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Francisco A. A. Blasques

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.

Blog mentions

As found by EconAcademics.org, the blog aggregator for Economics research:
  1. Francisco Blasques, 2013. "Solution-Driven Specification of DSGE Models," Tinbergen Institute Discussion Papers 13-062/III, Tinbergen Institute.

    Mentioned in:

    1. Solution-Driven Specification of DSGE Models
      by Christian Zimmermann in NEP-DGE blog on 2013-05-08 18:34:28

Working papers

  1. Andre Lucas & Anne Opschoor & Luca Rossini, 2021. "Tail Heterogeneity for Dynamic Covariance Matrices: the F-Riesz Distribution," Tinbergen Institute Discussion Papers 21-010/III, Tinbergen Institute, revised 11 Jul 2023.

    Cited by:

    1. Abdelhamid Hassairi & Fatma Ktari & Raoudha Zine, 2022. "On the Gaussian representation of the Riesz probability distribution on symmetric matrices," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 106(4), pages 609-632, December.

  2. Francisco Blasques & Meindert Heres Hoogerkamp & Siem Jan Koopman & Ilka van de Werve, 2020. "Dynamic Factor Models with Clustered Loadings: Forecasting Education Flows using Unemployment Data," Tinbergen Institute Discussion Papers 20-078/III, Tinbergen Institute, revised 21 Jan 2021.

    Cited by:

    1. Escribano, Alvaro & Peña, Daniel & Ruiz, Esther, 2021. "30 years of cointegration and dynamic factor models forecasting and its future with big data: Editorial," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1333-1337.

  3. Francisco Blasques & Christian Francq & Sébastien Laurent, 2020. "A New Class of Robust Observation-Driven Models," Tinbergen Institute Discussion Papers 20-073/III, Tinbergen Institute.

    Cited by:

    1. Paul Labonne, 2022. "Asymmetric Uncertainty: Nowcasting Using Skewness in Real-time Data," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2022-23, Economic Statistics Centre of Excellence (ESCoE).
    2. Blasques, F. & Francq, Christian & Laurent, Sébastien, 2023. "Quasi score-driven models," Journal of Econometrics, Elsevier, vol. 234(1), pages 251-275.

  4. Francisco Blasques & Vladimir Holy & Petra Tomanova, 2019. "Zero-Inflated Autoregressive Conditional Duration Model for Discrete Trade Durations with Excessive Zeros," Tinbergen Institute Discussion Papers 19-004/III, Tinbergen Institute.

    Cited by:

    1. Petra Tomanová & Vladimír Holý, 2021. "Clustering of arrivals in queueing systems: autoregressive conditional duration approach," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 29(3), pages 859-874, September.

  5. Francisco (F.) Blasques & Paolo Gorgi & Siem Jan (S.J.) Koopman, 2018. "Missing Observations in Observation-Driven Time Series Models," Tinbergen Institute Discussion Papers 18-013/III, Tinbergen Institute.

    Cited by:

    1. Luisa Bisaglia & Matteo Grigoletto, 2021. "A new time-varying model for forecasting long-memory series," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(1), pages 139-155, March.
    2. Harvey, A., 2021. "Score-driven time series models," Cambridge Working Papers in Economics 2133, Faculty of Economics, University of Cambridge.

  6. Francisco (F.) Blasques & Siem Jan (S.J.) Koopman & Marc Nientker, 2018. "A Time-Varying Parameter Model for Local Explosions," Tinbergen Institute Discussion Papers 18-088/III, Tinbergen Institute.

    Cited by:

    1. Fries, Sébastien, 2018. "Conditional moments of noncausal alpha-stable processes and the prediction of bubble crash odds," MPRA Paper 97353, University Library of Munich, Germany, revised Nov 2019.
    2. Blasques, Francisco & Nientker, Marc, 2023. "Stochastic properties of nonlinear locally-nonstationary filters," Journal of Econometrics, Elsevier, vol. 235(2), pages 2082-2095.
    3. Xuanling Yang & Dong Li & Ting Zhang, 2024. "A simple stochastic nonlinear AR model with application to bubble," Papers 2401.07038, arXiv.org.

  7. Francisco (F.) Blasques & Andre (A.) Lucas & Andries van Vlodrop, 2017. "Finite Sample Optimality of Score-Driven Volatility Models," Tinbergen Institute Discussion Papers 17-111/III, Tinbergen Institute.

    Cited by:

    1. Domenico Di Gangi & Giacomo Bormetti & Fabrizio Lillo, 2022. "Score Driven Generalized Fitness Model for Sparse and Weighted Temporal Networks," Papers 2202.09854, arXiv.org, revised Mar 2022.
    2. Carlo Campajola & Domenico Di Gangi & Fabrizio Lillo & Daniele Tantari, 2020. "Modelling time-varying interactions in complex systems: the Score Driven Kinetic Ising Model," Papers 2007.15545, arXiv.org, revised Aug 2021.

  8. Francisco (F.) Blasques & Marc Nientker, 2017. "A Stochastic Recurrence Equation Approach to Stationarity and phi-Mixing of a Class of Nonlinear ARCH Models," Tinbergen Institute Discussion Papers 17-072/III, Tinbergen Institute.

    Cited by:

    1. Blasques, Francisco & Koopman, Siem Jan & Nientker, Marc, 2022. "A time-varying parameter model for local explosions," Journal of Econometrics, Elsevier, vol. 227(1), pages 65-84.

  9. Bo Pieter Johannes Andree & Francisco Blasques & Eric Koomen, 2017. "Smooth Transition Spatial Autoregressive Models," Tinbergen Institute Discussion Papers 17-050/III, Tinbergen Institute.

    Cited by:

    1. Francisco Blasques & Vladimir Holy & Petra Tomanova, 2019. "Zero-Inflated Autoregressive Conditional Duration Model for Discrete Trade Durations with Excessive Zeros," Tinbergen Institute Discussion Papers 19-004/III, Tinbergen Institute.

  10. F Blasques & P Gorgi & S Koopman & O Wintenberger, 2016. "Feasible Invertibility Conditions for Maximum Likelihood Estimation for Observation-Driven Models," Papers 1610.02863, arXiv.org.

