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Minimax Quadratic Optimization and Its Application to Investment Planning

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Abstract

Minimax optimization with a quadratic criterion and linear equality- and inequality-type constraints is investigated. The minimax solution is expressed in general form. Sufficient conditions for the minimax solution to be uniquely determined by the solution of the dual problem are formulated. The results are applied to construct an investment portfolio having guaranteed characteristics under a priori statistical uncertainty.

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REFERENCES

  1. Kurzhanski, A.B. and Tanaka, M., On a Unified Framework for Deterministic and Stochastic Treatment of Identification problems, Laxenburg: IIASA, 1989.

    Google Scholar 

  2. Solov'ev, V.N., Dual Extremal Problems and Their Application in Minimax Estimation, Usp. Mat. Nauk, 1997, vol. 52, no. 4, pp. 49-86.

    Google Scholar 

  3. Pankov, A.R. and Semenikhin, K.V., A Minimax Indeterminate-Stochastic Linear Identification Model, Avtom. Telemekh., 1998, no. 11, pp. 158-171.

  4. Pankov, A.R. and Semenikhin, K.V., Methods of Parametric Identification of Multivariate Linear Models under a priori Uncertainty, Avtom. Telemekh., 2000, no. 5, pp. 76-92.

  5. Uberala, K., Factoreanalyse, New York: Springer-Verlag, 1977. Translated under the title Faktornyi analiz, Moscow: Statistika, 1980.

    Google Scholar 

  6. Polyak, B.T., Vvedenie v optimizatsiyu (Introduction to Optimization), Moscow: Nauka, 1980.

    Google Scholar 

  7. Zangwill, W.I., Nonlinear Programming. A Unified Approach, Englewood Cliffs: Prentice-Hall, 1969. Translated under the tile Nelineinoe programmirovanie. Edinyi podkhod, Moscow: Sovetskoe Radio, 1973.

    Google Scholar 

  8. Markowitz, H., Portfolio Selection: Efficient Diversification of Investment, New York: Wiley, 1959.

    Google Scholar 

  9. Merton, R.C., On the Microeconomic Theory of Investment under Uncertainty, in Handbook of Mathematical Economics, 1982, vol. 2, pp. 601-669.

  10. Elton, E.E. and Gruber, H.J., Modern Portfolio Theory and Investment Analysis, New York: Wiley, 1987.

    Google Scholar 

  11. Pervozvanskii, A.A. and Pervozvanskii, T.N., Finansovyi rynok: raschet i risk (Financial Market: Computation and Risk), Moscow: Infra-M, 1994.

    Google Scholar 

  12. Malykhin, V.I., Finansovaya matematika (Financial Mathematics), Moscow: Yuniti, 1999.

    Google Scholar 

  13. Pankov, A.R., Platonov, E.N., and Semenikhin, K.V., Minimax Optimization of the Markowitz-Tobin Investment Model, in Tr. Mezhdunar. konf. “Identifikatsiya sistem i zadachi upravleniya” (Proc. Int. Conf. Identification of Systems and Control Problems), Moscow, 2000, pp. 2012-2021.

  14. Kassam, S.A. and Poor, G.V., Robust Signal Processing Methods, TIIER, 1985, vol. 73, no. 3, pp. 54-110.

    Google Scholar 

  15. Golubev, G.A., Muravlev, O.V., and Pisarev, V.F., Linear Recurrent Filtering of Discrete-Time Dynamic Processes with Partial Information on Perturbations, Avtom. Telemekh., 1989, no. 12, pp. 49-59.

  16. Shiryaev, V.I., Synthesis of Controls for Linear Systems under Incomplete Information, Izv. Ross. Akad. Nauk, Tekh. Kibern., 1994, no. 3, pp. 229-237.

  17. Pankov, A.R., Control Strategies for a Linear Stochastic System with Non-Gaussian Perturbations, Avtom. Telemekh., 1994, no. 6, pp. 74-83.

  18. Matasov, A.I., Vvedenie v teoriyu garantiruyushchego otsenivaniya (Introduction to Guaranteed Estimation Theory), Moscow: Mosk. Aviats. Inst., 1999.

    Google Scholar 

  19. VerdÚ, S. and Poor, H.V., On Minimax Robustness: A General Approach and Applications, IEEE Trans. Inf. Theory, 1984, vol. 30, no. 2, pp. 328-340.

    Google Scholar 

  20. Albert, A., Regression, and the Moor-Penrose Pseudoinverse, New York: Academic, 1972. Translated under the title Regressiya, psevdoinversiya i rekurrentnoe otsenivanie, Moscow: Nauka, 1977.

    Google Scholar 

  21. Rockafellar, R.T., Convex Analysis, Princeton: Princeton Univ. Press, 1970. Translated under the title Vypuklyi analiz, Moscow: Mir, 1973.

    Google Scholar 

  22. Ioffe, A.D. and Tikhomirov, V.M., Teoriya ekstremal'nykh zadach (Theory of Extremal Problems), Moscow: Nauka, 1974.

    Google Scholar 

  23. Melas, V.B., The Choice of Experimental Design and Estimation Method with a priori Information on Parameters, in Matematicheskie metody planirovaniya eksperimenta (Mathematical Experiment Designing Methods), Novosibirsk: Nauka, 1981, pp. 155-173.

    Google Scholar 

  24. Kan, Yu.S. and Tuzov, N.V., Minimization of the Quantile of the Normal Distribution of the Bilinear Loss Function, Avtom. Telemekh., 1998, no. 11, pp. 82-92.

  25. Balakrishnan, A.V., Applied Functional Analysis, New York: Springer-Verlag, 1980. Translated under the title Prikladnoi funktsional'nyi analiz, Moscow: Nauka, 1980.

    Google Scholar 

  26. Pshenichinyi, B.N., Neobkhodimye usloviya ekstremuma (Necessary Conditions for an Extremum), Moscow: Nauka, 1982.

    Google Scholar 

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Pankov, A.R., Platonov, E.N. & Semenikhin, K.V. Minimax Quadratic Optimization and Its Application to Investment Planning. Automation and Remote Control 62, 1978–1995 (2001). https://doi.org/10.1023/A:1013768310715

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