基于滞后虚拟变量分位点回归模型的条件VaR估计
Estimation of conditional VaR based on the lag and dull variable quantile regression model
在大多数文献中，分位点回归模型是线性的，但是在实际中，线性的分位点回归模型已经不能很好地满足需要，为此本文提出了含有滞后虚拟变量的分位点回归模型，并应用此模型分析了流动性风险指标条件下的条件VaR 。经过实证分析发现，含有二阶滞后虚拟变量的分位点回归模型模拟数据得到的结果比线性分位点回归模型和基于流动性风险指标的虚拟变量分位点回归模型模拟得到的结果更好。而且，由条件 VaR 的事后检验知，含有二阶滞后虚拟变量的分位点回归模型能更好的估计条件VaR。
In most articles,quantile regression model is linear,but in practice, the linear quantile regression model can not suit the practical demand very well.So in this paper,the lag and dull variable quantile regression model is presented to estimate the conditional VaR,which is conditioned on the liquidity risk measure . By the empirical analysis, we can find that the lag 2 and dull variable quantile regression model can better describe real data than the linear quantile regression model and the dull variable quantile regression model based on the liquidity risk measure. And by the backtesting of the conditional value at risk, the conditional value at risk can be better stimulated by the lag 2 and dull variable quantile regression model.
贺壬癸、严定琪、裴培
财政、金融
线性分位点回归模型含虚拟变量的分位点回归模型含滞后虚拟变量的分位点回归模型条件VaR事后检验
Linear quantile regression modelDull variable quantile regression modelLag dull variable quantile regression modelConditional value at riskBack-test methods
贺壬癸,严定琪,裴培.基于滞后虚拟变量分位点回归模型的条件VaR估计[EB/OL].(2011-08-12)[2025-08-18].http://www.paper.edu.cn/releasepaper/content/201108-204.点此复制
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