    Cited by:

    1. Vladimír Holý & Jan Zouhar, 2022. "Modelling time‐varying rankings with autoregressive and score‐driven dynamics," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1427-1450, November.
    2. Francisco Blasques & Siem Jan Koopman & Gabriele Mingoli, 2023. "Observation-Driven filters for Time-Series with Stochastic Trends and Mixed Causal Non-Causal Dynamics," Tinbergen Institute Discussion Papers 23-065/III, Tinbergen Institute.
    3. Blasques, F. & Gorgi, P. & Koopman, S.J., 2021. "Missing observations in observation-driven time series models," Journal of Econometrics, Elsevier, vol. 221(2), pages 542-568.
    4. Hafner, Christian & Kyriakopoulou, Dimitra, 2020. "Exponential-Type GARCH Models With Linear-in-Variance Risk Premium," LIDAM Reprints ISBA 2020029, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    5. Serge Darolles & Christian Francq & Sébastien Laurent, 2018. "Asymptotics of Cholesky GARCH Models and Time-Varying Conditional Betas," Working Papers halshs-01944656, HAL.

  11. Francisco Blasques & Paolo Gorgi & Siem Jan Koopman & Olivier Wintenberger, 2016. "Feasible Invertibility Conditions and Maximum Likelihood Estimation for Observation-Driven Models," Tinbergen Institute Discussion Papers 16-082/III, Tinbergen Institute.

    Cited by:

    1. Harvey, A. & Palumbo, D., 2019. "Score-Driven Models for Realized Volatility," Cambridge Working Papers in Economics 1950, Faculty of Economics, University of Cambridge.
    2. Blazsek, Szabolcs & Escribano, Álvaro & Licht, Adrian, 2018. "Seasonal Quasi-Vector Autoregressive Models with an Application to Crude Oil Production and Economic Activity in the United States and Canada," UC3M Working papers. Economics 27484, Universidad Carlos III de Madrid. Departamento de Economía.
    3. Luisa Bisaglia & Matteo Grigoletto, 2018. "A new time-varying model for forecasting long-memory series," Papers 1812.07295, arXiv.org.
    4. Aknouche, Abdelhakim & Francq, Christian, 2023. "Two-stage weighted least squares estimator of the conditional mean of observation-driven time series models," Journal of Econometrics, Elsevier, vol. 237(2).
    5. Blasques, F. & Gorgi, P. & Koopman, S.J., 2021. "Missing observations in observation-driven time series models," Journal of Econometrics, Elsevier, vol. 221(2), pages 542-568.
    6. Blazsek, Szabolcs & Escribano, Álvaro & Licht, Adrian, 2020. "Prediction accuracy of bivariate score-driven risk premium and volatility filters: an illustration for the Dow Jones," UC3M Working papers. Economics 31339, Universidad Carlos III de Madrid. Departamento de Economía.
    7. Hafner, Christian & Kyriakopoulou, Dimitra, 2020. "Exponential-Type GARCH Models With Linear-in-Variance Risk Premium," LIDAM Reprints ISBA 2020029, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    8. Paolo Gorgi & Siem Jan Koopman, 2020. "Beta observation-driven models with exogenous regressors: a joint analysis of realized correlation and leverage effects," Tinbergen Institute Discussion Papers 20-004/III, Tinbergen Institute.
    9. Harvey, A. & Hurn, S. & Thiele, S., 2019. "Modeling directional (circular) time series," Cambridge Working Papers in Economics 1971, Faculty of Economics, University of Cambridge.
    10. Serge Darolles & Christian Francq & Sébastien Laurent, 2018. "Asymptotics of Cholesky GARCH Models and Time-Varying Conditional Betas," Working Papers halshs-01944656, HAL.
    11. Ayala, Astrid & Blazsek, Szabolcs & Escribano, Álvaro, 2019. "Score-driven time series models with dynamic shape : an application to the Standard & Poor's 500 index," UC3M Working papers. Economics 28133, Universidad Carlos III de Madrid. Departamento de Economía.
    12. Blazsek, Szabolcs & Escribano, Álvaro & Licht, Adrian, 2018. "Seasonality Detection in Small Samples using Score-Driven Nonlinear Multivariate Dynamic Location Models," UC3M Working papers. Economics 27483, Universidad Carlos III de Madrid. Departamento de Economía.
    13. Babii, Andrii & Chen, Xi & Ghysels, Eric, 2019. "Commercial and Residential Mortgage Defaults: Spatial Dependence with Frailty," Journal of Econometrics, Elsevier, vol. 212(1), pages 47-77.
    14. Ayala, Astrid & Blazsek, Szabolcs & Escribano, Álvaro, 2019. "Maximum likelihood estimation of score-driven models with dynamic shape parameters : an application to Monte Carlo value-at-risk," UC3M Working papers. Economics 28638, Universidad Carlos III de Madrid. Departamento de Economía.

  12. Francisco Blasques & Paolo Gorgi & Siem Jan Koopman & Olivier Wintenberger, 2015. "A Note on “Continuous Invertibility and Stable QML Estimation of the EGARCH(1,1) Model”," Tinbergen Institute Discussion Papers 15-131/III, Tinbergen Institute.

    Cited by:

    1. F Blasques & P Gorgi & S Koopman & O Wintenberger, 2016. "Feasible Invertibility Conditions for Maximum Likelihood Estimation for Observation-Driven Models," Papers 1610.02863, arXiv.org.
    2. Francisco Blasques & Paolo Gorgi & Siem Jan Koopman & Olivier Wintenberger, 2016. "Feasible Invertibility Conditions and Maximum Likelihood Estimation for Observation-Driven Models," Tinbergen Institute Discussion Papers 16-082/III, Tinbergen Institute.

  13. Francisco Blasques & Artem Duplinskiy, 2015. "Penalized Indirect Inference," Tinbergen Institute Discussion Papers 15-009/III, Tinbergen Institute.

    Cited by:

    1. Francisco Blasques & Falk Bräuning & Iman Van Lelyveld, 2016. "A dynamic network model of the unsecured interbank lending market," Working Papers 16-3, Federal Reserve Bank of Boston.
    2. Jump, Robert Calvert & Levine, Paul, 2019. "Behavioural New Keynesian models," Journal of Macroeconomics, Elsevier, vol. 59(C), pages 59-77.
    3. Wang,Dieter & Andree,Bo Pieter Johannes & Chamorro Elizondo,Andres Fernando & Spencer,Phoebe Girouard, 2020. "Stochastic Modeling of Food Insecurity," Policy Research Working Paper Series 9413, The World Bank.
    4. Wang, Dieter & Andrée, Bo Pieter Johannes & Chamorro, Andres Fernando & Spencer, Phoebe Girouard, 2022. "Transitions into and out of food insecurity: A probabilistic approach with panel data evidence from 15 countries," World Development, Elsevier, vol. 159(C).

  14. Francisco Blasques & Siem Jan Koopman & Katarzyna Lasak & André Lucas, 2015. "In-Sample Confidence Bands and Out-of-Sample Forecast Bands for Time-Varying Parameters in Observation Driven Models," Tinbergen Institute Discussion Papers 15-083/III, Tinbergen Institute.

