Particle Filters for Markov Switching Stochastic Volatility Models
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Cited by:
- Gorynin, Ivan & Derrode, Stéphane & Monfrini, Emmanuel & Pieczynski, Wojciech, 2017. "Fast smoothing in switching approximations of non-linear and non-Gaussian models," Computational Statistics & Data Analysis, Elsevier, vol. 114(C), pages 38-46.
- 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.
- Karol Gellert & Erik Schlogl, 2018. "Parameter Learning and Change Detection Using a Particle Filter With Accelerated Adaptation," Papers 1806.05387, arXiv.org.
- 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.
- Lux, Thomas, 2017. "Estimation of agent-based models using sequential Monte Carlo methods," Economics Working Papers 2017-07, Christian-Albrechts-University of Kiel, Department of Economics.
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More about this item
Keywords
Particle filters; Markov switching stochastic volatility models; Sequential Monte Carlo simulation;All these keywords.
JEL classification:
- C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
- D11 - Microeconomics - - Household Behavior - - - Consumer Economics: Theory
NEP fields
This paper has been announced in the following NEP Reports:- NEP-CMP-2012-02-20 (Computational Economics)
- NEP-ECM-2012-02-20 (Econometrics)
- NEP-ETS-2012-02-20 (Econometric Time Series)
- NEP-ORE-2012-02-20 (Operations Research)
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