VaR与CVaR的敏感性凸性及其核估计

Abstract

Abstract: Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR) are two mainly popular risk measurement tools presently. How these risk measure indicators can be estimated accurately is the primary and central problem in the risk management practice. Nonparametric kernel estimation method has received broad attention recently because it can process dependent structure problem very easy and its model is flexible. In this paper, the kernel estimator of VaR and CVaR is investigated and firstly some properties of kernel estimator of VaR and CVaR are discussed. Then the investment portfolio's VaR and CVaR are definited and the analytical expression is derived for the first and second derivatives of the VaR and CVaR, which are used to analyze the sensitivity and convexity of VaR and CVaR. Finally, the kernel estimation method is used to estimate the sensitivity and convexity of VaR and CVaR.

Key words: value-at-risk, conditional value-at-risk, kernel estimation, convexity

CLC Number:

F830.9
O212

HUANG Jin-bo, LI Zhong-fei, ZHOU Xian-bo. Sensitivity and Convexity of VaR (CVaR) and Their Kernel Estimator[J]. Chinese Journal of Management Science, 2014, 22(8): 1-9.

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Sensitivity and Convexity of VaR (CVaR) and Their Kernel Estimator

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