Stochastic switching for partially observable dynamics and optimal asset allocation

Hinz, J; Yee, J

Stochastic switching for partially observable dynamics and optimal asset allocation

Hinz, J Yee, J

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Publication Type:: Journal Article
Citation:: International Journal of Control, 2017, 90 (3), pp. 569 - 581
Issue Date:: 2017-03-04

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Field	Value	Language
dc.contributor.author	Hinz, J	en_US
dc.contributor.author	Yee, J	en_US
dc.date.issued	2017-03-04	en_US
dc.identifier.citation	International Journal of Control, 2017, 90 (3), pp. 569 - 581	en_US
dc.identifier.issn	0020-7179	en_US
dc.identifier.uri	http://hdl.handle.net/10453/124928
dc.description.abstract	© 2016 Informa UK Limited, trading as Taylor & Francis Group. In industrial applications, optimal control problems frequently appear in the context of decision-making under incomplete information. In such framework, decisions must be adapted dynamically to account for possible regime changes of the underlying dynamics. Using stochastic filtering theory, Markovian evolution can be modelled in terms of latent variables, which naturally leads to high-dimensional state space, making practical solutions to these control problems notoriously challenging. In our approach, we utilise a specific structure of this problem class to present a solution in terms of simple, reliable, and fast algorithms. The algorithms presented in this paper have already been implemented in an R package.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP130103315
dc.relation.ispartof	International Journal of Control	en_US
dc.relation.isbasedon	10.1080/00207179.2016.1186841	en_US
dc.subject.classification	Industrial Engineering & Automation	en_US
dc.title	Stochastic switching for partially observable dynamics and optimal asset allocation	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	90	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	0913 Mechanical Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (Teaching and Learning)
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
pubs.organisational-group	/University of Technology Sydney/Strength - QFRC - Quantitative Finance Research Centre
utslib.copyright.status	closed_access
pubs.issue	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	90	en_US

Abstract:

© 2016 Informa UK Limited, trading as Taylor & Francis Group. In industrial applications, optimal control problems frequently appear in the context of decision-making under incomplete information. In such framework, decisions must be adapted dynamically to account for possible regime changes of the underlying dynamics. Using stochastic filtering theory, Markovian evolution can be modelled in terms of latent variables, which naturally leads to high-dimensional state space, making practical solutions to these control problems notoriously challenging. In our approach, we utilise a specific structure of this problem class to present a solution in terms of simple, reliable, and fast algorithms. The algorithms presented in this paper have already been implemented in an R package.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/124928