Tail Index Estimation Of Heavy-tailed Distribution, The Calculation Of VaR And Empirical Analysis Of China's Stock Markets

The core of finaneial risk management is to measure the risk quantitatively.In order to measure the risk accurately,the statistieal distribution must be described.In the normal condition,the finaneial data is credible,the estimated VaR is aceurate. But in the abnormal condition,the trustworthy data can not be got and the estimated VaR is not accurate.Financial risk is assoeiated with low-probability events in the tails of asset price distribution.To capture the behavior of these tails,one should therefore rely on models that explicitly focus on the tails.Extreme value theory based models do exactly it.In this dissertation,the performance of the extreme value theory in Value-at-Risk (VaR) calculations is compared to the performances of other well-known modeling techniques,such as Monte-Carlo simulation method,variance-covariance(Var-Cov) method and historieal simulation in China's stock markets.Financial risk management typically deals with low-probability events in the tails of asset price distributions.To capture the behavior of these tails,one should therefore rely on models that explicitly focus on the tails.Extreme value theory based models do that perfectly.We use statistical software package to estimate the parameters of the extreme value theory model.The results indicate the GPD is a robust tool to produce the accurate foreeasts of extreme losses at very high confidence levels.