alphanumeric journal
The Journal of Operations Research, Statistics, Econometrics and Management Information Systems
Reducing Variation of Risk Estimation by Using Importance Sampling
bib
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
(). Reducing Variation of Risk Estimation by Using Importance Sampling. Alphanumeric Journal, 7(2), 173-184. http://dx.doi.org/10.17093/alphanumeric.605584
References
- Bassamboo, A., Juneja, S. & Zeevi, A. (2005). Portfolio Credit Risk with Extremal Dependence. Ssrn, 56(3), 593–606.
- Brereton, T., Kroese, D. & Chan, J. (2012). Monte Carlo methods for portfolio credit risk, ANU Working Papers in Economics and Econometrics. Access Domain: https://ideas.repec.org/p/acb/cbeeco/2012-579.html
- Danielsson, J. (2011). Financial Risk Forecasting: The Theory and Practice of Forecasting Market Risk, with Implementation in R and Matlab, Wiley&Sons Inc:UK
- De Vooys, F. (2012). Importance Sampling for Credit Risk Monte Carlo simulations using the Cross Entropy Approach, Nederland Open University Computer Science, Master Thesis. Access Domain: https://dspace.ou.nl/bitstream/1820/4285/1/INF_20120417_Vooys.pdf
- Glasserman, P. (2003). Monte Carlo Methods in Financial Engineering. Springer-Verlag New York.
- Glasserman, P. & Li, J. (2005). Importance Sampling for Portfolio Credit Risk. Management Science, 51(11), 1643–1656.
- Glasserman, P., Heidelberger, P. & Shahabuddin. P. (1999a). Asymptotically optimal importance sampling and stratification for pricing path-dependent options. Mathematical Finance, 9,117–152.
- Glasserman, P., Heidelberger, P. & Shahabuddin. P. (1999b). Importance sampling in the HeathJarrow-Morton framework. Technical report, IBM Research Report RC 21367, Yorktown Heights,NY.
- Gupta, J. & Chaudhry, S. (2019). Mind the Tail, or Risk to Fail, Journal of Business Research, 99, 167-185.
- Jorion, P. (2003). Financial Risk Manager Handbook. John Wiley&Sons Inc: New Jersey
- Kahn, H. (1950a). Random sampling (Monte Carlo) techniques in neutron attenuation problems, I. Nucleonics, 6(5), 27–37.
- Kahn, H. (1950b). Random sampling (Monte Carlo) techniques in neutron attenuation problems, II. Nucleonics, 6(6), 60–65.
- Kahn, H. & Marshall, A. (1953). Methods of reducing sample size in Monte Carlo computations. Journal of the Operations Research Society of America, 1(5), 263–278.
- Kalkbrener, M., Lotter, H. & Overbeck, L. (2004) Sensible and efficient allocation for credit portfolios, Risk, 17, S19-S24
- Keasler, T.R. (2001). The Underwriter's Early Lock-Up Release: Empirical Evidence. Journal of Economics and Finance, 25(2), 214-228.
- Keçeci, N.F. & Demirtaş, Y.E. (2018). Risk-Based DEA Efficiency and SSD Efficiency of OECD Members Stock Indices. Alphanumeric Journal, 6 (1), 25-36. DOI: 10.17093/alphanumeric. 345483
- Liu, G. (2010). Importance sampling for risk contributions of credit portfolios. Proceedings - Winter Simulation Conference, 2771–2781.
- Morokoff, W. J. (2004). Proceedings of the 2004 Winter Simulation Conference R .G. Ingalls, M. D. Rossetti, J. S. Smith, and B. A. Peters, eds.
- Müller, A. (2016). Improved Variance Reduced Monte-Carlo Simulation of in-the-Money Options. Journal of Mathematical Finance, 6(3), 361–367.
- Neftci, S. N. (2000). Value at Risk Calculations, Extreme Events, and Tail Estimation, Journal of Derivatives, 7, 23-38.
- Rubinstein, M.(2002). Markowitz's "Portfolio Selection": A Fifty-Year Retrospective. The Journal of Finance, 57(3), 1041-1045.
- Van den Goorbergh, R.W.J. & Vlaar P.J.G. (1999). Value-at-Risk Analysis of Stock Returns:Historical Simulation, Variance Techniques or Tail Index Estimation?. Research Memorandum WO&E nr 579, 1-38.
2019.07.02.ECON.02
alphanumeric journal
Pages 173-184
Received: Aug. 15, 2019
Accepted: Dec. 22, 2019
Published: Dec. 31, 2019
2019 Çoban, H., Deveci Kocakoç, İ., Erken, Ş., Aksoy, MA.
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence, which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
scan QR code to access this article from your mobile device
Contact Us
Faculty of Transportation and Logistics, Istanbul University
Beyazit Campus 34452 Fatih/Istanbul/TURKEY
Bahadır Fatih Yıldırım, Ph.D.
editor@alphanumericjournal.com
+ 90 (212) 440 00 00 - 13219
alphanumeric journal
alphanumeric journal has been publishing as "International Peer-Reviewed Journal" every six months since 2013. alphanumeric serves as a vehicle for researchers and practitioners in the field of quantitative methods, and is enabling a process of sharing in all fields related to the operations research, statistics, econometrics and management informations systems in order to enhance the quality on a globe scale.