Reducing Variation of Risk Estimation by Using Importance Sampling
| Autor: | İpek Deveci Kocakoç, Mehmet Akif Aksoy, Hatem Çoban, Şemsettin Erken |
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| Rok vydání: | 2019 |
| Předmět: |
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 |
| Zdroj: | Alphanumeric Journal, Vol 7, Iss 2, Pp 173-184 (2019) Volume: 7, Issue: 2 173-184 Alphanumeric Journal |
| ISSN: | 2148-2225 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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