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On the effectiveness of scenario generation techniques in single-period portfolio optimization

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  • Guastaroba, Gianfranco
  • Mansini, Renata
  • Speranza, M. Grazia

Abstract

In single-period portfolio selection problems the expected value of both the risk measure and the portfolio return have to be estimated. Historical data realizations, used as equally probable scenarios, are frequently used to this aim. Several other parametric and non-parametric methods can be applied. When dealing with scenario generation techniques practitioners are mainly concerned on how reliable and effective such methods are when embedded into portfolio selection models. In this paper we survey different techniques to generate scenarios for the rates of return. We also compare the techniques by providing in-sample and out-of-sample analysis of the portfolios obtained by using these techniques to generate the rates of return. Evidence on the computational burden required by the different techniques is also provided. As reference model we use the Worst Conditional Expectation model with transaction costs. Extensive computational results based on different historical data sets from London Stock Exchange Market (FTSE) are presented and some interesting financial conclusions are drawn.

Suggested Citation

  • Guastaroba, Gianfranco & Mansini, Renata & Speranza, M. Grazia, 2009. "On the effectiveness of scenario generation techniques in single-period portfolio optimization," European Journal of Operational Research, Elsevier, vol. 192(2), pages 500-511, January.
  • Handle: RePEc:eee:ejores:v:192:y:2009:i:2:p:500-511
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    References listed on IDEAS

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    1. Bollerslev, Tim & Engle, Robert F & Wooldridge, Jeffrey M, 1988. "A Capital Asset Pricing Model with Time-Varying Covariances," Journal of Political Economy, University of Chicago Press, vol. 96(1), pages 116-131, February.
    2. Andrea Consiglio & Flavio Cocco & Stavros A. Zenios, 2001. "The Value of Integrative Risk Management for Insurance Products with Guarantees," Center for Financial Institutions Working Papers 01-06, Wharton School Center for Financial Institutions, University of Pennsylvania.
    3. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    4. John M. Mulvey & Hercules Vladimirou, 1992. "Stochastic Network Programming for Financial Planning Problems," Management Science, INFORMS, vol. 38(11), pages 1642-1664, November.
    5. Bollerslev, Tim & Chou, Ray Y. & Kroner, Kenneth F., 1992. "ARCH modeling in finance : A review of the theory and empirical evidence," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 5-59.
    6. Bollerslev, Tim, 1990. "Modelling the Coherence in Short-run Nominal Exchange Rates: A Multivariate Generalized ARCH Model," The Review of Economics and Statistics, MIT Press, vol. 72(3), pages 498-505, August.
    7. Stephen P. Bradley & Dwight B. Crane, 1972. "A Dynamic Model for Bond Portfolio Management," Management Science, INFORMS, vol. 19(2), pages 139-151, October.
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    Citations

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    Cited by:

    1. Chen, Yu-Wang & Poon, Ser-Huang & Yang, Jian-Bo & Xu, Dong-Ling & Zhang, Dongxu & Acomb, Simon, 2012. "Belief rule-based system for portfolio optimisation with nonlinear cash-flows and constraints," European Journal of Operational Research, Elsevier, vol. 223(3), pages 775-784.
    2. Ceren Tuncer Şakar & Murat Köksalan, 2013. "A stochastic programming approach to multicriteria portfolio optimization," Journal of Global Optimization, Springer, vol. 57(2), pages 299-314, October.
    3. Mansini, Renata & Ogryczak, Wlodzimierz & Speranza, M. Grazia, 2014. "Twenty years of linear programming based portfolio optimization," European Journal of Operational Research, Elsevier, vol. 234(2), pages 518-535.
    4. repec:eee:ejores:v:264:y:2018:i:1:p:370-387 is not listed on IDEAS
    5. Guastaroba, G. & Speranza, M.G., 2012. "Kernel Search: An application to the index tracking problem," European Journal of Operational Research, Elsevier, vol. 217(1), pages 54-68.
    6. Sun, Qi & Dong, Yucheng & Xu, Weidong, 2013. "Effects of higher order moments on the newsvendor problem," International Journal of Production Economics, Elsevier, vol. 146(1), pages 167-177.
    7. repec:eee:ejores:v:261:y:2017:i:2:p:421-435 is not listed on IDEAS
    8. repec:spr:annopr:v:245:y:2016:i:1:d:10.1007_s10479-014-1719-y is not listed on IDEAS
    9. Enrico Angelelli & Renata Mansini & M. Speranza, 2012. "Kernel Search: a new heuristic framework for portfolio selection," Computational Optimization and Applications, Springer, vol. 51(1), pages 345-361, January.
    10. Cerqueti, Roy & Falbo, Paolo & Guastaroba, Gianfranco & Pelizzari, Cristian, 2013. "A Tabu Search heuristic procedure in Markov chain bootstrapping," European Journal of Operational Research, Elsevier, vol. 227(2), pages 367-384.
    11. Włodzimierz Ogryczak & Tomasz Śliwiński, 2011. "On solving the dual for portfolio selection by optimizing Conditional Value at Risk," Computational Optimization and Applications, Springer, vol. 50(3), pages 591-595, December.
    12. Min, Daiki & Chung, Jaewoo, 2013. "Evaluation of the long-term power generation mix: The case study of South Korea's energy policy," Energy Policy, Elsevier, vol. 62(C), pages 1544-1552.
    13. Guastaroba, G. & Mansini, R. & Ogryczak, W. & Speranza, M.G., 2016. "Linear programming models based on Omega ratio for the Enhanced Index Tracking Problem," European Journal of Operational Research, Elsevier, vol. 251(3), pages 938-956.
    14. Filippi, C. & Guastaroba, G. & Speranza, M.G., 2016. "A heuristic framework for the bi-objective enhanced index tracking problem," Omega, Elsevier, vol. 65(C), pages 122-137.

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