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Under heavy-tailed innovation based on asymmetric Log-GARCH VaR model estimates

Author: DongChenZuo
Tutor: LiuWeiQi
School: Shanxi University
Course: Probability Theory and Mathematical Statistics
Keywords: Heavy-tailed innovation Log-GARCH Value at Risk VaR Extreme Value Theory
CLC: F830.9
Type: Master's thesis
Year: 2009
Downloads: 26
Quote: 0
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Abstract


VaR is a measure of financial market risk new ways to cope with the early 1990s and developed a financial disaster . Now VaR methodology has been extended and applied to derivative instruments , VaR technology is currently the most popular on the market and most effective risk management techniques. 2007 by the U.S. subprime crisis triggered by the global financial crisis and the value of risk research challenge . Various financial institutions using different VaR models get different VaR estimates differ because risk appetite coupled with the lack of transparency of the market , so there are many hidden dangers . Currently the internationally recognized VaR calculation means, but there is no absolute evidence which is optimal, but only in some ways or some angle to illustrate the method Goodness. Therefore , how to establish as accurately describe VaR model is still an important issue. In the long-term empirical studies, it was found that most financial time series have shown a fat tail characteristics and cluster group ( heteroskedasticity ) effect, and people on the good news and bad news has obviously asymmetric reactions . Risk management study, people are more concerned about the occurrence of extreme events , this paper proposes an asymmetric Log-GARCH model , not only can well describe financial time series fat tail and heteroscedasticity , there are obvious leverage effect . However , Mikosch Starica found GARCH residuals follow a normal class model than the actual data in the tail of a thin , so use extreme value theory approach of heavy-tailed errors asymmetric Log-GARCH VaR model is of great significance . This paper describes the Value at Risk VaR system definition , and summarizes the various methods of calculating VaR . In Geweke (1986), Pantula (1986) proposed Log-GARCH model and Nelson (1991) proposed EGARCH model is proposed based on asymmetric Log-GARCH model and gives errors under heavy tail VaR estimates based on the model and prove the estimated asymptotic normality , and also to establish a level of p 0 confidence interval , making the estimates more precise. Finally, the paper on the Shanghai Composite Index and Shenzhen Component to happen rate of return for the proposed model empirical analysis results show that the model is effective.

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