    Cited by:

    1. Mariana Arozo B. de Melo & Cristiano A. C. Fernandes & Eduardo F. L. de Melo, 2018. "Forecasting aggregate claims using score‐driven time series models," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 72(3), pages 354-374, August.
    2. Petrella, Ivan & Delle Monache, Davide & Venditti, Fabrizio, 2019. "Price Dividend Ratio and Long-Run Stock Returns: a Score Driven State Space Model," CEPR Discussion Papers 14107, C.E.P.R. Discussion Papers.
    3. Beutner, Eric & Heinemann, Alexander & Smeekes, Stephan, 2017. "A Justification of Conditional Confidence Intervals," Research Memorandum 023, Maastricht University, Graduate School of Business and Economics (GSBE).
    4. Giacomo Bormetti & Fulvio Corsi, 2021. "A Lucas Critique Compliant SVAR model with Observation-driven Time-varying Parameters," Papers 2107.05263, arXiv.org, revised Feb 2022.
    5. P. Gorgi & Siem Jan (S.J.) Koopman & R. Lit, 2018. "The analysis and forecasting of ATP tennis matches using a high-dimensional dynamic model," Tinbergen Institute Discussion Papers 18-009/III, Tinbergen Institute.
    6. Jiawen Xu & Pierre Perron, 2017. "Forecasting in the presence of in and out of sample breaks," Boston University - Department of Economics - Working Papers Series WP2018-014, Boston University - Department of Economics, revised Nov 2018.
    7. Ardia, David & Bluteau, Keven & Boudt, Kris & Catania, Leopoldo, 2018. "Forecasting risk with Markov-switching GARCH models:A large-scale performance study," International Journal of Forecasting, Elsevier, vol. 34(4), pages 733-747.
    8. Anna Gloria Billé & Leopoldo Catania, 2018. "Dynamic Spatial Autoregressive Models with Time-varying Spatial Weighting Matrices," BEMPS - Bozen Economics & Management Paper Series BEMPS55, Faculty of Economics and Management at the Free University of Bozen.
    9. Olofsson, Petter & Råholm, Anna & Uddin, Gazi Salah & Troster, Victor & Kang, Sang Hoon, 2021. "Ethical and unethical investments under extreme market conditions," International Review of Financial Analysis, Elsevier, vol. 78(C).
    10. Giovanni Angelini & Paolo Gorgi, 2018. "DSGE Models with Observation-Driven Time-Varying parameters," Tinbergen Institute Discussion Papers 18-030/III, Tinbergen Institute.
    11. Angelini, Giovanni & Gorgi, Paolo, 2018. "DSGE Models with observation-driven time-varying volatility," Economics Letters, Elsevier, vol. 171(C), pages 169-171.
    12. Paolo Gorgi & Siem Jan Koopman & Rutger Lit, 2020. "Estimation of final standings in football competitions with premature ending: the case of COVID-19," Tinbergen Institute Discussion Papers 20-070/III, Tinbergen Institute.
    13. Bernd Schwaab & Xin Zhang & Andre Lucas, 2020. "Modeling extreme events: time-varying extreme tail shape," Tinbergen Institute Discussion Papers 20-076/III, Tinbergen Institute.
    14. Peng, Kang-Lin & Wu, Chih-Hung & Lin, Pearl M.C. & Kou, IokTeng Esther, 2023. "Investor sentiment in the tourism stock market," Journal of Behavioral and Experimental Finance, Elsevier, vol. 37(C).
    15. Heil, Thomas L.A. & Peter, Franziska J. & Prange, Philipp, 2022. "Measuring 25 years of global equity market co-movement using a time-varying spatial model," Journal of International Money and Finance, Elsevier, vol. 128(C).
    16. Hong, Yanran & Yu, Jize & Su, Yuquan & Wang, Lu, 2023. "Southern oscillation: Great value of its trends for forecasting crude oil spot price volatility," International Review of Economics & Finance, Elsevier, vol. 84(C), pages 358-368.
    17. F. Campigli & G. Bormetti & F. Lillo, 2022. "Measuring price impact and information content of trades in a time-varying setting," Papers 2212.12687, arXiv.org, revised Dec 2023.

  15. Francisco Blasques & Falk Bräuning & Iman van Lelyveld, 2015. "A dynamic network model of the unsecured interbank lending market," BIS Working Papers 491, Bank for International Settlements.

    Cited by:

