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Thomas Flury

Personal Details

First Name:Thomas
Middle Name:
Last Name:Flury
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RePEc Short-ID:pfl67
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Affiliation

(in no particular order)

Oxford-Man Institute of Quantitative Finance
Oxford University

Oxford, United Kingdom
http://www.oxford-man.ox.ac.uk/
RePEc:edi:omioxuk (more details at EDIRC)

Department of Economics
Oxford University

Oxford, United Kingdom
http://www.economics.ox.ac.uk/
RePEc:edi:sfeixuk (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Neil Shephard & Thomas Flury, 2009. "Learning and filtering via simulation: smoothly jittered particle filters," Economics Series Working Papers 469, University of Oxford, Department of Economics.
  2. Thomas Flury & Neil Shephard, 2008. "Bayesian inference based only on simulated likelihood: particle filter analysis of dynamic economic models," OFRC Working Papers Series 2008fe32, Oxford Financial Research Centre.

Articles

  1. Flury, Thomas & Shephard, Neil, 2011. "Bayesian Inference Based Only On Simulated Likelihood: Particle Filter Analysis Of Dynamic Economic Models," Econometric Theory, Cambridge University Press, vol. 27(5), pages 933-956, October.

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

  1. Neil Shephard & Thomas Flury, 2009. "Learning and filtering via simulation: smoothly jittered particle filters," Economics Series Working Papers 469, University of Oxford, Department of Economics.

    Cited by:

    1. 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.
    2. Andras Fulop & Junye Li & Jun Yu, 2012. "Bayesian Learning of Impacts of Self-Exciting Jumps in Returns and Volatility," Working Papers 03-2012, Singapore Management University, School of Economics.
    3. Duc Pham-Hi, 2014. "Shadow banking dynamics and learning behaviour," EcoMod2014 6920, EcoMod.
    4. Andras Fulop & Junye Li & Jun Yu, 2012. "Investigating Impacts of Self-Exciting Jumps in Returns and Volatility: A Bayesian Learning Approach," Global COE Hi-Stat Discussion Paper Series gd12-264, Institute of Economic Research, Hitotsubashi University.
    5. Fulop, Andras & Li, Junye, 2013. "Efficient learning via simulation: A marginalized resample-move approach," Journal of Econometrics, Elsevier, vol. 176(2), pages 146-161.
    6. Andreasen, Martin & Meldrum, Andrew, 2013. "Likelihood inference in non-linear term structure models: the importance of the lower bound," Bank of England working papers 481, Bank of England.

  2. Thomas Flury & Neil Shephard, 2008. "Bayesian inference based only on simulated likelihood: particle filter analysis of dynamic economic models," OFRC Working Papers Series 2008fe32, Oxford Financial Research Centre.

    Cited by:

