|
Evaluating Multiperiod VaR via Nonlinear Quantile Regression Model
XU Qi-fa, ZHANG Jin-xiu, JIANG Cui-xia
2015, 23 (3):
56-65.
doi: 10.16381/j.cnki.issn1003-207x.2015.03.007
The stylized facts of financial markets, such as volatility clustering, fat tail and asymmetry, make the multiperiod VaR do not comply with simple "rule of time root" in one period VaR measure. Therefore, a more reasonable method is need to seek to evaluate multiperiod VaR accurately. Multiperiod VaR is mainly influenced by two variables, i.e. holding period and volatility. To determine the impact model (linear or nonlinear) of the two variables is essential for evaluating VaR accurately. Nonlinear quantile regression model, overcoming the limitations of the linear quantile regression model in describing linear dependence between multiperiod VaR and its influencing factors, can be used to improve the accuracy of VaR. Three types of methods, volatility model, QRNN+volatility model, and SVQR+volatility model, for evaluating multiperiod VaR has been proposed in this paper based on volatility modeling and nonlinear quantile regression method. For empirical application, three stock price indices are selected: Shenzhen Composite Index, Hang Seng Index and S&P 500 from 19 Jan. 2011 to 28 Sep. 2012. Six different volatility models are considered and two types of nonlinear quantile regression models are combined with them. As a result, the 18 kinds of methods in multiperiod VaR measure are compared together. The empirical results show that volatility model has an influence on the effect of multiperiod VaR measure. In terms of the accuracy of VaR measure, the SVQR+volatility model is slightly better than QRNN+volatility model, and both of them are superior to the volatility model. The good performance of the nonlinear quantile regression models in VaR evaluation comes from the fact that the QRNN and SVQR models belong to nonparametric methods. They have the ability to discover a complex nonlinear relationship among variables without specifying a explicit functional form. This property is very useful for exploring the unknown relation among financial variables.
References |
Related Articles |
Metrics
|