Tail Risk Measurement in Stock Market Based on Levy-GARCH Model
Managing financial risk is crucial to guard against systematic risk,while risk management depends on describing stylized facts in stock markets and measuring tail risk.In Chinese stock market,we build 20 Levy-GARCH models to investigate the affect of asymmetry and non-Gaussianity on return prediction and tail risk measurement.We calculate the VaR and CVaR when noises follow jump processes and use fast Fourier transformation and back-testing in empirical research.According to empirical research on the Shanghai Stock Exchange Composite Index,asymmetry and non-Gaussianity are vital characteristics,and jump processes matter in fitting and predicting returns and measuring risks;in tail risk measurement,non-affine GARCH models with jumps outperform affine models,and GARCH models with finite intensity jump process perform better than those with infinite activity jumps.In conclusion,the non-affine Levy-GARCH models with asymmetry and non-Gaussianity perform well in return fitting and tail risk measurement,and jump process with finite intensity can explain tail risk in Chinese stock markets more precisely.
万方数据
维普
