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Stochastic Switching for Partially Observable Dynamics and Optimal Asset Allocation

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  • Juri Hinz

    (School of Mathematical Sciences, UTS Business School, University of Technology Sydney)

Abstract

In industrial applic ations, optimal c ontrol problems frequently appear in the c ontext of dec isions-making under inc omplete information. In su ch framework, dec isions must be adapted dynami cally to acc ount for possible regime c hanges of the underlying dynamic s. Using sto hastic filtering theory, Markovian evolution c an be modeled in terms of latent variables, whi h naturally leads to high dimensional state spa ce, making prac tic al solutions to these c ontrol problems notoriously c hallenging. In our approa h, we utilize a speci fic stru ture of this problem c lass to present a solution in terms of simple, reliable, and fast algorithms.

Suggested Citation

  • Juri Hinz, 2015. "Stochastic Switching for Partially Observable Dynamics and Optimal Asset Allocation," Research Paper Series 358, Quantitative Finance Research Centre, University of Technology, Sydney.
    • Handle: RePEc:uts:rpaper:358
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    File URL: https://www.uts.edu.au/sites/default/files/qfr-archive-03/QFR-rp358.pdf
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