time series and the covariance between two time series on a scale by scale basis through maximal overlap discrete wavelet transform. The composition of risk at different scales for high-frequency financial assets is discussed by the method. In accordance with multiresolution character of return and risk, multiresolution portfolio tactics are put forward, which better static portfolio method of Markowitz. The empirical results show that the proposed methods not only capture character of multiresolution, but also put investment risk at different scales down to the minimum level.