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

Karol Gellert and Erik Schlogl
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Karol Gellert: Finance Discipline Group, UTS Business School, University of Technology Sydney, http://www.uts.edu.au/about/uts-business-school/finance

No 392, Research Paper Series from Quantitative Finance Research Centre, University of Technology, Sydney

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.

Pages: 38 pages
Date: 2018-06-01
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
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https://www.uts.edu.au/sites/default/files/article/downloads/rp392.pdf (application/pdf)

Related works:
Journal Article: Parameter Learning and Change Detection Using a Particle Filter with Accelerated Adaptation (2021)
Working Paper: Parameter Learning and Change Detection Using a Particle Filter With Accelerated Adaptation (2018)
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