1. Reducing Variation of Risk Estimation by Using Importance Sampling
- Author
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İpek Deveci Kocakoç, Mehmet Akif Aksoy, Hatem Çoban, and Şemsettin Erken
- Subjects
Estimation ,Operations Research and Management Science ,Index (economics) ,Importance Sampling,Value at Risk,Monte Carlo Simulation,Delta Normal Method,Tail Risk ,lcsh:T55.4-60.8 ,business.industry ,Monte Carlo method ,monte carlo simulation ,Variation (game tree) ,lcsh:Business ,delta normal method ,tail risk ,value at risk ,importance sampling ,Management of Technology and Innovation ,Statistics ,lcsh:Industrial engineering. Management engineering ,Tail risk ,lcsh:HF5001-6182 ,business ,Yöneylem, Araştırma ve Yönetim Bilimi ,Value at risk ,Risk management ,Importance sampling ,Mathematics - 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.
- Published
- 2019
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