Nonparametric kernel estimation of CVaR under α-mixing sequences View Full Text


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Article Info

DATE

2017-10-03

AUTHORS

Zhongde Luo

ABSTRACT

Conditional Value-at-Risk (CVaR) is an increasingly popular coherent risk measure in financial risk management. In this paper, a new nonparametric kernel estimator of CVaR is established, and a Bahadur type expansion of the estimator is also given under α-mixing sequences. Furthermore, the mean, variance, mean square error (MSE) and uniformly asymptotic normality of the new estimator are discussed, optimal bandwidths are obtained as well. In order to better illustrate performances of the new CVaR estimator, we conduct numerical simulations under some α-mixing sequences and a GARCH model, and discover that the new CVaR estimator is smoother and more accurate than estimators proposed by other scholars because of the bias and MSE of the new estimator are smaller. Finally, we use the new estimator to analyze the daily log-loss of real financial series. More... »

PAGES

1-29

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00362-017-0952-2

DOI

http://dx.doi.org/10.1007/s00362-017-0952-2

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https://app.dimensions.ai/details/publication/pub.1092065204


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