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Particle Filtering

In: Handbook of Financial Time Series

Author

Listed:
  • Michael Johannes

    (Columbia University, Graduate School of Business)

  • Nicholas Polson

    (University of Chicago, Graduate School of Business,)

Abstract

This chapter provides an overview of particle filters. Particle filters generate approximations to filtering distributions and are commonly used in non-linear and/or non-Gaussian state space models. We discuss general concepts associated with particle filtering, provide an overview of the main particle filtering algorithms, and provide an empirical example of filtering volatility from noisy asset price data.

Suggested Citation

  • Michael Johannes & Nicholas Polson, 2009. "Particle Filtering," Springer Books, in: Thomas Mikosch & Jens-Peter Kreiß & Richard A. Davis & Torben Gustav Andersen (ed.), Handbook of Financial Time Series, chapter 44, pages 1015-1029, Springer.
    • Handle: RePEc:spr:sprchp:978-3-540-71297-8_44
    DOI: 10.1007/978-3-540-71297-8_44
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    Cited by:

    1. Hasumi, Ryo & Iiboshi, Hirokuni & Matsumae, Tatsuyoshi & Nakamura, Daisuke, 2019. "Does a financial accelerator improve forecasts during financial crises? Evidence from Japan with prediction-pooling methods," Journal of Asian Economics, Elsevier, vol. 60(C), pages 45-68.

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