Inference for Lévy‐Driven Stochastic Volatility Models via Adaptive Sequential Monte Carlo
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DOI: j.1467-9469.2010.00723.x
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Cited by:
- Arnaud Dufays, 2014. "On the conjugacy of off-line and on-line Sequential Monte Carlo Samplers," Working Paper Research 263, National Bank of Belgium.
- Luc Bauwens & Jean-François Carpantier & Arnaud Dufays, 2017.
"Autoregressive Moving Average Infinite Hidden Markov-Switching Models,"
Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(2), pages 162-182, April.
- Bauwens, Luc & Carpantier, Jean-François & Dufays, Arnaud, 2015. "Autoregressive moving average infinite hidden markov-switching models," LIDAM Discussion Papers CORE 2015007, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- Luc Bauwens & Jean-François Carpantier & Arnaud Dufays, 2017. "Autoregressive Moving Average Infinite Hidden Markov-Switching Models," Post-Print hal-01795051, HAL.
- Luc BAUWENS & Jean-François CARPENTIER & Arnaud DUFAYS, 2017. "Autoregressive moving average infinite hidden Markov-switching models," LIDAM Reprints CORE 2836, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- Yijie Peng & Michael C. Fu & Jian-Qiang Hu, 2016. "Gradient-based simulated maximum likelihood estimation for stochastic volatility models using characteristic functions," Quantitative Finance, Taylor & Francis Journals, vol. 16(9), pages 1393-1411, September.
- Herbst, Edward & Schorfheide, Frank, 2019.
"Tempered particle filtering,"
Journal of Econometrics, Elsevier, vol. 210(1), pages 26-44.
- Edward P. Herbst & Frank Schorfheide, 2016. "Tempered Particle Filtering," Finance and Economics Discussion Series 2016-072, Board of Governors of the Federal Reserve System (U.S.).
- Edward Herbst & Frank Schorfheide, 2017. "Tempered Particle Filtering," NBER Working Papers 23448, National Bureau of Economic Research, Inc.
- Edward Herbst & Frank Schorfheide, 2016. "Tempered Particle Filtering," PIER Working Paper Archive 16-017, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 25 Oct 2016.
- Dubiel-Teleszynski, Tomasz & Kalogeropoulos, Konstantinos & Karouzakis, Nikolaos, 2024. "Sequential learning and economic benefits from dynamic term structure models," LSE Research Online Documents on Economics 123659, London School of Economics and Political Science, LSE Library.
- Michael Cai & Marco Del Negro & Edward Herbst & Ethan Matlin & Reca Sarfati & Frank Schorfheide, 2021.
"Online estimation of DSGE models,"
The Econometrics Journal, Royal Economic Society, vol. 24(1), pages 33-58.
- Michael Cai & Marco Del Negro & Edward Herbst & Ethan Matlin & Reca Sarfati & Frank Schorfheide, 2019. "Online Estimation of DSGE Models," PIER Working Paper Archive 19-014, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
- Michael Cai & Marco Del Negro & Edward P. Herbst & Ethan Matlin & Reca Sarfati & Frank Schorfheide, 2019. "Online Estimation of DSGE Models," Liberty Street Economics 20190821, Federal Reserve Bank of New York.
- Michael D. Cai & Marco Del Negro & Edward P. Herbst & Ethan Matlin & Reca Sarfati & Frank Schorfheide, 2020. "Online Estimation of DSGE Models," NBER Working Papers 26826, National Bureau of Economic Research, Inc.
- Michael Cai & Marco Del Negro & Edward P. Herbst & Ethan Matlin & Reca Sarfati & Frank Schorfheide, 2019. "Online Estimation of DSGE Models," Staff Reports 893, Federal Reserve Bank of New York.
- Michael Cai & Marco Del Negro & Edward P. Herbst & Ethan Matlin & Reca Sarfati & Frank Schorfheide, 2020. "Online Estimation of DSGE Models," Finance and Economics Discussion Series 2020-023, Board of Governors of the Federal Reserve System (U.S.).
- Sophie Donnet & Stéphane Robin, 2021. "Accelerating Bayesian estimation for network Poisson models using frequentist variational estimates," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(4), pages 858-885, August.
- Arnaud Dufays, 2016.
"Evolutionary Sequential Monte Carlo Samplers for Change-Point Models,"
Econometrics, MDPI, vol. 4(1), pages 1-33, March.
