The Dynamics of VaRs with Skew t Distribution for A300 Index in China

Cheng Li Zheng; Dong Dong Huang

doi:10.4028/www.scientific.net/AMM.135-136.1051

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 135-136The Dynamics of VaRs with Skew t Distribution for...

The Dynamics of VaRs with Skew t Distribution for A300 Index in China

Abstract:

The high frequency of financial crises make the risk management of financial asset become the focus of the financial investors and scholars. The traditional VaR based on assumption about the normal distribution of yield is not suitable for the real thing in China. The characteristics of stock market yield in China is fat tail and non-symmetry. In this paper, the dynamics of VaRs based on TARCH model with the skew t distribution are computed and analysed. And then failure rate of VaRs are compared under normal distribution, student's-t distribution and GED distribution. The results show that the most accurate VaR is the one with the skew t distribution, which describes the reality of the stock market better than others.

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Periodical:

Applied Mechanics and Materials (Volumes 135-136)

Pages:

1051-1056

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.135-136.1051

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Online since:

October 2011

Authors:

Cheng Li Zheng, Dong Dong Huang

Keywords:

Management, Risk Measure, Skew t Distribution, Value-at-Risk (VaR)

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References

[1] Jorison,P. Value at Risk: The new benchmark for contorllinf market risk[M]. New York: The McGraw-Hill Companies, (1997).

Google Scholar

[2] Engle R F., 1982, Auto regressive conditional heteroskedasticity w ith estimates of the variance of U nited Kingdom Infla2tion Journal of Econometrica, Vol. 50: 987—1006.

DOI: 10.2307/1912773

Google Scholar

[3] Wang jipei, wang congying. The volatility of fund based on GED-GARCH model. Modern economy, Jul. 2008 pp: 12-14.

Google Scholar

[4] Chen youdong, yu shidian. Using VaR methods to analysis the Chinese stock market based on GARCH model. Social Science of Jilin university. Jul. 2002 pp: 11-17.

Google Scholar

[5] Liu kunlun. Risk Metering of Financial Market based on VaR model. Huazhong University of Science. 2006. pp: 21-34.

Google Scholar

[6] Tong yonghuan. Risk measures of market based on tail distribution. Graduate Science of Sun Yat-sen. Mar. (2008).

Google Scholar

[7] Wangjipei, wangkaiye. The risk characteristics of CSI 300 stock index futures—based on dependent risk measures of Copula functions. Sciencepaper on line.

Google Scholar

[8] Gao tiemei. Econometric Analysis and Modeling. Tsinghua University Press. pp: 193-207. (2005).

Google Scholar

[9] Philip Chory writen, chenyue translation. Value at Risk. CITIC Publishing House. 2005. pp: 171-210.

Google Scholar