    1. Peter Grundke, 2019. "Ranking consistency of systemic risk measures: a simulation-based analysis in a banking network model," Review of Quantitative Finance and Accounting, Springer, vol. 52(4), pages 953-990, May.
    2. Olivier Accominotti & Delio Lucena-Piquero & Stefano Ugolini, 2023. "Intermediaries’ Substitutability and Financial Network Resilience: A Hyperstructure Approach," Post-Print hal-04160805, HAL.
    3. Abbassi, Puriya & Bräuning, Falk & Schulze, Niels, 2017. "Bargaining power and outside options in the interbank lending market," Discussion Papers 31/2017, Deutsche Bundesbank.
    4. Co-Pierre Georg & Silvia Gabrieli, 2015. "A Network View on Interbank Market Freezes," Working Papers 488, Economic Research Southern Africa.
    5. Simpson Zhang & Mihaela van der Schaar, 2018. "Reputational Dynamics in Financial Networks During a Crisis," Working Papers 18-03, Office of Financial Research, US Department of the Treasury.
    6. Dietmar Maringer & Ben Craig & Sandra Paterlini, 2022. "Constructing banking networks under decreasing costs of link formation," Computational Management Science, Springer, vol. 19(1), pages 41-64, January.
    7. Marnix Van Soom & Milan Van Den Heuvel & Jan Ryckebusch & Koen Schoors, 2019. "Loan Maturity Aggregation In Interbank Lending Networks Obscures Mesoscale Structure And Economic Functions," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 19/952, Ghent University, Faculty of Economics and Business Administration.
    8. Fernando Linardi & Cees Diks & Marco van der Leij & Iuri Lazier, 2018. "Dynamic Interbank Network Analysis Using Latent Space Models," Working Papers Series 487, Central Bank of Brazil, Research Department.
    9. Rainone, Edoardo, 2020. "The network nature of over-the-counter interest rates," Journal of Financial Markets, Elsevier, vol. 47(C).
    10. Margaretic, Paula & Cifuentes, Rodrigo & Carreño, José Gabriel, 2021. "Banks’ interconnections and peer effects: Evidence from Chile," Research in International Business and Finance, Elsevier, vol. 58(C).
    11. Peter Dietsch, 2021. "Money creation, debt, and justice," Politics, Philosophy & Economics, , vol. 20(2), pages 151-179, May.
    12. Müller, Alexander & Paulick, Jan, 2020. ""The devil is in the details, but so is salvation": Different approachesin money market measurement," Discussion Papers 66/2020, Deutsche Bundesbank.
    13. Coen, Patrick & Coen, Jamie, 2019. "A structural model of interbank network formation and contagion," Bank of England working papers 833, Bank of England.
    14. Chiu, Jonathan & Monnet, Cyril, 2016. "Relationships in the interbank market," Working Paper Series 19479, Victoria University of Wellington, School of Economics and Finance.
    15. Xu, Ying & Corbett, Jenny, 2020. "What a network measure can tell us about financial interconnectedness and output volatility," Journal of the Japanese and International Economies, Elsevier, vol. 58(C).
    16. Craig, Ben & Ma, Yiming, 2022. "Intermediation in the interbank lending market," Journal of Financial Economics, Elsevier, vol. 145(2), pages 179-207.
    17. Anand, Kartik & van Lelyveld, Iman & Banai, Ádám & Friedrich, Soeren & Garratt, Rodney & Hałaj, Grzegorz & Fique, Jose & Hansen, Ib & Martínez Jaramillo, Serafín & Lee, Hwayun & Molina-Borboa, José Lu, 2017. "The missing links: A global study on uncovering financial network structures from partial data," ESRB Working Paper Series 51, European Systemic Risk Board.
    18. Jonathan Chiu & Cyril Monnet, 2016. "Relationships in the Interbank Market," Staff Working Papers 16-33, Bank of Canada.
    19. Gofman, Michael, 2017. "Efficiency and stability of a financial architecture with too-interconnected-to-fail institutions," Journal of Financial Economics, Elsevier, vol. 124(1), pages 113-146.
    20. Zhang, Minghui & He, Jianmin & Li, Shouwei, 2018. "Interbank lending, network structure and default risk contagion," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 493(C), pages 203-209.
    21. Paul Glasserman & H. Peyton Young, 2016. "Contagion in Financial Networks," Journal of Economic Literature, American Economic Association, vol. 54(3), pages 779-831, September.
    22. Bluhm, Marcel, 2018. "Persistent liquidity shocks and interbank funding," Journal of Financial Stability, Elsevier, vol. 36(C), pages 246-262.
    23. Le, Chau & Dickinson, David & Le, Anh, 2022. "Sovereign risk spillovers: A network approach," Journal of Financial Stability, Elsevier, vol. 60(C).
    24. Falk Bräuning & Falko Fecht, 2017. "Relationship Lending in the Interbank Market and the Price of Liquidity," Review of Finance, European Finance Association, vol. 21(1), pages 33-75.
    25. Q. Farooq Akram & Casper Christophersen, 2015. "Pricing in the Norwegian interbank market – the effects of liquidity and implicit government support," Working Paper 2016/2, Norges Bank.
    26. Gaïffas, Stéphane & Matulewicz, Gustaw, 2019. "Sparse inference of the drift of a high-dimensional Ornstein–Uhlenbeck process," Journal of Multivariate Analysis, Elsevier, vol. 169(C), pages 1-20.
    27. Faia, Ester & Aldasoro, Inaki, 2015. "Systemic Loops and Liquidity Regulation," CEPR Discussion Papers 10918, C.E.P.R. Discussion Papers.
    28. Ben R. Craig & Dietmar Maringer & Sandra Paterlini, 2019. "Recreating Banking Networks under Decreasing Fixed Costs," Working Papers 19-21, Federal Reserve Bank of Cleveland.
    29. Bräuning, Falk & Koopman, Siem Jan, 2020. "The dynamic factor network model with an application to international trade," Journal of Econometrics, Elsevier, vol. 216(2), pages 494-515.
    30. Zhang, Simpson & van der Schaar, Mihaela, 2020. "Reputational dynamics in financial networks during a crisis," Journal of Financial Stability, Elsevier, vol. 49(C).
    31. Paul Glasserman & Peyton Young, 2015. "Contagion in Financial Networks," Economics Series Working Papers 764, University of Oxford, Department of Economics.
    32. Edoardo Rainone, 2017. "Pairwise trading in the money market during the European sovereign debt crisis," Temi di discussione (Economic working papers) 1160, Bank of Italy, Economic Research and International Relations Area.
    33. Glasserman, Paul & Young, H. Peyton, 2016. "Contagion in financial networks," LSE Research Online Documents on Economics 68681, London School of Economics and Political Science, LSE Library.
    34. Matteo Chinazzi & Stefano Pegoraro & Giorgio Fagiolo, 2015. "Defuse the Bomb: Rewiring Interbank Networks," LEM Papers Series 2015/16, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    35. Paul Glasserman & H. Peyton Young, 2015. "Contagion in Financial Markets," Working Papers 15-21, Office of Financial Research, US Department of the Treasury.
    36. Tabak, Benjamin Miranda & Silva, Thiago Christiano & Fiche, Marcelo Estrela & Braz, Tércio, 2021. "Citation likelihood analysis of the interbank financial networks literature: A machine learning and bibliometric approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 562(C).
    37. Francesco Palazzo, 2016. "Peer monitoring via loss mutualization," Temi di discussione (Economic working papers) 1088, Bank of Italy, Economic Research and International Relations Area.
    38. Miyakoshi, Tatsuyoshi & Shimada, Junji, 2022. "Network analysis of local currency Asian government bond markets: Assessments of the ABFI and the ABMI," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).
    39. Rünstler, Gerhard, 2016. "Network Dependence in the Euro Area Money Market," Working Paper Series 1887, European Central Bank.
    40. Morteza Alaeddini & Philippe Madiès & Paul J. Reaidy & Julie Dugdale, 2023. "Interbank money market concerns and actors’ strategies—A systematic review of 21st century literature," Journal of Economic Surveys, Wiley Blackwell, vol. 37(2), pages 573-654, April.
    41. Jonathan Chiu & Jens Eisenschmidt & Cyril Monnet, 2019. "Online Appendix to "Relationships in the Interbank Market"," Online Appendices 18-238, Review of Economic Dynamics.
    42. Lux, Thomas, 2015. "Emergence of a core-periphery structure in a simple dynamic model of the interbank market," Journal of Economic Dynamics and Control, Elsevier, vol. 52(C), pages 11-23.

  16. Francisco Blasques & Siem Jan Koopman & André Lucas, 2014. "Maximum Likelihood Estimation for correctly Specified Generalized Autoregressive Score Models: Feedback Effects, Contraction Conditions and Asymptotic Properties," Tinbergen Institute Discussion Papers 14-074/III, Tinbergen Institute.

