The estimation of Value-at-Risk using a non-parametric approach

Amir Olfat and Farzad Eskandari
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Amir Olfat: PhD Student of Statistics, Allameh Tabataba'i University, Iran
Farzad Eskandari: Professor of Statistics, Allameh Tabataba'i University, Iran

Journal of Risk Management in Financial Institutions, 2023, vol. 16, issue 2, 180-188

Abstract: This paper is concerned with estimating the risk measure, Value-at-Risk (VaR), without considering the usual hypothesis used in parametric methods. A non-parametric method is used to fit severity and frequency loss distributions in collective risk models. In addition, an optimum bandwidth is estimated. The model is then applied to insurance claims data from a particular insurance company. As a result of the new model, the outcomes show better accuracy, for both light-tailed and heavy-tailed distributions

Keywords: risk management; non-parametric models; VaR; cross-validation; kernel function (search for similar items in EconPapers)
JEL-codes: E5 G2 (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:aza:rmfi00:y:2023:v:16:i:2:p:180-188

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