Construction and Application of Mean-ES Model Based on Distorted Mixed Copula Function
Considering the profound tail dependence structure among financial assets,which exerts a substantial impact on portfolio risk optimization,a mean-ES model is constructed based on distorted mixed copula functions to derive an investment portfolio optimization strategy with a specific expected return by capturing the extreme tail characteristics of financial assets through distortion functions.Firstly,extend the covariance matrix employed in the mean-ES model to depict the extreme tail dependence structure of the distorted mixed copula function,thereby constructing a mean-ES model based on it.Secondly,an ES estimation method is proposed based on this model.Finally,the efficacy of this model is demonstrated in investment portfolio optimization for datasets exhibiting extreme tail dependence structure through numerical simulation and empirical studies.The results from numerical simulation indicate that,the mean-ES model relying on distorted mixed copula function is suitable for datasets characterized by extreme tail dependence structure.Upon optimizing the investment portfolio using this model,both returns and risks are significantly improved.Empirical study results further confirm that the model substantially enhances out-of-sample performance of optimal investment portfolios and validate its accuracy in predicting risk during portfolio optimization.Consequently,mean-ES model based on distorted mixture copula function fills a crucial gap in traditional portfolio risk optimization models by addressing extreme tail risks and propels research into applying distorted mixture copula function in portfolio risk optimization.
万方数据
维普
