Stochastic Switching for Partially Observable Dynamics and Optimal Asset Allocation

Juri Hinz
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Juri Hinz: School of Mathematical Sciences, UTS Business School, University of Technology Sydney

No 358, Research Paper Series from Quantitative Finance Research Centre, 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.

Pages: 19 pages
Date: 2015-03-01
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Published as: Hinz, J. and Yee, J., 2017, "Stochastic Switching for Partially Observable Dynamics and Optimal Asset Allocation", International Journal of Control, 90(3), 553-565.

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