Nonparametric estimation of operational value-at-risk (OpVaR)

Ainura Tursunalieva and Param Silvapulle

Insurance: Mathematics and Economics, 2016, vol. 69, issue C, 194-201

Abstract: This paper introduces nonparametric methods for estimating 99.9% operational value-at-risk (OpVaR) and its confidence interval (CI), and demonstrates their applications to US business losses. An attractive feature of these new methods is that there is no need to estimate either the entire heavy-tailed loss distribution or the tail region of the distribution. Furthermore, we provide algorithms that facilitate applied researchers and practitioners in risk management area to implement the sophisticated empirical likelihood ratio (ELR) based methodologies to construct the CI of the true underlying 99.9% OpVaR. In a simulation study, we find that the weighted ELR (WELR) CI estimator is more reliable than the ELR CI estimator. The empirical results show that the nonparametric OpVaR estimates are consistently larger than those of other comparable methods, which provide adequate regulatory capitals, particularly during crises. The findings have implications for regulators, and effective and efficient risk financing.

Keywords: Loss severity distribution; Risk analysis; Operational risk; Simulation; Empirical likelihood confidence band (search for similar items in EconPapers)
JEL-codes: C13 C14 C16 G32 (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:insuma:v:69:y:2016:i:c:p:194-201

DOI: 10.1016/j.insmatheco.2016.05.010

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