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Optimal stochastic control in multi-period portfolio optimization

Funding or Partner Organisation: Zurich University of Applied Sciences

Start year: 2016

Summary: In this project, we investigate and examine novel methods for obtaining solutions to specific discrete-time optimal control problems. Our approach is based utilizes linear state dynamics and convexity assumptions commonly satisfied in practical applications. We extend this approach to a class of optimal switching problems under partial observation and we exploit specific model features to improve numerical performance. Further we adopt duality techniques to assess distance-to-optimality of our approximate solutions and apply these methods to address multi-period decision problems arising in the context of portfolio optimization.


Hinz, J 2016, 'Using Convex Switching Techniques for Partially Observable Decision Processes', IEEE Transactions on Automatic Control, vol. 61, no. 9, pp. 2727-2732.
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Hinz, J & Yap, N 2016, 'Algorithms for Optimal Control of Stochastic Switching Systems', Theory of Probability & Its Applications, vol. 60, no. 4, pp. 580-603.
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Keywords: Markov Decision, Approximate dynamic programming, Least Squares Monte Carlo

FOR Codes: Investment Services (excl. Superannuation), Financial Mathematics