基于分位数回归模型的VaR研究——以贵州百灵股票为例
VaR Research Based on Quantile Regression Model—Taking Guizhou Bailing Stock as an Example

DOI: 10.12677/SA.2019.82040, PP. 364-369

Keywords: VaR风险测度，分位数回归方法，GARCH模型
Value-at-Risk, Quantile Regression Method, Garch Model

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Abstract:

本文以贵州百灵股票作为研究对象，通过建立分位数回归的VaR模型，进行描述股票数据的风险测度研究。将分位数回归模型和传统GARCH模型对VaR的风险测度结果进行比较。实证结果表明，分位数回归方法在所取数据样本内取得了较为乐观的结果，基于分位数回归模型所得结果的精度要比传统模型要高。
Taking Guizhou Bailing as the research object, this paper established the VaR model of Quantile Regression to describe the risk measurement of stock data. The Quantile Regression model and the traditional GARCH model were compared with the risk measurement results of VaR. The empirical results show that the Quantile Regression method achieves relatively optimistic results in the data samples, and the accuracy of the results based on the Quantile Regression model is higher than that of the traditional model.

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