关键词: 资产组合, 非线性分位数, Copula函数, 风险非线性叠加

Abstract: In order to optimize the asset portfolio scheme, considering the characteristics of single asset distribution such as asymmetry, heteroscedasticity, leptokurtosis and fat-tail, and the time-varying nonlinear correlation between assets, the Copula-nonlinear quantile regression model is established. The contributions are as follows. Firstly, the objective function is established by constructing the VaR ratio function that considers the return rate and the effect of dynamic loss aversion, which changes the traditional models measuring the risk using the standard deviation of the excess return, while the empirical research pays more attention to the loss of assets rather than all risks, and failure to consider the lack of investor asymmetry in preferences for gains and losses. Secondly, the quantile regression model based on the support vector machine model is established, and expression of the edge distribution function is obtained, which solves the problem with which that the traditional model can't deal well with asymmetry and nonlinearity and depends on the distribution hypothesis. Thirdly, a mixture Copula function is established, which can effectively capture the tail correlation and asymmetry in the financial market. Fourthly, an overall risk assessment model based on the non-linear superposition of assets risk is established, which makes up for the existing risk assessment model without considering the correlation between assets, and the total risk of the assets is the simple linear addition of the individual asset. Through comparative analysis, it is found that the prediction performance of the model established in this paper is higher than that of other models. The model can have higher VaR ratio value.

Key words: asset portfolio, nonlinear quantile regression, Copula function, non-linear superposition of risk