基于分位数回归的人民币兑美元汇率风险测度

针对人民币兑美元汇率风险问题,提出了一种基于分位数回归的风险测度方法;以2015-08-11—2019-09-16人民币兑美元汇率中间价数据为研究样本,运用EGARCH模型和TGARCH模型刻画了外汇收益率序列存在的不对称性、波动集聚性以及尖峰厚尾性特征,并在GARCH族VaR模型的基础上构建了QR-GARCH族VaR模型,最后选择Kupiec失败率检验和动态分位数检验等后测检验方法,比较了两类模型的风险预测精度;结果表明:相对于GARCH族VaR模型,QR-GARCH族VaR模型不仅仅对随机扰动项的假设分布不敏感,并且表现出显著优异的风险预测能力,其中基于t分布的QR-EGARCH VaR模型的预测能力最优,故QR-GARCH族VaR模型在人民币兑美元风险测度问题上更具适用性和稳健性。

Risk Measure of RMB/USD Exchange Rate Based on Quantile Regression

In view of the risk measure of RMB/USD exchange rate, this paper proposes a method to measure the exchange rate risk by quantile regression. Firstly, by using the data of the central parity rate of the RMB /USD from August 11, 2015 to September 16, 2019, we choose EGARCH model and TGARCH model to describe the asymmetry, volatility clustering and peak thick tail characteristics of foreign exchange rate series. Next, on the basis of GARCH-type VaR models, we establish QR-GARCH-type VaR models. Finally, Kupiec failure rate test and dynamic quantile test as the post test method are selected to compare the risk prediction accuracy of the two kinds of models. The results show that compared with GARCH-type VaR models, QR-GARCH-type VaR models are not sensitive to the hypothetical distribution of random disturbances and show significantly excellent risk prediction ability. Among them, QR-EGARCH VaR models based on t distribution have the best prediction ability, so QR-GARCH-type VaR models are more applicable and robust in the measurement of the risk of RMB/USD exchange rate.