A comparative study for mult i-period asset allocation of defined contribution schemes : Evidence from Turkey

Uzun dönemli varlık tahsisi, çok dönemli doğası nedeni ile, klasik varlık tahsisi paradigmasından çok daha karmaşıktır. Analitik modeller sorunu matematiksel gerekçelerle aşırı derecede basitleştirme yoluna gittiğinden temelde çok daha farklı bir yaklaşıma ihtiyaç duyulmaktadır. Kendini doğrusal olmayan bir çözüm uzayında süreksizliklerle gösteren bu karmaşıklığı çözebilmek için genelde sayısal yöntemler, özel olarak da genetik algoritmalar önemli bir alternatif olarak karşımıza çıkmaktadır. Daha önce hipotetik ve ABD verileri ile yaptığımız çalışmaları tamamlayıcı bir niteliğe sahip olan bu çalışma ile, sayısal yöntemlerin çok dönemli varlık tahsisi sorununu çözme performansı hakkında daha fazla kanıt elde etme amacını güdüyoruz.

Bireysel emeklilik fonlarında çok dönemli varlık tahsisi için Türkiye üzerine karşılaştırmalı bir çalışma

Long term asset allocation is more complicated than the usual asset allocation paradigm due to the multi-period nature of the problem. Since analytical models usually oversimplify for the sake of mathematical convenience, a fundamentally different approach is needed. Numerical solutions such as genetic algorithms provide an important alternative to analytical solutions in handling the inherent complexity which manifests itself as discontinuities and nonlinearities of the solution space. Complementing our previous studies with hypothetical and US market data, this study brings further evidence on the comparative performance of numerical solutions for multi-period asset allocation from an emerging market.

PDF

___

Baglioni S., Da Costa Pereira C., Sorbello D., Tettamanzi A. G. B., (2000), “An Evolutionary Approach to Multiperiod Asset Allocation”, Lecture Notes in Computer Science, (1802), 225-236.
Benartzi S., Thaler R. H., (2001), “Naive Diversification Strategies in Defined Contribution Saving Plans”, American Economic Review, 91(1), 79-98.
Blake D., Cairns A., Dowd K., (1999), “Pension Metrics: Stochastic Pension Plan Design and Value at Risk During the Accumulation Phase”, BSI-Gamma Foundation (Global Asset Management Methods And Applications), Working Paper Series, (11), 1-77.
Haberman S., Vigna E., (2002), “Optimal Investment Strategies and Risk Measures in Defined Contribution Pension Schemes”, Insurance: Mathematics and Economics, (31), 35-69.
Hibiki N., (2006), “Multi-period stochastic optimization models for dynamic asset allocation”, Journal of Banking & Finance, (30), 365–390.
Holland J. H., (1975), Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial intelligence, Ann Arbor, MI, Michigan University Press.
http://www.egm.org.tr/weblink/BESgostergeler.htm, [2 February 2012, WEB].
Huang H., Lee Y., (2010), “Optimal asset allocation for a general portfolio of life insurance policies”, Insurance: Mathematics and Economics, (46), 271-280.
Ingber L., (1993), “Simulated Annealing: Practice versus Theory”, Mathematical and Computer Modeling, 18(11), 29-57.
Josa-Fombellida R., Rincon-Zapatero J. P., (2010), “Optimal asset allocation for aggregated defined benefit pension funds with stochastic interest rates”, European Journal of Operational Research, (201), 211–221.
Kirkpatrick S., Gelatt C. D. Jr., Vecchi M. P., (1983), “Optimization by Simulated Annealing”, Science, 220(4598), 671-680.
Kung J. J., (2008), “Multi-period asset allocation by stochastic dynamic programming”, Applied Mathematics and Computation, (199), 341–348.
Markowitz H. M., (1987), Mean–Variance Analysis in Portfolio Choice and Capital Markets, Oxford, UK, Basil Blackwell.
Markowitz H. M., (1952), “Portfolio selection”, Journal of Finance, (7), 77–91.
McNelis P. D., (2005), Neural Networks in Finance: Gaining Predictive Edge in the Market, Burlington, MA, Elsevier.
Merton R. C., (1969), “Lifetime Portfolio Selection under Uncertainty: The Continuous-Time Case”, The Review of Economics and Statistics, (51), 247–257.
Merton R. C., (1971), “Optimum consumption and portfolio rules in a continuous-time model”, Journal of Economic Theory, (3), 373–413.
Merton R. C., (1992), Continuous-Time Finance, Revised Edition, Malden, MA, Blackwell Publishing.
Merton R. C., (1973), “An intertemporal capital asset pricing model”, Econometrica, (41), 867–887.
Metropolis N., Rosenbluth A. W., Rosenbluth M. N., Teller A. H., Teller E., (1953), “Equation of State Calculations by Fast Computing Machines”, Journal of Chemical Physics, 21(6), 1087-1092.
Mossin J., (1968), “Optimal multiperiod portfolio policies”, Journal of Business, (41), 215–229.
R Development Core Team, (2007), R: A language and environment for statistical computing, Vienna, Austria, R Foundation for Statistical Computing.
Senel K., Pamukcu A. B., Yanik S., (2006), “Using Genetic Algorithms for Optimal Investment Allocation Decision in Defined Contribution Pension Schemes”, 10th International Congress on Insurance: Mathematics & Economics.
Senel K., Pamukcu A. B., Yanik S., (2008), An Evolutionary Approach to Asset Allocation in Defined Contribution Pension Schemes, Brabazon A., O’Neill M., (edt.), Natural Computing in Computational Finance, (25–52), Berlin Heidelberg, Springer-Verlag.
Sherris M., (1993), “Portfolio Selection Models for Life Insurance and Pension Funds”, Proceedings of Actuarial Approach for Financial Risks Colloquium, 2 April 1993, 915-930.
Szmolyan N., (2008), “The Remaking of the DC Market”, Employee Benefit Research Institute 62nd Policy Forum, May 2008 (http://www.ebri.org).
Tuncer R., Senel K., (2004), “Optimal Investment Allocation Decision in Defined Contribution Pension Schemes with Time-Varying Risk”, 8th International Congress on Insurance: Mathematics and Economics.
Vigna E., Haberman S., (2001), “Optimal Investment Strategy for Defined Contribution Pension Schemes”, Insurance: Mathematics and Economics, (28), 233-262.
Willighagen E., (2005), R Based Genetic Algorithm, R package version 0.1.1.
Zhang X., Zhang K., (2009), “Using genetic algorithm to solve a new multi-period stochastic optimization model”, Journal of Computational and Applied Mathematics, (231), 114-123.