    1. Leopoldo Catania & Nima Nonejad, 2016. "Density Forecasts and the Leverage Effect: Some Evidence from Observation and Parameter-Driven Volatility Models," Papers 1605.00230, arXiv.org, revised Nov 2016.
    2. Guofang Huang & Hong Luo & Jing Xia, 2019. "Invest in Information or Wing It? A Model of Dynamic Pricing with Seller Learning," Management Science, INFORMS, vol. 65(12), pages 5556-5583, December.
    3. Martin T. Bohl & Nicole Branger & Mark Trede, 2022. "Measurement errors in index trader positions data: Is the price pressure hypothesis still invalid?," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 44(3), pages 1534-1553, September.
    4. Luc Bauwens & Arnaud Dufays & Jeroen V.K. Rombouts, 2011. "Marginal Likelihood for Markov-Switching and Change-Point GARCH Models," Cahiers de recherche 1138, CIRPEE.
    5. Papp, Tamás K. & Reiter, Michael, 2020. "Estimating linearized heterogeneous agent models using panel data," Journal of Economic Dynamics and Control, Elsevier, vol. 115(C).
    6. Brix, Anne Floor & Lunde, Asger & Wei, Wei, 2018. "A generalized Schwartz model for energy spot prices — Estimation using a particle MCMC method," Energy Economics, Elsevier, vol. 72(C), pages 560-582.
    7. Joshua C.C. Chan & Rodney Strachan, 2014. "The Zero Lower Bound: Implications for Modelling the Interest Rate," Working Paper series 42_14, Rimini Centre for Economic Analysis.
    8. Matias Quiroz & Mattias Villani & Robert Kohn & Minh-Ngoc Tran & Khue-Dung Dang, 2018. "Subsampling MCMC - an Introduction for the Survey Statistician," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 80(1), pages 33-69, December.
    9. Tsionas, Mike G. & Malikov, Emir & Kumbhakar, Subal C., 2019. "Endogenous Dynamic Efficiency in the Intertemporal Optimization Models of Firm Behavior," MPRA Paper 97780, University Library of Munich, Germany.
    10. Jesús Fernández-Villaverde & Pablo Guerrón-Quintana & Juan F. Rubio-Ramírez, 2014. "Estimating Dynamic Equilibrium Models with Stochastic Volatility," Working Papers 2014-11, FEDEA.
    11. Rhys M. Bidder & Matthew E. Smith, 2013. "Doubts and Variability: A Robust Perspective on Exotic Consumption Series," Working Paper Series 2013-28, Federal Reserve Bank of San Francisco.
    12. Creal, Drew D. & Tsay, Ruey S., 2015. "High dimensional dynamic stochastic copula models," Journal of Econometrics, Elsevier, vol. 189(2), pages 335-345.
    13. Andrew Binning & Junior Maih, 2015. "Sigma Point Filters For Dynamic Nonlinear Regime Switching Models," Working Papers No 4/2015, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    14. Emmanuel C. Mamatzakis & Mike G. Tsionas, 2021. "A Bayesian panel stochastic volatility measure of financial stability," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(4), pages 5363-5384, October.
    15. Ron Gallant & Raffaella Giacomini & Giuseppe Ragusa, 2013. "Generalized method of moments with latent variables," CeMMAP working papers CWP50/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    16. Matthew Smith, 2012. "Estimating Nonlinear Economic Models Using Surrogate Transitions," 2012 Meeting Papers 494, Society for Economic Dynamics.
    17. Rubio-Ramírez, Juan Francisco & Schorfheide, Frank & Fernández-Villaverde, Jesús, 2015. "Solution and Estimation Methods for DSGE Models," CEPR Discussion Papers 11032, C.E.P.R. Discussion Papers.
    18. Pablo A. Cuba-Borda & Luca Guerrieri & Matteo Iacoviello & Molin Zhong, 2019. "Likelihood Evaluation of Models with Occasionally Binding Constraints," Finance and Economics Discussion Series 2019-028, Board of Governors of the Federal Reserve System (U.S.).
    19. Joshua Chan & Rodney Strachan, 2012. "Estimation in Non-Linear Non-Gaussian State Space Models with Precision-Based Methods," CAMA Working Papers 2012-13, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    20. Elmar Mertens & James M. Nason, 2018. "Inflation and professional forecast dynamics: an evaluation of stickiness, persistence, and volatility," BIS Working Papers 713, Bank for International Settlements.
    21. Wolf, Elias, 2022. "Estimating growth at risk with skewed stochastic volatility models," Discussion Papers 2022/2, Free University Berlin, School of Business & Economics.
    22. Laura Liu & Mikkel Plagborg-M?ller, 2021. "Full-Information Estimation of Heterogeneous Agent Models Using Macro and Micro Data," CAEPR Working Papers 2021-001 Classification- , Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    23. Neil Shephard, 2013. "Martingale unobserved component models," Economics Papers 2013-W01, Economics Group, Nuffield College, University of Oxford.
    24. Yumiao Tian & Maorong Ge & Frank Neitzel, 2020. "Variance Reduction of Sequential Monte Carlo Approach for GNSS Phase Bias Estimation," Mathematics, MDPI, vol. 8(4), pages 1-15, April.
    25. A. Ronald Gallant & Han Hong & Ahmed Khwaja, 2012. "Bayesian Estimation of a Dynamic Game with Endogenous, Partially Observed, Serially Correlated State," Working Papers 12-01, Duke University, Department of Economics.
