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Analyses of VaR and CVaR on Shanghai and Shenzhen Stock Markets by the Use of Extreme Value Theory

Hui Wang

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Abstract

Uniting VaR and CVaR can fully describe tailrelated risk. Since Shanghai and Shenzhen A Stock Market return distribution exhibit fat tails, the writers of this paper make use of extreme value theory to analyze VaR and CvaR on the two markets. Bootstrap method is used to reproduce subsamples to offset insufficient data. Bootstrap and likelihoodbased methods are also used to give point estimation and construct confidence intervals for the VaR and CVaR.

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What this paper is about

Uniting VaR and CVaR can fully describe tailrelated risk. Since Shanghai and Shenzhen A Stock Market return distribution exhibit fat tails, the writers of this paper make use of extreme value theory to analyze VaR and CvaR on the two markets. Bootstrap method is used to reproduce subsamples to offset insufficient data. Bootstrap and likelihoodbased methods are also used to give point estimation and construct confidence intervals for the VaR and CVaR.

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Available abstract

Uniting VaR and CVaR can fully describe tailrelated risk. Since Shanghai and Shenzhen A Stock Market return distribution exhibit fat tails, the writers of this paper make use of extreme value theory to analyze VaR and CvaR on the two markets. Bootstrap method is used to reproduce subsamples to offset insufficient data. Bootstrap and likelihoodbased methods are also used to give point estimation and construct confidence intervals for the VaR and CVaR.

Key concepts: CVAR, Extreme value theory, Econometrics, Value at risk, Economics, Offset (computer science), Stock (firearms), Financial economics

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Analyses of VaR and CVaR on Shanghai and Shenzhen Stock Markets by the Use of Extreme Value Theory — Research Paper | ScholarLens