Abstract
The Value-at-Risk (VaR) approach has been used for measuring and controlling the market risks in financial institutions. Studies show that the t-distribution is more suited to representing the financial asset returns in VaR calculations than the commonly used normal distribution. The frequency of extremely positive or extremely negative financial asset returns is higher than that is suggested by normal distribution. Such a leptokurtic distribution can better be approximated by a t-distribution. The aim of this study is to asses the performance of a real coded Genetic Algorithm (GA) with Evolutionary Strategies (ES) approach for Maximum Likelihood (ML) parameter estimation. Using Monte Carlo (MC) simulations, we compare the test results of VaR simulations using the t-distribution, whose optimal parameters are generated by the Evolutionary Algorithms (EAs), to that of the normal distribution. It turns out that the VaR figures calculated with the assumption of normal distribution significantly understate the VaR figures computed from the actual historical distribution at high confidence levels. On the other hand, for the same confidence levels, the VaR figures calculated with the assumption of t-distribution are very close to the results found using the actual historical distribution. Finally, in order to speed up the MC simulation technique, which is not commonly preferred in financial applications due to its time consuming algorithm, we implement a parallel version of it.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Jackson, P., Maude, D.J., Perraudin, W.: Bank Capital and Value at Risk. The. Journal of Derivatives 4, 73–90 (1997)
Engelbrecht, R.: A Comparison of Value-At-Risk Methods for Portfolios Consisting of Interest Rate Swaps and FRAs. Economics Series Working Papers, University of Oxford, Department of Economics (2003)
Duffie, D., Pan, J.: An Overview of Value at Risk. Journal of Derivatives 4, 7–49 (1997)
Jorion, P.: Value at Risk: The new Benchmark for Controlling Market Risk. In: RiskMetrics Technical Manual, pp. 847–860. McGraw-Hill, New York (1997)
Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Springer, Berlin Heidelberg New York (2003)
Bayer, H.G., Schwefel, H.P.: Evolution Strategies a Comprehensive Introduction. Natural Comp. 1, 3–52 (2002)
Uludag, G., Senel, K., Etaner-Uyar, A.S., Dag, H.: ML Estimation of Distribution Parameters for VaR Calculation Using Evolutionary Algorithm. WSEAS Transaction on Business and Economics (2005)
Dowd, K., Blake, D., Cairns, A.: Long-Term Value at Risk. Journal of Risk Finance 5(2), 52–57 (2004)
Van den Goorbergh, R.W.J., Vlaar, P.: Value-at-Risk Analysis of Stock Returns Historical Simulation, Variance Techniques or Tail Index Estimation?. DNB Staff Reports, Amsterdam (1999)
http://www.weibull.com/AccelTestWeb/mle_maximum_likelihood_parameter_estimation.htm
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Uludag, G., Uyar, A.S., Senel, K., Dag, H. (2007). Comparison of Evolutionary Techniques for Value-at-Risk Calculation. In: Giacobini, M. (eds) Applications of Evolutionary Computing. EvoWorkshops 2007. Lecture Notes in Computer Science, vol 4448. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71805-5_24
Download citation
DOI: https://doi.org/10.1007/978-3-540-71805-5_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71804-8
Online ISBN: 978-3-540-71805-5
eBook Packages: Computer ScienceComputer Science (R0)