DYNAMIC PROBLEM OF FORMATION OF SECURITIES PORTFOLIO UNDER UNCERTAINTY CONDITIONS

Lev Raskin, Oksana Sira, Tetiana Katkova

Abstract


The analysis of known methods for solving the problem of forming a portfolio of securities in the face of uncertainty is carried out. Traditionally, the problem is solved under the assumption that for each type of asset, the values of the main statistical characteristics of the random value of their profitability (mathematical expectation and variance) are known. At the same time, the variance of portfolio returns, which is minimized, is used as a criterion for portfolio optimization. Two alternative approaches to solving the formulated problem are proposed. The first of them provides a decision on the criterion of the probability that the random total portfolio return will not be lower than the given. It is assumed that the random return for each type of asset is distributed normally and the statistical characteristics of the respective densities are known. The original problem is reduced to the problem of maximizing the quadratic fractional criterion in the presence of linear constraints. To solve this non-standard optimization problem, a special iterative algorithm is proposed that implements the procedure for sequential improvement of the plan. The method converges and the computational procedure for obtaining a solution can be stopped by any of the standard criteria. The second approach considers the possibility of solving the problem under the assumption that the distribution densities of random asset returns are not known, however, based on the results of preliminary statistical processing of the initial data, estimates of the values of the main numerical characteristics for each of the assets are obtained. To solve the problem, a new mathematical apparatus is used – continuous linear programming, which is a generalization of ordinary linear programming to the case when the task variables are continuous. This method, in the considered problem, is based on solving an auxiliary problem: finding the worst-case distribution density of a random total portfolio return at which this total return does not reach an acceptable threshold with maximum probability. Now the main minimax problem is being solved: the formation of the best portfolio in the worst conditions. The resulting computational scheme leads to the problem of quadratic mathematical programming in the presence of linear constraints. Next, a method is proposed for solving the problem of forming a portfolio of securities, taking into account the real dynamics of the value of assets. The problem that arises in this case is formulated and solved in terms of the general theory of control, using the Riccati equation.


Keywords


formation of a portfolio of securities; worst-case density distribution of portfolio returns; fractional non-linear optimization problem; Riccati control

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References


Gurin, L. S., Dymarskiy, Ya. S., Merkulov, A. D. (1968). Zadachi i metody optimal'nogo raspredeleniya resursov. Moscow: Sovetskoe radio, 368.

Larichev, O. I. (2002). Teoriya i metody prinyatiya resheniy. Moscow: Logos, 392.

Bazaraa, M. S., Shetty, C. M. (1979). Nonhnear Programming: Theory and Algorithms. Wiley, 312.

Raskin, L. G. (1976). Analiz slozhnyh sistem i ehlementy teorii upravleniya. Moscow: Sovetskoe radio, 344.

Karmanov, V. G. (1980). Matematicheskoe programmirovanie. Moscow, 256.

Himmelblau, D. (1972). Applied Nonlinear Programming. McGraw-Hill, 498.

Zangwill, W. I. (1969). Nonlinear Programming: A Unified Approach. PrenticeHall, 356.

Yudin, D. B. (1974). Matematicheskie metody upravleniya v usloviyah nepolnoy informatsii. Zadachi i metody stohasticheskogo programmirovaniya. Мoscow: Sovetskoe radio, 392.

Demutskiy, V. P., Pignastyy, O. M. (2003). Teoriya predpriyatiya. Ustoychivost' funktsionirovaniya massovogo proizvodstva i prodvizheniya produktsii na rynok. Kharkiv: KhNU im. V. N. Karazina, 272.

Pignastiy, O. M. (2005). Stohasticheskoe opisanie ehkonomiko-proizvodstvennyh sistem s massovym vypuskom produktsii. Doklady Nats. Akad. Nauk Ukrainy, 7, 66–71.

Pignastiy, O. M. (2007). Stohasticheskaya teoriya proizvodstvennyh sistem. Kharkiv: KhNU im. V. N. Karazina, 387.

Raskin, L. G., Kirichenko, I. O., Seraya, O. V. (2013). Prikladnoe kontinual'noe lineynoe programmirovanie. Kharkiv, 293.

Seraya, O. V., Demin, D. A. (2012). Linear Regression Analysis of a Small Sample of Fuzzy Input Data. Journal of Automation and Information Sciences, 44 (7), 34–48. doi: https://doi.org/10.1615/jautomatinfscien.v44.i7.40

Raskin, L. G., Seraya, O. V. (2003). Formirovanie skalyarnogo kriteriya predpochteniya po rezul'tatam poparnyh sravneniy obektov. Sistemnyy analiz, upravlenie i informatsionnye tehnologii. Vestnik NTU «KhPI», 6, 63–68.

Connor, G., Goldberg, L. R., Korajczyk, R. A. (2010). Portfolio Risk Analysis. Princeton University Press. doi: https://doi.org/10.1515/9781400835294

Otani, Y., Imai, J. (2013). Pricing Portfolio Credit Derivatives with Stochastic Recovery and Systematic Factor. IAENG International Journal of Applied Mathematics, 43 (4), 176–184.

Read, C. (2012). The Portfolio Theorists: von Neumann, Savage, Arrow and Markowitz. Palgrave Macmillan. doi: https://doi.org/10.1057/9780230362307

Bellman, R. E., Zadeh, L. A. (1970). Decision-Making in a Fuzzy Environment. Management Science, 17 (4), B–141–B–164. doi: https://doi.org/10.1287/mnsc.17.4.b141

Sira, O., Katkova, T. (2017). Formation of securities portfolio under conditions of uncertainty. Eastern-European Journal of Enterprise Technologies, 1 (4 (85)), 49–55. doi: https://doi.org/10.15587/1729-4061.2017.92283

Orlovskiy, S. A. (1981). Problemy prinyatiya resheniy pri nechetkoy informatsii. Moscow: Nauka, 264.

Atans, M. (1968). Optimal'noe upravlenie. Moscow: Mashinostroenie, 764.

Brayson, A. (1972). Prikladnaya teoriya optimal'nogo upravleniya. Moscow: Mir, 544.

Pawlak, Z. (1991). Rough Sets: Theoretical Aspects of Reasoning about Data. Dordrecht: Kluwer Academic Publisher. doi: https://doi.org/10.1007/978-94-011-3534-4

Raskin, L., Sira, O. (2016). Method of solving fuzzy problems of mathematical programming. Eastern-European Journal of Enterprise Technologies, 5 (4 (83)), 23–28. doi: https://doi.org/10.15587/1729-4061.2016.81292

Raskin, L., Sira, O. (2016). Fuzzy models of rough mathematics. Eastern-European Journal of Enterprise Technologies, 6 (4 (84)), 53–60. doi: https://doi.org/10.15587/1729-4061.2016.86739

Raskin, L., Sira, O., Ivanchykhin, Y. (2017). Models and methods of regression analysis under conditions of fuzzy initial data. Eastern-European Journal of Enterprise Technologies, 4 (4 (88)), 12–19. doi: https://doi.org/10.15587/1729-4061.2017.107536




DOI: http://dx.doi.org/10.21303/2461-4262.2019.00985

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ISSN 2461-4262 (Online), ISSN 2461-4254 (Print)