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An overview of representation theorems for static risk measures 被引量:2
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作者 SONG YongSheng YAN JiaAn 《Science China Mathematics》 SCIE 2009年第7期1412-1422,共11页
In this paper,we give an overview of representation theorems for various static risk measures:coherent or convex risk measures, risk measures with comonotonic subadditivity or convexity, law-invariant coherent or conv... In this paper,we give an overview of representation theorems for various static risk measures:coherent or convex risk measures, risk measures with comonotonic subadditivity or convexity, law-invariant coherent or convex risk measures, risk measures with comonotonic subadditivity or convexity and respecting stochastic orders. 展开更多
关键词 Choquet integral (concave) distortion law-invariant risk measure stochastic orders 46N10 60E05 60E15 91B28 91b30
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Large-deviation probabilities for maxima of sums of subexponential random variables with application to finite-time ruin probabilities 被引量:2
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作者 JIANG Tao School of Finance,Zhejiang Gongshang University,Hangzhou 310018,China 《Science China Mathematics》 SCIE 2008年第7期1257-1265,共9页
We establish an asymptotic relation for the large-deviation probabilities of the maxima of sums of subexponential random variables centered by multiples of order statistics of i.i.d.standard uniform random variables.T... We establish an asymptotic relation for the large-deviation probabilities of the maxima of sums of subexponential random variables centered by multiples of order statistics of i.i.d.standard uniform random variables.This extends a corresponding result of Korshunov.As an application,we generalize a result of Tang,the uniform asymptotic estimate for the finite-time ruin probability,to the whole strongly subexponential class. 展开更多
关键词 large-deviation probability strongly subexponential distribution finite-time ruin probability the compound Poisson model uniform asymptotics 91b30 60G70 62P05
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Empirical likelihood-based evaluations of Value at Risk models
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作者 WEI ZhengHong WEN SongQiao ZHU LiXing 《Science China Mathematics》 SCIE 2009年第9期1995-2006,共12页
Value at Risk (VaR) is a basic and very useful tool in measuring market risks. Numerous VaR models have been proposed in literature. Therefore, it is of great interest to evaluate the efficiency of these models, and t... Value at Risk (VaR) is a basic and very useful tool in measuring market risks. Numerous VaR models have been proposed in literature. Therefore, it is of great interest to evaluate the efficiency of these models, and to select the most appropriate one. In this paper, we shall propose to use the empirical likelihood approach to evaluate these models. Simulation results and real life examples show that the empirical likelihood method is more powerful and more robust than some of the asymptotic method available in literature. 展开更多
关键词 Value at Risk VOLATILITY empirical likelihood specification test non-nested test 62G10 62P20 91b30
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