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Adaptive Filtering, Nonlinear State-Space Models, and Applications in Finance and Econometrics

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State-Space Models

Part of the book series: Statistics and Econometrics for Finance ((SEFF,volume 1))

Abstract

Sequential Monte Carlo methods, also known as particle filters, have far-reaching and powerful applications in modern time series analysis problems involving state-space models. In this chapter we describe important application areas in finance and economics, including frailty models for portfolio default probabilities, stochastic volatility models with contemporaneous price and volatility jumps, and hidden Markov models for high-frequency transaction data. We review particle filters for estimating the latent states under the assumption that the parameters are known and their modifications without such an assumption. We also describe a new adaptive particle filter that uses a computationally efficient Markov Chain Monte Carlo estimate of the posterior distribution of the state-space model parameters in conjunction with sequential state estimation.

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Author information

Authors and Affiliations

  1. Department of Statistics, Stanford University, Stanford, CA, USA

    Tze Leung Lai

  2. Department of Management Science and Engineering, Stanford University, Stanford, CA, USA

    Vibhav Bukkapatanam

Authors
  1. Tze Leung Lai
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  2. Vibhav Bukkapatanam
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Correspondence to Tze Leung Lai .

Editor information

Editors and Affiliations

  1. Department of Mathematics and Statistics, University of Missouri at Kansas City, Kansas City, Missouri, USA

    Yong Zeng

  2. Department of Economics, The University of Kansas, Lawrence, Kansas, USA

    Shu Wu

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Lai, T.L., Bukkapatanam, V. (2013). Adaptive Filtering, Nonlinear State-Space Models, and Applications in Finance and Econometrics. In: Zeng, Y., Wu, S. (eds) State-Space Models. Statistics and Econometrics for Finance, vol 1. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7789-1_1

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