基于跳扩散过程的ETF基金动态市场风险测度研究

A Study on the Dynamic Market Risk Measurement of ETF Based on the Jump Diffusion Process

本文分别构建了基于双指数-跳扩散过程及双因素-跳扩散过程的ETF基金收益率模型,研究认为后者对于价格的预测较为准确。建立了时变EVT-POT-GPD方法来确定ETF基金收益率标准残差的分位数,据此提出了动态市场风险测度方法,并以中国、香港、美国ETF基金的样本进行实证研究。结果表明,采用双因素模型并以一年期历史数据作为窗口数据进行ETF基金价格预测时的效果较好,坏消息及好消息对于ETF基金收益率的冲击具有较显著的非对称性影响和杠杆效应,而TGARCH模型在度量ETF基金收益率正向非对称性冲击及条件异方差特性时的效果较好。采用四种GARCH模型得到的ETF基金动态CVaR值均大于VaR值,这说明本文所构建的动态风险测度方法在风险估计方面更为保守和有效。当置信水平从95%上升到99%时,ETF基金的CVaR的增长率要快于VaR的增长率,这也表明当置信水平较高时本文所提出的动态CVaR风险测度方法敏感程度要高于动态VaR的。利用Back-testing及卡方检验发现,随着置信水平的提高,本文提出的动态CVaR及动态VaR测度方法的成功率均上升。

This paper constructs a model of ETF fund return based on the double exponential jump diffusion process and two-factor jump diffusion process,and presents the solving method for parameters based on MCMC. According to the four kinds of asymmetric GARCH model,it establishes a time-varying EVT-POT-GPD method to further measure quantile of standard residuals of ETF fund return,and presents a method of determining the dynamic market risk VaR and CVaR. The empirical results prove that using two-factor model and one-year historical data can better forecast the price of ETF fund,the return of ETF fund market suffering different impact from good and bad news has significant asymmetry characteristics and leverage effects,and the TGARCH model is better in measuring positive asymmetric shocks and conditional heteroscedasticity. It finds using the EVT-POT-GPD method constructed to calculate standard residuals of ETF fund return,with the improvement of confidence level,threshold value k will move towards tail and quantile will increase gradually. Additionally,dynamic CVaR of the five ETF fund is greater than VaR obtained by any GARCH model,and when the confidence level rises from 95% to 99%,the CVaR growth rate is far faster than the growth rate of VaR. It also shows that the sensitivity of CVaR in measuring risks is much higher than that of VaR. Meanwhile,back-testing and the chi-square test find that the success rate of CVaR and VaR rises with increasing the confidence level.