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

Author

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  • Karol Gellert

    (Quantitative Finance Research Centre, University of Technology Sydney, Sydney, NSW 2007, Australia)

  • Erik Schlögl

    (Quantitative Finance Research Centre, University of Technology Sydney, Sydney, NSW 2007, Australia
    The African Institute for Financial Markets and Risk Management (AIFMRM), University of Cape Town, Western Cape 7700, South Africa
    Department of Statistics, Faculty of Science, University of Johannesburg, Auckland Park 2006, South Africa)

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.

Suggested Citation

  • Karol Gellert & Erik Schlögl, 2021. "Parameter Learning and Change Detection Using a Particle Filter with Accelerated Adaptation," Risks, MDPI, vol. 9(12), pages 1-18, December.
    • Handle: RePEc:gam:jrisks:v:9:y:2021:i:12:p:228-:d:704389
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    References listed on IDEAS

    as
    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.
    2. Yun Bao & Carl Chiarella & Boda Kang, 2012. "Particle Filters for Markov Switching Stochastic Volatility Models," Research Paper Series 299, Quantitative Finance Research Centre, University of Technology, Sydney.
    3. Neil Shephard & Thomas Flury, 2009. "Learning and filtering via simulation: smoothly jittered particle filters," Economics Series Working Papers 469, University of Oxford, Department of Economics.
    4. 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.
    5. Hedibert F. Lopes & Ruey S. Tsay, 2011. "Particle filters and Bayesian inference in financial econometrics," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(1), pages 168-209, January.
    6. repec:dau:papers:123456789/6066 is not listed on IDEAS
    7. Nicolas Chopin & Alessandra Iacobucci & Jean-Michel Marin & Kerrie L. Mengersen & Christian P. Robert & Robin Ryder & Christian Schafer, 2010. "On Particle Learning," Working Papers 2010-22, Center for Research in Economics and Statistics.
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