一个新的时变系数分位数回归模型及应用

A Quantile Regression Model with Time Varying Coefficients and Its Applications

结合普通分位数回归的模型结构和可行性最小二乘方法的时变系数特征,在普通分位数回归模型的损失函数中引入动态误差设定,提出了一个新的模型:时变系数分位数回归模型,并给出其模型表示、模型估计以及模型检验等建模方法。时变系数分位数回归模型更能够适应广泛数据类型的建模需求,体现回归系数的时变特征,揭示解释变量对响应变量完整条件分布特征的影响,具有广阔的应用前景。将其应用于组合投资决策分析,构造出VaR风险动态组合投资方案,并与VaR风险静态组合投资方案、方差风险静态组合投资方案、方差风险动态组合投资方案等进行实证比较。结果表明,基于时变系数分位数回归模型的VaR风险动态组合投资方案所得投资效果在收益、方差、Sharpe比率和VaR数值等方面都显著优于其他三种方案。

We propose a new quantile regression model with time varying coefficients by con- sidering both the structure of traditional quantile regression model and the character of time varying coefficients in flexible least square approach. The new model can be estimated by utilizing a flexible quantile regression approach, which is obtained by adding dynamic error to the loss function of tradi- tional quantile regression model. It is very powerful to investigate the regular patter of a wider data. It has broad application prospects because it can not only reflect time varying feature of regression coeffi- cients, but also reveal how predictors influence on the complete conditional distribution of responses. In this paper, the model is applied to portfolio section analysis and to construct a VaR risk based dynamic portfolio scheme. We do empirical analysis and compare the new scheme with those traditional portfolio schemes, including VaR risk based static portfolio, variance risk based dynamic portfolio and variance risk based static portfolio. The empirical results show that our new portfolio scheme is superior to other three portfolio schemes in terms of return, variance, Shame ratio and Var