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.

Publications:

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

Acknowledgement of Country

UTS acknowledges the Gadigal people of the Eora Nation, the Boorooberongal people of the Dharug Nation, the Bidiagal people and the Gamaygal people, upon whose ancestral lands our university stands. We would also like to pay respect to the Elders both past and present, acknowledging them as the traditional custodians of knowledge for these lands.