    Cited by:

    1. Roman Frydman & Soeren Johansen & Anders Rahbek & Morten Nyboe, 2017. "The Qualitative Expectations Hypothesis: Model Ambiguity, Consistent Representations Of Market Forecasts, And Sentiment," Discussion Papers 17-10, University of Copenhagen. Department of Economics.
    2. Mohamed Chikhi & Claude Diebolt & Tapas Mishra, 2019. "Measuring Success: Does Predictive Ability of an Asset Price Rest in 'Memory'? Insights from a New Approach," Working Papers 11-19, Association Française de Cliométrie (AFC).
    3. Francisco Blasques & Christian Francq & Sébastien Laurent, 2020. "A New Class of Robust Observation-Driven Models," Tinbergen Institute Discussion Papers 20-073/III, Tinbergen Institute.
    4. Mohamed CHIKHI & Claude DIEBOLT & Tapas MISHRA, 2019. "Does Predictive Ability of an Asset Price Rest in 'Memory'? Insights from a New Approach," Working Papers of BETA 2019-43, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
    5. Roman Matkovskyy, 2019. "Extremal Economic (Inter)Dependence Studies: A Case of the Eastern European Countries," Post-Print hal-02332090, HAL.
    6. Mohamed Chikhi & Claude Diebolt & Tapas Mishra, 2019. "Memory that Drives! New Insights into Forecasting Performance of Stock Prices from SEMIFARMA-AEGAS Model," Working Papers of BETA 2019-24, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
    7. Hoeltgebaum, Henrique & Borenstein, Denis & Fernandes, Cristiano & Veiga, Álvaro, 2021. "A score-driven model of short-term demand forecasting for retail distribution centers," Journal of Retailing, Elsevier, vol. 97(4), pages 715-725.
    8. Olusanya E. Olubusoye & OlaOluwa S. Yaya, 2016. "Time series analysis of volatility in the petroleum pricing markets: the persistence, asymmetry and jumps in the returns series," OPEC Energy Review, Organization of the Petroleum Exporting Countries, vol. 40(3), pages 235-262, September.

  17. Blasques, Francisco & Koopman, Siem Jan & Lucas, Andre & Schaumburg, Julia, 2014. "Spillover dynamics for systemic risk measurement using spatial financial time series models," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100632, Verein für Socialpolitik / German Economic Association.

    Cited by:

