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An efficient sequential learning algorithm in regime-switching environments

An efficient sequential learning algorithm in regime-switching environments AbstractWe provide a novel approach of estimating a regime-switching nonlinear and non-Gaussian state-space model based on a particle learning scheme. In particular, we extend the particle learning method in Liu, J., and M. West. 2001. “Combined Parameter and State Estimation in Simulation-Based Filtering.” In Sequential Monte Carlo Methods in Practice, 197–223. Springer. by constructing a new proposal distribution for the latent regime index variable that incorporates all available information contained in the current and past observations. The Monte Carlo simulation result implies that our approach categorically outperforms a popular existing algorithm. For empirical illustration, the proposed algorithm is used to analyze the underlying dynamics of US excess stock return. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Studies in Nonlinear Dynamics & Econometrics de Gruyter

An efficient sequential learning algorithm in regime-switching environments

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Publisher
de Gruyter
Copyright
©2019 Walter de Gruyter GmbH, Berlin/Boston
ISSN
1558-3708
eISSN
1558-3708
DOI
10.1515/snde-2018-0016
Publisher site
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Abstract

AbstractWe provide a novel approach of estimating a regime-switching nonlinear and non-Gaussian state-space model based on a particle learning scheme. In particular, we extend the particle learning method in Liu, J., and M. West. 2001. “Combined Parameter and State Estimation in Simulation-Based Filtering.” In Sequential Monte Carlo Methods in Practice, 197–223. Springer. by constructing a new proposal distribution for the latent regime index variable that incorporates all available information contained in the current and past observations. The Monte Carlo simulation result implies that our approach categorically outperforms a popular existing algorithm. For empirical illustration, the proposed algorithm is used to analyze the underlying dynamics of US excess stock return.

Journal

Studies in Nonlinear Dynamics & Econometrics – de Gruyter

Published: Jun 26, 2019

References

  • The Risk Return Tradeoff in the Long Run: 1836–2003
  • Regime Changes and Financial Markets
  • The Stochastic Behavior of Common Stock Variances: Value, Leverage and Interest Rate Effects
  • Estimation and Comparison of Multiple Change-Point Models