Modeling Financial Time-Varying Higher-Order Moments Based on Return Decomposition with Application to Risk Measurement
Based on the return decomposition method,this paper proposes a dynamic return decompo-sition(DRD)model to characterize the dynamic higher-order-moment of returns.This approach decom-poses the returns into a product of sign and absolute value components and specifies the time-variability of the sign component,absolute value components and the correlation between these two components,respectively.The DRD model can not only flexibly set the time-varying evolution of conditional skewness and kurtosis,but also depict the nonlinear characteristics of returns by setting the time-varying Copula for the correlation between the sign and absolute components.Therefore,this model has an advantage in return prediction.We also conduct an empirical study of the DRD model using daily return data of the Shanghai Composite Index and the Shenzhen Composite Index.The results show that two return series exhibit significant higher-order moment dynamics,and the evolution of conditional skewness and kurtosis have the characteristics of volatility clustering.Compared with other time-varying higher-order moment models,the DRD model not only has a better in-sample model fitting but also shows some superiority in out-of-sample value-at-risk prediction and economic value evaluation.
万方数据
维普