    1. Enzo D'Innocenzo & André Lucas & Anne Opschoor & Xingmin Zhang, 2024. "Heterogeneity and dynamics in network models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(1), pages 150-173, January.
    2. Lu, Yunzhi & Li, Jie & Yang, Haisheng, 2021. "Time-varying inter-urban housing price spillovers in China: Causes and consequences," Journal of Asian Economics, Elsevier, vol. 77(C).
    3. Xu, Yuhong & Yang, Zhenlin, 2020. "Specification Tests for Temporal Heterogeneity in Spatial Panel Data Models with Fixed Effects," Regional Science and Urban Economics, Elsevier, vol. 81(C).
    4. Blasques, F. & Gorgi, P. & Koopman, S.J., 2021. "Missing observations in observation-driven time series models," Journal of Econometrics, Elsevier, vol. 221(2), pages 542-568.
    5. Hongjun Zeng & Ran Lu & Abdullahi D. Ahmed, 2023. "Dynamic dependencies and return connectedness among stock, gold and Bitcoin markets: Evidence from South Asia and China," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 18(1), pages 49-87, March.
    6. Sophie Béreau & Nicolas Debarsy & Cyrille Dossougoin & Jean-Yves Gnabo, 2022. "Contagion in the Banking Industry: a Robust-to-Endogeneity Analysis," Working Papers halshs-03513049, HAL.
    7. Francisco (F.) Blasques & Andre (A.) Lucas & Andries van Vlodrop, 2017. "Finite Sample Optimality of Score-Driven Volatility Models," Tinbergen Institute Discussion Papers 17-111/III, Tinbergen Institute.
    8. Dalhaus, Tatjana & Schaumburg, Julia & Sekhposyan, Tatevik, 2021. "Networking the yield curve: implications for monetary policy," Working Paper Series 2532, European Central Bank.
    9. Mauro Bernardi & Leopoldo Catania, 2015. "Switching-GAS Copula Models With Application to Systemic Risk," Papers 1504.03733, arXiv.org, revised Jan 2016.
    10. Christis Katsouris, 2023. "Quantile Time Series Regression Models Revisited," Papers 2308.06617, arXiv.org, revised Aug 2023.
    11. Blasques, Francisco & van Brummelen, Janneke & Koopman, Siem Jan & Lucas, André, 2022. "Maximum likelihood estimation for score-driven models," Journal of Econometrics, Elsevier, vol. 227(2), pages 325-346.
    12. Marius Amba & Julie Le Gallo, 2022. "Specification and estimation of a periodic spatial panel autoregressive model," Post-Print hal-03910243, HAL.
    13. Leopoldo Catania & Anna Gloria Billé, 2016. "Dynamic Spatial Autoregressive Models with Autoregressive and Heteroskedastic Disturbances," CEIS Research Paper 375, Tor Vergata University, CEIS, revised 31 Mar 2016.
    14. Pino, Gabriel & Herrera, Rodrigo & Rodríguez, Alejandro, 2019. "Geographical spillovers on the relation between risk-taking and market power in the US banking sector," The North American Journal of Economics and Finance, Elsevier, vol. 47(C), pages 351-364.
    15. Nicolas Debarsy & Cyrille Dossougoin & Cem Ertur & Jean-Yves Gnabo, 2018. "Measuring sovereign risk spillovers and assessing the role of transmission channels: A spatial econometrics approach," Post-Print hal-01744629, HAL.
    16. Deng, Chao & Su, Xiaojian & Wang, Gangjin & Peng, Cheng, 2022. "The existence of flight-to-quality under extreme conditions: Evidence from a nonlinear perspective in Chinese stocks and bonds' sectors," Economic Modelling, Elsevier, vol. 113(C).
    17. Matteo Foglia & Eliana Angelini, 2019. "The Time-Spatial Dimension of Eurozone Banking Systemic Risk," Risks, MDPI, vol. 7(3), pages 1-25, July.
    18. Mardi Dungey & Moses Kangogo & Vladimir Volkov, 2022. "Dynamic effects of network exposure on equity markets," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 12(4), pages 569-629, December.
    19. Hannes Böhm & Julia Schaumburg & Lena Tonzer, 2022. "Financial Linkages and Sectoral Business Cycle Synchronization: Evidence from Europe," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 70(4), pages 698-734, December.
    20. Chen, Na & Jin, Xiu, 2020. "Industry risk transmission channels and the spillover effects of specific determinants in China’s stock market: A spatial econometrics approach," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    21. Yun Feng & Xin Li, 2022. "The Cross-Shareholding Network and Risk Contagion from Stochastic Shocks: An Investigation Based on China’s Market," Computational Economics, Springer;Society for Computational Economics, vol. 59(1), pages 357-381, January.
    22. Huang, Jionghao & Li, Ziruo & Xia, Xiaohua, 2021. "Network diffusion of international oil volatility risk in China's stock market: Quantile interconnectedness modelling and shock decomposition analysis," International Review of Economics & Finance, Elsevier, vol. 76(C), pages 1-39.
    23. Ou Bianling & Zhao Xin & Wang Mingxi, 2015. "Power of Moran’s I Test for Spatial Dependence in Panel Data Models with Time Varying Spatial Weights Matrices," Journal of Systems Science and Information, De Gruyter, vol. 3(5), pages 463-471, October.
    24. Peter Schwendner & Martin Schuele & Thomas Ott & Martin Hillebrand, 2015. "European Government Bond Dynamics and Stability Policies: Taming Contagion Risks," Working Papers 8, European Stability Mechanism.
    25. Blasques, Francisco & Lucas, André & van Vlodrop, Andries C., 2021. "Finite Sample Optimality of Score-Driven Volatility Models: Some Monte Carlo Evidence," Econometrics and Statistics, Elsevier, vol. 19(C), pages 47-57.
    26. Billio, Monica & Caporin, Massimiliano & Frattarolo, Lorenzo & Pelizzon, Loriana, 2023. "Networks in risk spillovers: A multivariate GARCH perspective," Econometrics and Statistics, Elsevier, vol. 28(C), pages 1-29.
    27. Füss, Roland & Ruf, Daniel, 2021. "Bank systemic risk exposure and office market interconnectedness," Journal of Banking & Finance, Elsevier, vol. 133(C).
    28. Anna Gloria Billé & Leopoldo Catania, 2018. "Dynamic Spatial Autoregressive Models with Time-varying Spatial Weighting Matrices," BEMPS - Bozen Economics & Management Paper Series BEMPS55, Faculty of Economics and Management at the Free University of Bozen.
    29. Niko Hauzenberger & Michael Pfarrhofer, 2019. "Bayesian state-space modeling for analyzing heterogeneous network effects of US monetary policy," Papers 1911.06206, arXiv.org, revised Sep 2020.
    30. J. W. Muteba Mwamba & Mathias Manguzvane, 2020. "Contagion risk in african sovereign debt markets: A spatial econometrics approach," International Finance, Wiley Blackwell, vol. 23(3), pages 506-536, December.
    31. Giovanni Angelini & Paolo Gorgi, 2018. "DSGE Models with Observation-Driven Time-Varying parameters," Tinbergen Institute Discussion Papers 18-030/III, Tinbergen Institute.
    32. Chen, Na & Jin, Xiu, 2023. "Cross-industry asset allocation with the spatial interaction on multiple risk transmission channels," The North American Journal of Economics and Finance, Elsevier, vol. 67(C).
    33. Lee, Chien-Chiang & Zhou, Hegang & Xu, Chao & Zhang, Xiaoming, 2023. "Dynamic spillover effects among international crude oil markets from the time-frequency perspective," Resources Policy, Elsevier, vol. 80(C).
    34. Geraci, Marco Valerio & Gnabo, Jean-Yves, 2018. "Measuring Interconnectedness between Financial Institutions with Bayesian Time-Varying Vector Autoregressions," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 53(3), pages 1371-1390, June.
    35. Capasso, Salvatore & D'Uva, Marcella & Fiorelli, Cristiana & Napolitano, Oreste, 2023. "Cross-border Italian sovereign risk transmission in EMU countries," Economic Modelling, Elsevier, vol. 126(C).
    36. Eric A. Beutner & Yicong Lin & Andre Lucas, 2023. "Consistency, distributional convergence, and optimality of score-driven filters," Tinbergen Institute Discussion Papers 23-051/III, Tinbergen Institute.
    37. Katarina Valaskova & Tomas Kliestik & Lucia Svabova & Peter Adamko, 2018. "Financial Risk Measurement and Prediction Modelling for Sustainable Development of Business Entities Using Regression Analysis," Sustainability, MDPI, vol. 10(7), pages 1-15, June.
    38. Berloco, Claudia & Argiento, Raffaele & Montagna, Silvia, 2023. "Forecasting short-term defaults of firms in a commercial network via Bayesian spatial and spatio-temporal methods," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1065-1077.
    39. Billio, Monica & Caporin, Massimiliano & Panzica, Roberto Calogero & Pelizzon, Loriana, 2017. "The impact of network connectivity on factor exposures, asset pricing and portfolio diversification," SAFE Working Paper Series 166, Leibniz Institute for Financial Research SAFE.
    40. Debarsy, Nicolas & Yang, Zhenlin, 2018. "Editorial for the special issue entitled: New advances in spatial econometrics: Interactions matter," Regional Science and Urban Economics, Elsevier, vol. 72(C), pages 1-5.
    41. Shinya Fukui, 2020. "Business Cycle Spatial Synchronization: Measuring a Synchronization Parameter," Discussion Papers 2009, Graduate School of Economics, Kobe University.
    42. Chengliang Liu & Qingbin Guo, 2019. "Technology Spillover Effect in China: The Spatiotemporal Evolution and Its Drivers," Sustainability, MDPI, vol. 11(6), pages 1-14, March.
    43. Choi, Sun-Yong, 2022. "Credit risk interdependence in global financial markets: Evidence from three regions using multiple and partial wavelet approaches," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 80(C).
    44. Kangogo, Moses & Volkov, Vladimir, 2021. "Dynamic effects of network exposure on equity markets," Working Papers 2021-03, University of Tasmania, Tasmanian School of Business and Economics.
    45. Harvey, A., 2021. "Score-driven time series models," Cambridge Working Papers in Economics 2133, Faculty of Economics, University of Cambridge.
    46. Ouyang, Ruolan & Chen, Xiang & Fang, Yi & Zhao, Yang, 2022. "Systemic risk of commodity markets: A dynamic factor copula approach," International Review of Financial Analysis, Elsevier, vol. 82(C).
    47. Gül Huyugüzel Kışla & Y. Gülnur Muradoğlu & A. Özlem Önder, 2022. "Spillovers from one country’s sovereign debt to CDS (credit default swap) spreads of others during the European crisis: a spatial approach," Journal of Asset Management, Palgrave Macmillan, vol. 23(4), pages 277-296, July.
    48. David Ardia & Kris Boudt & Leopoldo Catania, 2016. "Generalized Autoregressive Score Models in R: The GAS Package," Papers 1609.02354, arXiv.org.
    49. Hüttner, Amelie & Scherer, Matthias & Gräler, Benedikt, 2020. "Geostatistical modeling of dependent credit spreads: Estimation of large covariance matrices and imputation of missing data," Journal of Banking & Finance, Elsevier, vol. 118(C).
    50. Capasso Salvatore & D’Uva Marcella, & Fiorelli Cristiana & Napolitano Oreste, 2022. "Assessing the Impact of Country-Specific Sovereign Risk on Financial and Banking System in EMU: the Role of Italy," CSEF Working Papers 654, Centre for Studies in Economics and Finance (CSEF), University of Naples, Italy.
    51. Zornitsa Todorova, 2020. "Network Risk in the European Sovereign CDS Market," The Review of Finance and Banking, Academia de Studii Economice din Bucuresti, Romania / Facultatea de Finante, Asigurari, Banci si Burse de Valori / Catedra de Finante, vol. 12(2), pages 137-154, December.
    52. Guo, Juncong & Qu, Xi, 2020. "Fixed effects spatial panel data models with time-varying spatial dependence," Economics Letters, Elsevier, vol. 196(C).
    53. Heil, Thomas L.A. & Peter, Franziska J. & Prange, Philipp, 2022. "Measuring 25 years of global equity market co-movement using a time-varying spatial model," Journal of International Money and Finance, Elsevier, vol. 128(C).
    54. Rubo Zhao & Yixiang Tian & Ao Lei & Francis Boadu & Ze Ren, 2019. "The Effect of Local Government Debt on Regional Economic Growth in China: A Nonlinear Relationship Approach," Sustainability, MDPI, vol. 11(11), pages 1-22, May.
    55. Babii, Andrii & Chen, Xi & Ghysels, Eric, 2019. "Commercial and Residential Mortgage Defaults: Spatial Dependence with Frailty," Journal of Econometrics, Elsevier, vol. 212(1), pages 47-77.
    56. Marco Valerio Geraci & Jean-Yves Gnabo, 2015. "Measuring Interconnectedness between Financial Institutions with Bayesian Time-Varying VARS," Working Papers ECARES ECARES 2015-51, ULB -- Universite Libre de Bruxelles.
    57. Carlos Henrique Dias Cordeiro de Castro & Fernando Antonio Lucena Aiube, 2023. "Forecasting inflation time series using score‐driven dynamic models and combination methods: The case of Brazil," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(2), pages 369-401, March.
    58. Li, Liyao & Yang, Zhenlin, 2020. "Estimation of fixed effects spatial dynamic panel data models with small T and unknown heteroskedasticity," Regional Science and Urban Economics, Elsevier, vol. 81(C).
    59. Yun Feng & Xin Li, 2021. "Does cross-shareholding lead to China's stock returns comovement? Evidence from a GMM-based spatial AR model," Empirical Economics, Springer, vol. 61(6), pages 3213-3237, December.
    60. Bo Pieter Johannes Andree & Francisco Blasques & Eric Koomen, 2017. "Smooth Transition Spatial Autoregressive Models," Tinbergen Institute Discussion Papers 17-050/III, Tinbergen Institute.
    61. F. Blasques & P. Gorgi & S. J. Koopman & J. Sampi, 2023. "Does trade integration imply growth in Latin America? Evidence from a dynamic spatial spillover model," Tinbergen Institute Discussion Papers 23-007/IVI, Tinbergen Institute.
    62. Zheng, Yingfei & Shen, Anran & Li, Ruihai & Yang, Yuhong & Wang, Shengjin & Cheng, Lee-Young, 2023. "Spillover effects between internet financial industry and traditional financial industry: Evidence from the Chinese stock market," Pacific-Basin Finance Journal, Elsevier, vol. 79(C).
    63. Rutger-Jan Lange & Andre Lucas & Arjen H. Siegmann, 2016. "Score-Driven Systemic Risk Signaling for European Sovereign Bond Yields and CDS Spreads," Tinbergen Institute Discussion Papers 16-064/IV, Tinbergen Institute.

