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A Matlab Package for Approximating the Solution to a Continuous- Time Stochastic Optimal Control Problem

Author

Listed:
  • Alistair Windsor

    (Victoria University of Wellington)

  • Jacek B. Krawczyk

    (Victoria University of Wellington)

Abstract

Computing the solution to a stochastic optimal control problem is difficult. A method of approximating a solution to a given stochatic optimal problem was developed in [1]. This paper describes a suite of Matlab functions implementing this method of approximating a solution to a given continuous stochastic optimal control problem.

Suggested Citation

  • Alistair Windsor & Jacek B. Krawczyk, 1997. "A Matlab Package for Approximating the Solution to a Continuous- Time Stochastic Optimal Control Problem," Computational Economics 9710002, University Library of Munich, Germany.
  • Handle: RePEc:wpa:wuwpco:9710002
    Note: Type of Document - LaTeX; prepared on UNIX; to print on PostScript; pages: 14 ; figures: none
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    Citations

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    Cited by:

    1. Foster, Jarred, 2011. "Target variation in a loss avoiding pension fund problem," MPRA Paper 36177, University Library of Munich, Germany.
    2. Krawczyk, Jacek & Azzato, Jeffrey, 2006. "NISOCSol an algorithm for approximating Markovian equilibria in dynamic games with coupled-constraints," MPRA Paper 1195, University Library of Munich, Germany.
    3. Jacek B. Krawczyk, 2000. "A Markovian Approximated Solution To A Portfolio Management Problem," Computing in Economics and Finance 2000 233, Society for Computational Economics.
    4. Jacek B Krawczyk, 2015. "Delivering Left-Skewed Portfolio Payoff Distributions in the Presence of Transaction Costs," Risks, MDPI, Open Access Journal, vol. 3(3), pages 1-20, August.
    5. Foster, Jarred & Krawczyk, Jacek B, 2013. "Sensitivity of cautious-relaxed investment policies to target variation," Working Paper Series 2972, Victoria University of Wellington, School of Economics and Finance.

    More about this item

    Keywords

    s Approximating Markov decision chains; economic software;

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software

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