    26. Nonejad Nima, 2015. "Particle Gibbs with ancestor sampling for stochastic volatility models with: heavy tails, in mean effects, leverage, serial dependence and structural breaks," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 19(5), pages 561-584, December.
    27. Arnaud Doucet & Neil Shephard, 2012. "Robust inference on parameters via particle filters and sandwich covariance matrices," Economics Papers 2012-W05, Economics Group, Nuffield College, University of Oxford.
    28. Angelo Marsiglia Fasolo, 2012. "A Note on Particle Filters Applied to DSGE Models," Working Papers Series 281, Central Bank of Brazil, Research Department.
    29. Cameron Fen, 2022. "Fast Simulation-Based Bayesian Estimation of Heterogeneous and Representative Agent Models using Normalizing Flow Neural Networks," Papers 2203.06537, arXiv.org.
    30. Nonejad, Nima, 2014. "Particle Markov Chain Monte Carlo Techniques of Unobserved Component Time Series Models Using Ox," MPRA Paper 55662, University Library of Munich, Germany.
    31. Tsionas, Mike & Patel, Pankaj C. & Guedes, Maria João, 2022. "Endogenous efficiency of the dynamic profit maximization in the intertemporal production models of venture behavior," International Journal of Production Economics, Elsevier, vol. 246(C).
    32. Man Chung Fung & Gareth W. Peters & Pavel V. Shevchenko, 2016. "A unified approach to mortality modelling using state-space framework: characterisation, identification, estimation and forecasting," Papers 1605.09484, arXiv.org.
    33. Nima Nonejad, 2013. "Particle Markov Chain Monte Carlo Techniques of Unobserved Component Time Series Models Using Ox," CREATES Research Papers 2013-27, Department of Economics and Business Economics, Aarhus University.
    34. Delis, Manthos D. & Tsionas, Mike G., 2018. "Measuring management practices," International Journal of Production Economics, Elsevier, vol. 199(C), pages 65-77.
    35. Luisa Corrado & Stefano Grassi & Aldo Paolillo, 2021. "Modelling and Estimating Large Macroeconomic Shocks During the Pandemic," National Institute of Economic and Social Research (NIESR) Discussion Papers 530, National Institute of Economic and Social Research.
    36. Patrick Leung & Catherine S. Forbes & Gael M Martin & Brendan McCabe, 2019. "Forecasting Observables with Particle Filters: Any Filter Will Do!," Monash Econometrics and Business Statistics Working Papers 22/19, Monash University, Department of Econometrics and Business Statistics.
    37. Malik, Sheheryar & Pitt, Michael K., 2011. "Particle filters for continuous likelihood evaluation and maximisation," Journal of Econometrics, Elsevier, vol. 165(2), pages 190-209.
    38. Guay, François & Schwenkler, Gustavo, 2021. "Efficient estimation and filtering for multivariate jump–diffusions," Journal of Econometrics, Elsevier, vol. 223(1), pages 251-275.
    39. Tsionas, Mike G. & Michaelides, Panayotis G., 2017. "Neglected chaos in international stock markets: Bayesian analysis of the joint return–volatility dynamical system," LSE Research Online Documents on Economics 80749, London School of Economics and Political Science, LSE Library.
    40. Tsionas, Mike G. & Michaelides, Panayotis G., 2017. "Bayesian analysis of chaos: The joint return-volatility dynamical system," MPRA Paper 80632, University Library of Munich, Germany.
    41. Scharth, Marcel & Kohn, Robert, 2016. "Particle efficient importance sampling," Journal of Econometrics, Elsevier, vol. 190(1), pages 133-147.
    42. Asger Lunde & Anne Floor Brix & Wei Wei, 2015. "A Generalized Schwartz Model for Energy Spot Prices - Estimation using a Particle MCMC Method," CREATES Research Papers 2015-46, Department of Economics and Business Economics, Aarhus University.
    43. 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.
    44. Gael M. Martin & David T. Frazier & Worapree Maneesoonthorn & Ruben Loaiza-Maya & Florian Huber & Gary Koop & John Maheu & Didier Nibbering & Anastasios Panagiotelis, 2022. "Bayesian Forecasting in Economics and Finance: A Modern Review," Papers 2212.03471, arXiv.org, revised Jul 2023.
    45. Gael M. Martin & David T. Frazier & Ruben Loaiza-Maya & Florian Huber & Gary Koop & John Maheu & Didier Nibbering & Anastasios Panagiotelis, 2023. "Bayesian Forecasting in the 21st Century: A Modern Review," Monash Econometrics and Business Statistics Working Papers 1/23, Monash University, Department of Econometrics and Business Statistics.
    46. Kim, Jaeho, 2015. "Bayesian Inference in a Non-linear/Non-Gaussian Switching State Space Model: Regime-dependent Leverage Effect in the U.S. Stock Market," MPRA Paper 67153, University Library of Munich, Germany.
    47. Wolf, Elias, 2023. "Estimating Growth at Risk with Skewed Stochastic Volatility Models," VfS Annual Conference 2023 (Regensburg): Growth and the "sociale Frage" 277696, Verein für Socialpolitik / German Economic Association.
