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Affiliation: Georgia Institute of Technology
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Bounds for the Sum of Dependent Risks and Worst Value-at-Risk with Monotone Marginal Densities 2012
Wang, Ruodu
Peng, Liang
Yang, Jingping
In quantitative risk management, it is important and challenging to find sharp bounds for the distribution of the sum of dependent risks with given ma...
Jackknife Empirical Likelihood Method for Some Risk Measures and Related Quantities 2012
Peng, Liang
Qi, Yongcheng
Yang, Jingping
Wang, Ruodu
Quantifying risks is of importance in insurance. In this paper, we employ the jackknife empirical likelihood method to construct confidence intervals ...
Empirical Likelihood Intervals for Conditional Value-at-Risk in Heteroscedastic Regression Models 2011
Li, Zhouping
Gong, Yun
Peng, Liang
Non-parametric regression models have been studied well including estimating the conditional mean function, the conditional variance function and the ...
Semi-Parametric Models for the Multivariate Tail Dependence Function - The Asymptotically Dependent Case 2007
Kluppelberg, Claudia
Peng, Liang
Kuhn, Gabriel
In general, the risk of joint extreme outcomes in financial markets can be expressed as a function of the tail dependence function of a high-dimension...
Bias Reduction for High Quantiles 2010
Li, Deyuan
Yang, Jingping
Peng, Liang
High quantile estimation is of importance in risk management. For a heavy-tailed distribution, estimating a high quantile is done via estimating the t...
Confidence Regions for High Quantiles of a Heavy Tailed Distribution 2006
Qi, Yongcheng
Peng, Liang
Estimating high quantiles plays an important role in the context of risk management. This involves extrapolation of an unknown distribution function. ...
Empirical likelihood intervals for conditional Value-at-Risk in ARCH/GARCH models 2010
Gong, Yun
Li, Zhouping
Peng, Liang
Value-at-Risk (VaR) is a simple, but useful measure in risk management. When some volatility model is employed, conditional VaR is of importance. As a...
Interval Estimation for the Value-at-Risk Based on GARCH Models with Heavy Tailed Innovations 2005
Chan, Ngai Hang
Deng, Shi-Jie
Peng, Liang
Xia, Zhendong
ARCH and GARCH models are widely used to model financial market volatilities inmany risk management applications. Based on a GARCH model with heavy-ta...
Using a bootstrap method to choose the sample fraction in tail index estimation 1999
de Vries, Casper G.
Daníelsson, Jón
De Haan, Laurens
Peng, Liang
Tail index estimation depends for its accuracy on a recise choice of the sample fraction,i.e.the number of extreme order statistics on which the estim...
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