Reducing Variation of Risk Estimation by Using Importance Sampling

Çoban, Hatem; Deveci Kocakoç, İpek; Erken, Şemsettin; Aksoy, Mehmet Akif

doi:http://dx.doi.org/10.17093/alphanumeric.605584

Reducing Variation of Risk Estimation by Using Importance Sampling

Hatem Çoban

İpek Deveci Kocakoç, Ph.D.

Şemsettin Erken

Mehmet Akif Aksoy

Abstract

In today's world, risk measurement and risk management are of great importance for various economic reasons. Especially in the crisis periods, the tail risk becomes very important in risk estimation. Many methods have been developed for accurate measurement of risk. The easiest of these methods is the Value at Risk (VaR) method. However, standard VaR methods are not very effective in tail risks. This study aims to demonstrate the usage of delta normal method, historical simulation method, Monte Carlo simulation, and importance sampling to calculate the value at risk and to show which method is more effective by applying them to the S&P index between 1993 and 2003.

Keywords: Delta Normal Method, Importance Sampling, Monte Carlo Simulation, Tail Risk, Value at Risk

Jel Classification: G32

Suggested citation

Çoban, H., Deveci Kocakoç, İ., Erken, Ş. & Aksoy, M. A. (2019). Reducing Variation of Risk Estimation by Using Importance Sampling. Alphanumeric Journal, 7(2), 173-184. http://dx.doi.org/10.17093/alphanumeric.605584

bibtex

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