    48. Nikolaos Englezos & Xanthi Kartala & Phoebe Koundouri & Mike Tsionas & Angelos Alamanos, 2021. "A Novel Hydro - Economic - Econometric Approach for Integrated Transboundary Water Management under Uncertainty," DEOS Working Papers 2101, Athens University of Economics and Business.
    49. Hall, Jamie & Pitt, Michael K. & Kohn, Robert, 2014. "Bayesian inference for nonlinear structural time series models," Journal of Econometrics, Elsevier, vol. 179(2), pages 99-111.
    50. Emmanuel Mamatzakis & Mike Tsionas, 2018. "A Bayesian dynamic model to test persistence in funds' performance," Working Paper series 18-23, Rimini Centre for Economic Analysis.
    51. Gallant, A. Ronald & Giacomini, Raffaella & Ragusa, Giuseppe, 2017. "Bayesian estimation of state space models using moment conditions," Journal of Econometrics, Elsevier, vol. 201(2), pages 198-211.
    52. Celik, Nurcin & Son, Young-Jun, 2011. "State estimation of a shop floor using improved resampling rules for particle filtering," International Journal of Production Economics, Elsevier, vol. 134(1), pages 224-237, November.
    53. Martin T. Bohl & Nicole Branger & Mark Trede, 2019. "Measurement Errors in Index Trader Positions Data: Is the Price Pressure Hypothesis Still Invalid?," CQE Working Papers 8019, Center for Quantitative Economics (CQE), University of Muenster.
    54. Patrick Leung & Catherine S. Forbes & Gael M. Martin & Brendan McCabe, 2016. "Data-driven particle Filters for particle Markov Chain Monte Carlo," Monash Econometrics and Business Statistics Working Papers 17/16, Monash University, Department of Econometrics and Business Statistics.
    55. Andreasen, Martin & Meldrum, Andrew, 2013. "Likelihood inference in non-linear term structure models: the importance of the lower bound," Bank of England working papers 481, Bank of England.
    56. Laura Liu & Mikkel Plagborg‐Møller, 2023. "Full‐information estimation of heterogeneous agent models using macro and micro data," Quantitative Economics, Econometric Society, vol. 14(1), pages 1-35, January.
    57. Roberto Leon-Gonzalez & Blessings Majoni, 2023. "Exact Likelihood for Inverse Gamma Stochastic Volatility Models," Working Paper series 23-11, Rimini Centre for Economic Analysis.
    58. Yang, Yuan & Wang, Lu, 2015. "An Improved Auxiliary Particle Filter for Nonlinear Dynamic Equilibrium Models," Dynare Working Papers 47, CEPREMAP.
    59. A. Ronald Gallant & Han Hong & Ahmed Khwaja, 2018. "The Dynamic Spillovers of Entry: An Application to the Generic Drug Industry," Management Science, INFORMS, vol. 64(3), pages 1189-1211, March.
    60. Martin M. Andreasen, 2010. "Non-linear DSGE Models and The Optimized Particle Filter," CREATES Research Papers 2010-05, Department of Economics and Business Economics, Aarhus University.
    61. Nonejad Nima, 2016. "Particle Markov Chain Monte Carlo Techniques of Unobserved Component Time Series Models Using Ox," Journal of Time Series Econometrics, De Gruyter, vol. 8(1), pages 55-90, January.
    62. 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.
    63. Luisa Corrado & Stefano Grassi & Aldo Paolillo, 2021. "Identifying Economic Shocks in a Rare Disaster Environment," CEIS Research Paper 517, Tor Vergata University, CEIS, revised 19 Nov 2021.
    64. Leland E. Farmer, 2021. "The discretization filter: A simple way to estimate nonlinear state space models," Quantitative Economics, Econometric Society, vol. 12(1), pages 41-76, January.
    65. Nonejad, Nima, 2017. "Parameter instability, stochastic volatility and estimation based on simulated likelihood: Evidence from the crude oil market," Economic Modelling, Elsevier, vol. 61(C), pages 388-408.
    66. Pitt, Michael K. & Silva, Ralph dos Santos & Giordani, Paolo & Kohn, Robert, 2012. "On some properties of Markov chain Monte Carlo simulation methods based on the particle filter," Journal of Econometrics, Elsevier, vol. 171(2), pages 134-151.
    67. Kjartan Kloster Osmundsen & Tore Selland Kleppe & Roman Liesenfeld & Atle Oglend, 2021. "Estimating the Competitive Storage Model with Stochastic Trends in Commodity Prices," Econometrics, MDPI, vol. 9(4), pages 1-24, November.

Articles

  1. Flury, Thomas & Shephard, Neil, 2011. "Bayesian Inference Based Only On Simulated Likelihood: Particle Filter Analysis Of Dynamic Economic Models," Econometric Theory, Cambridge University Press, vol. 27(5), pages 933-956, October.
    See citations under working paper version above.Sorry, no citations of articles recorded.

More information

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Statistics

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Co-authorship network on CollEc

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 2 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.
  1. NEP-ECM: Econometrics (2) 2008-12-07 2010-02-05
  2. NEP-ETS: Econometric Time Series (2) 2008-12-07 2010-02-05
  3. NEP-CMP: Computational Economics (1) 2010-02-05
  4. NEP-DGE: Dynamic General Equilibrium (1) 2008-12-07
  5. NEP-MAC: Macroeconomics (1) 2008-12-07

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