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Portfolio Optimization Under Partial Information and Convex Constraints in a Hidden Markov Model

In: Operations Research Proceedings 2005

Author

Listed:
  • Jörn Sass

    (Austrian Academy of Sciences)

Abstract

Summary In a continuous-time hidden Markov model (HMM) for stock returns we consider an investor who wishes to maximize the expected utility of terminal wealth. As a means to deal with the resulting highly risky strategies we impose convex constraints on the trading strategies covering e.g. short selling restrictions. Based on HMM filtering methods we show how to reformulate this model with partial information as a model with full information. Then results on portfolio optimization under constraints are used to give a verification result. By its application an optimal trading strategy can be computed. Numerical results are provided.

Suggested Citation

  • Jörn Sass, 2006. "Portfolio Optimization Under Partial Information and Convex Constraints in a Hidden Markov Model," Operations Research Proceedings, in: Hans-Dietrich Haasis & Herbert Kopfer & Jörn Schönberger (ed.), Operations Research Proceedings 2005, pages 223-228, Springer.
    • Handle: RePEc:spr:oprchp:978-3-540-32539-0_36
    DOI: 10.1007/3-540-32539-5_36
    as

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