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

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  • Tursunalieva, Ainura
  • Silvapulle, Param

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.

Suggested Citation

  • Tursunalieva, Ainura & Silvapulle, Param, 2016. "Nonparametric estimation of operational value-at-risk (OpVaR)," Insurance: Mathematics and Economics, Elsevier, vol. 69(C), pages 194-201.
    • Handle: RePEc:eee:insuma:v:69:y:2016:i:c:p:194-201
    DOI: 10.1016/j.insmatheco.2016.05.010
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    References listed on IDEAS

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    1. Andreas A. Jobst, 2007. "It's all in the data – consistent operational risk measurement and regulation," Journal of Financial Regulation and Compliance, Emerald Group Publishing Limited, vol. 15(4), pages 423-449, November.
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    3. Patrick de Fontnouvelle & Eric Rosengren & John Jordan, 2007. "Implications of Alternative Operational Risk Modeling Techniques," NBER Chapters, in: The Risks of Financial Institutions, pages 475-505, National Bureau of Economic Research, Inc.
    4. Marco Moscadelli, 2004. "The modelling of operational risk: experience with the analysis of the data collected by the Basel Committee," Temi di discussione (Economic working papers) 517, Bank of Italy, Economic Research and International Relations Area.
    5. Peter Hall & Qiwei Yao, 2003. "Data tilting for time series," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(2), pages 425-442, May.
    6. Ainura Tursunalieva & Param Silvapulle, 2014. "A semi-parametric approach to estimating the operational risk and Expected Shortfall," Applied Economics, Taylor & Francis Journals, vol. 46(30), pages 3659-3672, October.
    7. Chavez-Demoulin, V. & Embrechts, P. & Neslehova, J., 2006. "Quantitative models for operational risk: Extremes, dependence and aggregation," Journal of Banking & Finance, Elsevier, vol. 30(10), pages 2635-2658, October.
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    Cited by:

    1. Chai, Shanglei & Zhou, P., 2018. "The Minimum-CVaR strategy with semi-parametric estimation in carbon market hedging problems," Energy Economics, Elsevier, vol. 76(C), pages 64-75.

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    More about this item

    Keywords

    Loss severity distribution; Risk analysis; Operational risk; Simulation; Empirical likelihood confidence band;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C16 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Econometric and Statistical Methods; Specific Distributions
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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