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

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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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  • Karol Gellert & Erik Schlögl, 2018. "Parameter Learning and Change Detection Using a Particle Filter With Accelerated Adaptation," Research Paper Series 392, Quantitative Finance Research Centre, University of Technology, Sydney.
    • Handle: RePEc:uts:rpaper:392
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    1. Carvalho, Carlos M. & Lopes, Hedibert F., 2007. "Simulation-based sequential analysis of Markov switching stochastic volatility models," Computational Statistics & Data Analysis, Elsevier, vol. 51(9), pages 4526-4542, May.
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    6. Xiaojun Yang & Keyi Xing, 2011. "Joint State and Parameter Estimation in Particle Filtering and Stochastic Optimization," Chapters, in: Ioannis Dritsas (ed.), Stochastic Optimization - Seeing the Optimal for the Uncertain, IntechOpen.
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