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- W2034697537 abstract "Abstract Value-at-Risk (VaR) has become the standard tool used by many financial institutions to measure market risk. However, the performance of a VaR estimator may be affected by sample variation or estimation risk caused from heavy-tailed distributions. After surveying several existing procedures proposed by Jorin (Jorion, P. (1996 Jorion, P. 1996. Risk2—measuring the risk in value at risk. Financial Analysis Journal, 52: 47–56. [Crossref] , [Google Scholar]). Risk2—Measuring the risk in value at risk. Financial Analysis Journal 52:47–56), Huschens (Huschens, S. (1997 Huschens, S. 1997. “Confidence intervals for the value-at-risk”. In Risk Measurement, Econometrics and Neural Networks Edited by: Bol, G., Nakhaeizadeh, G. and Vollmer, K. H. 233–244. Heidelberg: Physica-Verlag. [Google Scholar]). Confidence intervals for the value-at-risk. In: Bol, G., Nakhaeizadeh, G., Vollmer, K. H., eds. Risk Measurement, Econometrics and Neural Networks. Heidelberg: Physica-Verlag, pp. 233–244), and Ridder (Ridder, T. (1997 Ridder, T. 1997. “Basics of statistical VaR–estimation”. In Risk Measurement, Econometrics and Neural Networks Edited by: Bol, G., Nakhaeizadeh, G. and Vollmer, K. H. 161–187. Heidelberg: Physica-Verlag. [Google Scholar]). Basics of statistical VaR-estimation. In: Bol, G., Nakhaeizadeh, G., Vollmer, K. H., eds. Risk Measurement, Econometrics and Neural Networks. Heidelberg: Physica-Verlag, pp. 161–187) etc., this article strives to propose several new estimators in measuring the risk involved in VaR estimation. We compare the performance of these VaR models through Monte Carlo simulation studies. We find that the newly proposed methods provide better accuracy and robustness in the estimation of the risk in VaR estimator." @default.
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- W2034697537 date "2003-01-11" @default.
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- W2034697537 title "Nonparametric Estimation for Risk in Value-at-Risk Estimator" @default.
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- W2034697537 doi "https://doi.org/10.1081/sac-120023877" @default.
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