Parameter Learning and Change Detection Using a Particle Filter with Accelerated Adaptation

Gellert, K; Schlögl, E

Parameter Learning and Change Detection Using a Particle Filter with Accelerated Adaptation

Gellert, K Schlögl, E

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Publisher:: MDPI AG
Publication Type:: Journal Article
Citation:: Risks, 2021, 9, (12), pp. 228
Issue Date:: 2021-12-16

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Field	Value	Language
dc.contributor.author	Gellert, K
dc.contributor.author	Schlögl, E
dc.date.accessioned	2022-01-24T04:59:40Z
dc.date.available	2022-01-24T04:59:40Z
dc.date.issued	2021-12-16
dc.identifier.citation	Risks, 2021, 9, (12), pp. 228
dc.identifier.issn	2227-9091
dc.identifier.issn	2227-9091
dc.identifier.uri	http://hdl.handle.net/10453/153508
dc.description.abstract	<jats:p>This paper presents the construction of a particle filter, which incorporates elements inspired by genetic algorithms, in order to achieve accelerated adaptation of the estimated posterior distribution to changes in model parameters. Specifically, the filter is designed for the situation where the subsequent data in online sequential filtering does not match the model posterior filtered based on data up to a current point in time. The examples considered encompass parameter regime shifts and stochastic volatility. The filter adapts to regime shifts extremely rapidly and delivers a clear heuristic for distinguishing between regime shifts and stochastic volatility, even though the model dynamics assumed by the filter exhibit neither of those features.</jats:p>
dc.language	en
dc.publisher	MDPI AG
dc.relation.ispartof	Risks
dc.relation.isbasedon	10.3390/risks9120228
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	1502 Banking, Finance and Investment, 1503 Business and Management
dc.title	Parameter Learning and Change Detection Using a Particle Filter with Accelerated Adaptation
dc.type	Journal Article
utslib.citation.volume	9
utslib.for	1502 Banking, Finance and Investment
utslib.for	1503 Business and Management
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Strength - QFRC - Quantitative Finance Research Centre
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
utslib.copyright.status	open_access	*
dc.date.updated	2022-01-24T04:59:38Z
pubs.issue	12
pubs.publication-status	Published online
pubs.volume	9
utslib.citation.issue	12

Abstract:

This paper presents the construction of a particle filter, which incorporates elements inspired by genetic algorithms, in order to achieve accelerated adaptation of the estimated posterior distribution to changes in model parameters. Specifically, the filter is designed for the situation where the subsequent data in online sequential filtering does not match the model posterior filtered based on data up to a current point in time. The examples considered encompass parameter regime shifts and stochastic volatility. The filter adapts to regime shifts extremely rapidly and delivers a clear heuristic for distinguishing between regime shifts and stochastic volatility, even though the model dynamics assumed by the filter exhibit neither of those features.

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http://hdl.handle.net/10453/153508