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General Maximum Principles for Partially Observed Risk-Sensitive Optimal Control Problems and Applications to Finance

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Abstract

This paper is concerned with partially observed risk-sensitive optimal control problems. Combining Girsanov’s theorem with a standard spike variational technique, we obtain some general maximum principles for the aforementioned problems. One of the distinctive differences between our results and the standard risk-neutral case is that the adjoint equations and variational inequalities strongly depend on a risk-sensitive parameter γ. Two examples are given to illustrate the applications of the theoretical results obtained in this paper. As a natural deduction, a general maximum principle is also obtained for a fully observed risk-sensitive case. At last, this result is applied to study a risk-sensitive optimal portfolio problem. An explicit optimal investment strategy and a cost functional are obtained. A numerical simulation result shows the influence of a risk-sensitive parameter on an optimal investment proportion; this coincides with its economic meaning and theoretical results.

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References

  1. Yong, J.M., Zhou, X.Y.: Stochastic Controls: Hamiltonian Systems and HJB Equations. Springer, New York (1999)

    MATH  Google Scholar 

  2. Bensoussan, A., Van Schuppen, J.H.: Optimal control of partially observable stochastic systems with an exponential-of-integral performance index. SIAM J. Control Optim. 23, 599–613 (1985)

    Article  MATH  MathSciNet  Google Scholar 

  3. Charalambous, C.D., Hibey, J.L.: Minimum principle for partially observable nonlinear risk-sensitive control problems using measure-valued decomposition. Stoch. Stoch. Rep. 57, 247–288 (1996)

    MATH  MathSciNet  Google Scholar 

  4. Nagai, H.: Risk-sensitive dynamic asset management with partial information. In: Hida, T., Karandikar, R.L., Kunita, H., Rajput, B.S., Watanale, S., Xiong, J. (eds.) Stochastic in Finite and Infinite Dimension, A volume in Honor of Gopinath Kallianpur, pp. 321–340. Birkhäuser, Boston (2000)

    Google Scholar 

  5. Nagai, H., Peng, S.G.: Risk-sensitive dynamic portfolio optimization with partial information on infinite time horizon. Ann. Appl. Probab. 12, 173–195 (2002)

    Article  MATH  MathSciNet  Google Scholar 

  6. Bensoussan, A.: Maximum principle and dynamic programming approaches of the optimal control of partially observed diffusions. Stochastic 9, 169–222 (1983)

    MATH  MathSciNet  Google Scholar 

  7. Haussmann, U.G.: The maximum principle for optimal control of diffusions with partial information. SIAM J. Control Optim. 25, 341–361 (1987)

    Article  MATH  MathSciNet  Google Scholar 

  8. Baras, J.S., Elliott, R.J., Kohlmann, M.: The partially observed stochastic minimum principle. SIAM J. Control Optim. 27, 1279–1292 (1989)

    Article  MATH  MathSciNet  Google Scholar 

  9. Zhou, X.Y.: On the necessary conditions of optimal controls for stochastic partial differential equations. SIAM J. Control Optim. 31, 1462–1478 (1993)

    Article  MATH  MathSciNet  Google Scholar 

  10. Li, X.J., Tang, S.J.: General necessary conditions for partially observed optimal stochastic controls. J. Appl. Probab. 32, 1118–1137 (1995)

    Article  MATH  MathSciNet  Google Scholar 

  11. Baghery, F., Øksendal, B.: A maximum principle for stochastic control with partial information. Stoch. Anal. Appl. 25, 705–717 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  12. Peng, S.G.: A general stochastic maximum principle for optimal control problems. SIAM J. Control Optim. 28, 966–979 (1990)

    Article  MATH  MathSciNet  Google Scholar 

  13. Hamadène, S.: Backward-forward SDE’s and stochastic differential games. Stoch. Process. Appl. 77, 1–15 (1998)

    Article  MATH  Google Scholar 

  14. Wu, Z., Yu, Z.Y.: Linear quadratic nonzero-sum differential games with random jumps. Appl. Math. Mech. 26, 1034–1039 (2005)

    Article  MATH  MathSciNet  Google Scholar 

  15. Kalman, R.E.: A new approach to linear filtering and prediction problems. Trans. ASME—J. Basic Eng. Ser. D 82, 35–45 (1960)

    Google Scholar 

  16. Liptser, R.S., Shiryayev, A.N.: Statistics of Random Process. Springer, New York (1977)

    Google Scholar 

  17. Wohnam, W.H.: On the separation theorem of stochastic control. SIAM J. Control 6, 312–326 (1968)

    Article  MathSciNet  Google Scholar 

  18. Karatzas, I., Shreve, S.E.: Methods of Mathematical Finance. Springer, New York (1998)

    MATH  Google Scholar 

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Correspondence to Z. Wu.

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Communicated by F. Zirilli.

This work was partially supported by the National Natural Science Foundation (10671112), the National Basic Research Program of China (973 Program, No. 2007CB814904), the Natural Science Foundation of Shandong Province (Z2006A01) and the Doctoral Fund of the Education Ministry of China.

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Wang, G.C., Wu, Z. General Maximum Principles for Partially Observed Risk-Sensitive Optimal Control Problems and Applications to Finance. J Optim Theory Appl 141, 677–700 (2009). https://doi.org/10.1007/s10957-008-9484-1

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