- Arnaud Dufays, 2015. "Evolutionary Sequential Monte Carlo Samplers for Change-point Models," Cahiers de recherche 1508, Centre de recherche sur les risques, les enjeux économiques, et les politiques publiques.
- Arnaud Dufays, 2015. "Evolutionary Sequential Monte Carlo Samplers for Change-point Models," Cahiers de recherche 1518, CIRPEE.
- Drew Creal, 2012.
"A Survey of Sequential Monte Carlo Methods for Economics and Finance,"
Econometric Reviews, Taylor & Francis Journals, vol. 31(3), pages 245-296.
- Creal, D., 2009. "A survey of sequential Monte Carlo methods for economics and finance," Serie Research Memoranda 0018, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
- P. P. Osei & A. Jasra, 2018. "Estimating option prices using multilevel particle filters," Papers 1806.01734, arXiv.org.
- Gunawan, David & Dang, Khue-Dung & Quiroz, Matias & Kohn, Robert & Tran, Minh-Ngoc, 2019. "Subsampling Sequential Monte Carlo for Static Bayesian Models," Working Paper Series 371, Sveriges Riksbank (Central Bank of Sweden).
- Marko Mlikota & Frank Schorfheide, 2022.
"Sequential Monte Carlo With Model Tempering,"
Papers
2202.07070, arXiv.org.
- Mlikota, Marko & Schorfheide, Frank, 2022. "Sequential Monte Carlo With Model Tempering," CEPR Discussion Papers 17035, C.E.P.R. Discussion Papers.
- Dufays, A. & Rombouts, V., 2015. "Sparse Change-Point Time Series Models," LIDAM Discussion Papers CORE 2015032, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- Qi Wang & Jos'e E. Figueroa-L'opez & Todd Kuffner, 2019. "Bayesian Inference on Volatility in the Presence of Infinite Jump Activity and Microstructure Noise," Papers 1909.04853, arXiv.org.
- Ajay Jasra & Kody Law & Carina Suciu, 2020. "Advanced Multilevel Monte Carlo Methods," International Statistical Review, International Statistical Institute, vol. 88(3), pages 548-579, December.
- Moffa, Giusi & Kuipers, Jack, 2014. "Sequential Monte Carlo EM for multivariate probit models," Computational Statistics & Data Analysis, Elsevier, vol. 72(C), pages 252-272.
- Beatrice Franzolini & Alexandros Beskos & Maria De Iorio & Warrick Poklewski Koziell & Karolina Grzeszkiewicz, 2022. "Change point detection in dynamic Gaussian graphical models: the impact of COVID-19 pandemic on the US stock market," Papers 2208.00952, arXiv.org, revised May 2023.
- Laurini, Márcio Poletti & Hotta, Luiz Koodi, 2013. "Indirect Inference in fractional short-term interest rate diffusions," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 94(C), pages 109-126.
- Ho, Paul, 2023.
"Global robust Bayesian analysis in large models,"
Journal of Econometrics, Elsevier, vol. 235(2), pages 608-642.
- Paul Ho, 2019. "Global Robust Bayesian Analysis in Large Models," 2019 Meeting Papers 390, Society for Economic Dynamics.
- Paul Ho, 2020. "Global Robust Bayesian Analysis in Large Models," Working Paper 20-07, Federal Reserve Bank of Richmond.
- Duffield, Samuel & Singh, Sumeetpal S., 2022. "Ensemble Kalman inversion for general likelihoods," Statistics & Probability Letters, Elsevier, vol. 187(C).
- Golchi, Shirin & Campbell, David A., 2016. "Sequentially Constrained Monte Carlo," Computational Statistics & Data Analysis, Elsevier, vol. 97(C), pages 98-113.
- Speich, Matthias & Dormann, Carsten F. & Hartig, Florian, 2021. "Sequential Monte-Carlo algorithms for Bayesian model calibration – A review and method comparison✰," Ecological Modelling, Elsevier, vol. 455(C).
- Yan-Feng Wu & Xiangyu Yang & Jian-Qiang Hu, 2024. "Method of Moments Estimation for Affine Stochastic Volatility Models," Papers 2408.09185, arXiv.org.
- Zhou, Yan, 2015. "vSMC: Parallel Sequential Monte Carlo in C++," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 62(i09).
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