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Finding optimal portfolio based on genetic algorithm with generalized Pareto distribution
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 Title & Authors
Finding optimal portfolio based on genetic algorithm with generalized Pareto distribution
Kim, Hyundon; Kim, Hyun Tae;
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 Abstract
Since the Markowitz`s mean-variance framework for portfolio analysis, the topic of portfolio optimization has been an important topic in finance. Traditional approaches focus on maximizing the expected return of the portfolio while minimizing its variance, assuming that risky asset returns are normally distributed. The normality assumption however has widely been criticized as actual stock price distributions exhibit much heavier tails as well as asymmetry. To this extent, in this paper we employ the genetic algorithm to find the optimal portfolio under the Value-at-Risk (VaR) constraint, where the tail of risky assets are modeled with the generalized Pareto distribution (GPD), the standard distribution for exceedances in extreme value theory. An empirical study using Korean stock prices shows that the performance of the proposed method is efficient and better than alternative methods.
 Keywords
Extreme value theory;genetic algorithm;GPD;portfolio optimization;VaR;
 Language
Korean
 Cited by
1.
The estimation of CO concentration in Daegu-Gyeongbuk area using GEV distribution, Journal of the Korean Data and Information Science Society, 2016, 27, 4, 1001  crossref(new windwow)
 References
1.
Alexander, G. J. and Baptista, A. M. (2002). Economic implications of using a mean-VaR model for portfolio selection: a comparison with mean-variance analysis. Journal of Economic Dynamics and Control, 26, 1159-1198 crossref(new window)

2.
Anione, S., Loraschi, A. and Tettamanzi, A. (1993). A genetic approach to portfolio selection. Neural Network World, 6, 597-604. crossref(new window)

3.
Balkema, A. A. and De Haan, L. (1974). Residual life time at great age. The Annals of probability, 2, 792-804. crossref(new window)

4.
Bridges, C. L. and Goldberg, D. E. (1987). An analysis of reproduction and crossover in a binary-coded genetic algorithm. Grefenstette, 878, 9-13.

5.
Byun, H. W., Song, C. W., Han, S. K., Lee, T. K. and Oh, K. J. (2009). Using genetic algorithm to optimize rough set strategy in KOSPI200 futures market. Journal of the Korean Data & Information Science Society, 20, 1049-1060.

6.
Chambers, L. D. (1995). Practical Handbook of Genetic Algorithms, CRC Press, Florida.

7.
Chung, S. H. and Oh, K. J. (2014). Using genetic algorithm to optimize rough set strategy in KOSPI200 futures market. Journal of the Korean Data & Information Science Society, 25, 281-292. crossref(new window)

8.
Davison, A. and R. Smith. (1990). Models for exceedances over high thresholds (with discussion). Journal of the Royal Statistical Society, 52, 393-442.

9.
Eberhart, R., Simpson, P. and Dobbins, R. (1996). Computational intelligence PC tools, Academic Press Professional, San Diego.

10.
Embrechts, P., Kluppelberg, C. and Mikosch, T. (1997). Modelling extremal events for Insurance and Finance, Springer, New York.

11.
Golberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning . Addison wesley, Boston.

12.
Lin, P. C. and Ko, P. C. (2009). Portfolio value-at-risk forecasting with GA-based extreme value theory. Expert Systems with Applications, 36, 2503-2512. crossref(new window)

13.
Longin, F. M. (2000). From value at risk to stress testing: The extreme value approach. Journal of Banking and Finance, 24, 1097-1130. crossref(new window)

14.
Markowitz, H. M. (1952). Portfolio selection. Journal of Finance, 7, 77-91.

15.
Marsili, M., Maslov, S. and Zhang, Y. C. (1998). Dynamical optimization theory of a diversified portfolio. Physica A: Statistical Mechanics and its Applications, 253, 403-418.

16.
Oh, K. J., Kim, T. Y., Min, S. H. and Lee, H. Y. (2006). Portfolio algorithm based on portfolio beta using genetic algorithm. Expert Systems with Applications, 30, 527-534. crossref(new window)

17.
Oh, S. K. (2005). Oh, S. K., Extreme Value Theory and Value at Risk focusing on GPD Models. Journal of Money and Finance, 19, 72-114.

18.
Pickands III, J. (1975). Statistical inference using extreme order statistics. the Annals of Statistics, 3, 119-131. crossref(new window)

19.
Rankovi, V., Drenovak, M., Stojanovi, B., Kalini, Z. and Arsovski, Z. (2014). The mean-Value at Risk static portfolio optimization using genetic algorithm. Computer Science and Information Systems, 11, 89-109. crossref(new window)

20.
Scrucca, L. (2014). GA: a package for genetic algorithms in R. Journal of Statistical Software, 53, 1-37.

21.
Shoaf, J. and Foster, J. (1998). The efficient set GA for stock portfolios. In Proceedings of the 1998 IEEE international conference on computational intelligence, 354-359, IEEE Service Center, New Jersey.

22.
Statman, M. (1987). How many stocks make a diversified portfolio?. Journal of Financial and Quantitative Analysis, 22, 353-363. crossref(new window)