  18. Francisco Blasques & Siem Jan Koopman & André Lucas, 2014. "Information Theoretic Optimality of Observation Driven Time Series Models," Tinbergen Institute Discussion Papers 14-046/III, Tinbergen Institute.

    Cited by:

    1. Francisco Blasques & Siem Jan Koopman & André Lucas, 2014. "Maximum Likelihood Estimation for correctly Specified Generalized Autoregressive Score Models: Feedback Effects, Contraction Conditions and Asymptotic Properties," Tinbergen Institute Discussion Papers 14-074/III, Tinbergen Institute.
    2. Lucas, André & Zhang, Xin, 2015. "Score Driven Exponentially Weighted Moving Averages and Value-at-Risk Forecasting," Working Paper Series 309, Sveriges Riksbank (Central Bank of Sweden).
    3. Mauro Bernardi & Leopoldo Catania, 2015. "Switching-GAS Copula Models With Application to Systemic Risk," Papers 1504.03733, arXiv.org, revised Jan 2016.
    4. Lucas, André & Schwaab, Bernd & Zhang, Xin, 2015. "Modeling financial sector joint tail risk in the euro area," Working Paper Series 308, Sveriges Riksbank (Central Bank of Sweden).
    5. Siem Jan Koopman & Rutger Lit & André Lucas, 2015. "Intraday Stock Price Dependence using Dynamic Discrete Copula Distributions," Tinbergen Institute Discussion Papers 15-037/III/DSF90, Tinbergen Institute.
    6. Francisco Blasques & Siem Jan Koopman & Andre Lucas & Julia Schaumburg, 2014. "Spillover Dynamics for Systemic Risk Measurement using Spatial Financial Time Series Models," Tinbergen Institute Discussion Papers 14-107/III, Tinbergen Institute.
    7. Olusanya E. Olubusoye & OlaOluwa S. Yaya, 2016. "Time series analysis of volatility in the petroleum pricing markets: the persistence, asymmetry and jumps in the returns series," OPEC Energy Review, Organization of the Petroleum Exporting Countries, vol. 40(3), pages 235-262, September.
    8. P Gorgi & P R Hansen & P Janus & S J Koopman, 2019. "Realized Wishart-GARCH: A Score-driven Multi-Asset Volatility Model," Journal of Financial Econometrics, Oxford University Press, vol. 17(1), pages 1-32.
    9. Francisco Blasques & Siem Jan Koopman & André Lucas, 2014. "Optimal Formulations for Nonlinear Autoregressive Processes," Tinbergen Institute Discussion Papers 14-103/III, Tinbergen Institute.
    10. Andre Lucas & Bernd Schwaab & Xin Zhang, 2013. "Measuring Credit Risk in a Large Banking System: Econometric Modeling and Empirics," Tinbergen Institute Discussion Papers 13-063/IV/DSF56, Tinbergen Institute, revised 13 Oct 2014.
    11. Pawel Janus & André Lucas & Anne Opschoor & Dick J.C. van Dijk, 2014. "New HEAVY Models for Fat-Tailed Returns and Realized Covariance Kernels," Tinbergen Institute Discussion Papers 14-073/IV, Tinbergen Institute, revised 19 Aug 2015.

