The estimation of Value-at-Risk using a non-parametric approach
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
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Author's Biography
Amir Olfat is pursuing his PhD in statistics at Allameh Tabatab'i university. He has a master's degree in actuarial science and a bachelor's degree in statistics. His master's thesis was on copula modelling in operational risk management. After ten years' experience in the insurance market, he now works as an associate vice president in the Quantitative Risk Control Group at MUFG Bank in the Canadian branch.
Farzad Eskandari is Professor of Statistics and Data Science at Allameh Tabataba'i University. He has published more than 100 articles in international journals with i10-index 11 and h-index 10. Farzad's main interests are non-parametric statistics, data science, computational statistics and Bayesian statistics.
Citation
Olfat, Amir and Eskandari, Farzad (2023, August 1). The estimation of Value-at-Risk using a non-parametric approach. In the Journal of Risk Management in Financial Institutions, Volume 16, Issue 2. https://doi.org/10.69554/YDLU1514.
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