  19. Francisco Blasques & Siem Jan Koopman & Andre Lucas, 2014. "Maximum Likelihood Estimation for Score-Driven Models," Tinbergen Institute Discussion Papers 14-029/III, Tinbergen Institute, revised 23 Oct 2017.

    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. Francisco Blasques & Siem Jan Koopman & André Lucas, 2014. "Maximum Likelihood Estimation for correctly Specified Generalized Autoregressive Score Models: Feedback Effects, Contraction Conditions and Asymptotic Properties," Tinbergen Institute Discussion Papers 14-074/III, Tinbergen Institute.
    3. Tata Subba Rao & Granville Tunnicliffe Wilson & Andrew Harvey & Rutger-Jan Lange, 2017. "Volatility Modeling with a Generalized t Distribution," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(2), pages 175-190, March.
    4. Francisco Blasques & Siem Jan Koopman & André Lucas, 2014. "Information Theoretic Optimality of Observation Driven Time Series Models," Tinbergen Institute Discussion Papers 14-046/III, Tinbergen Institute.
    5. Paul Labonne & Leif Anders Thorsrud, 2023. "Risky news and credit market sentiment," Working Papers No 14/2023, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    6. Blazsek, Szabolcs & Escribano, Álvaro, 2022. "Score-driven threshold ice-age models: benchmark models for long-run climate forecasts," UC3M Working papers. Economics 34757, Universidad Carlos III de Madrid. Departamento de Economía.
    7. Drew Creal & Siem Jan Koopman & André Lucas & Marcin Zamojski, 2015. "Generalized Autoregressive Method of Moments," Tinbergen Institute Discussion Papers 15-138/III, Tinbergen Institute, revised 06 Jul 2018.
    8. Andrew Harvey & Ryoko Ito, 2017. "Modeling time series with zero observations," Economics Papers 2017-W01, Economics Group, Nuffield College, University of Oxford.
    9. Mauro Bernardi & Leopoldo Catania, 2015. "Switching-GAS Copula Models With Application to Systemic Risk," Papers 1504.03733, arXiv.org, revised Jan 2016.
    10. F Blasques & P Gorgi & S Koopman & O Wintenberger, 2016. "Feasible Invertibility Conditions for Maximum Likelihood Estimation for Observation-Driven Models," Papers 1610.02863, arXiv.org.
    11. Leopoldo Catania & Anna Gloria Billé, 2016. "Dynamic Spatial Autoregressive Models with Autoregressive and Heteroskedastic Disturbances," CEIS Research Paper 375, Tor Vergata University, CEIS, revised 31 Mar 2016.
    12. Lucas, André & Schwaab, Bernd & Zhang, Xin, 2015. "Modeling financial sector joint tail risk in the euro area," Working Paper Series 308, Sveriges Riksbank (Central Bank of Sweden).
    13. Vladim'ir Hol'y, 2022. "An Intraday GARCH Model for Discrete Price Changes and Irregularly Spaced Observations," Papers 2211.12376, arXiv.org, revised Sep 2023.
    14. Mariia Artemova & Francisco Blasques & Siem Jan Koopman, 2023. "A Multilevel Factor Model for Economic Activity with Observation Driven Dynamic Factors," Tinbergen Institute Discussion Papers 23-021/III, Tinbergen Institute.
    15. Francisco Blasques & Siem Jan Koopman & Andre Lucas & Julia Schaumburg, 2014. "Spillover Dynamics for Systemic Risk Measurement using Spatial Financial Time Series Models," Tinbergen Institute Discussion Papers 14-107/III, Tinbergen Institute.
    16. Ryoko Ito, 2016. "Asymptotic Theory for Beta-t-GARCH," Cambridge Working Papers in Economics 1607, Faculty of Economics, University of Cambridge.
    17. Sergio Contreras-Espinoza & Francisco Novoa-Muñoz & Szabolcs Blazsek & Pedro Vidal & Christian Caamaño-Carrillo, 2022. "COVID-19 Active Case Forecasts in Latin American Countries Using Score-Driven Models," Mathematics, MDPI, vol. 11(1), pages 1-17, December.
    18. Rutger-Jan Lange & Bram van Os & Dick van Dijk, 2022. "Robust Observation-Driven Models Using Proximal-Parameter Updates Abstract We propose an observation-driven modelling framework that permits time variation in the model’s parameters using a proximal-p," Tinbergen Institute Discussion Papers 22-066/III, Tinbergen Institute, revised 20 Dec 2022.
    19. Ayala Astrid & Blazsek Szabolcs & Escribano Alvaro, 2023. "Anticipating extreme losses using score-driven shape filters," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 27(4), pages 449-484, September.
    20. Jean-Claude Hessing & Rutger-Jan Lange & Daniel Ralph, 2022. "This article establishes the Poisson optional stopping times (POST) method by Lange et al. (2020) as a near-universal method for solving liquidity-constrained American options, or, equivalently, penal," Tinbergen Institute Discussion Papers 22-007/IV, Tinbergen Institute.
    21. Blasques, Francisco & Ji, Jiangyu & Lucas, André, 2016. "Semiparametric score driven volatility models," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 58-69.
    22. Andrew Harvey & Rutger-Jan Lange, 2015. "Modeling the Interactions between Volatility and Returns," Cambridge Working Papers in Economics 1518, Faculty of Economics, University of Cambridge.
    23. Eric A. Beutner & Yicong Lin & Andre Lucas, 2023. "Consistency, distributional convergence, and optimality of score-driven filters," Tinbergen Institute Discussion Papers 23-051/III, Tinbergen Institute.
    24. Bram van Os, 2023. "Information-Theoretic Time-Varying Density Modeling," Tinbergen Institute Discussion Papers 23-037/III, Tinbergen Institute.
    25. Francisco Blasques & Siem Jan Koopman & Max Mallee, 2014. "Low Frequency and Weighted Likelihood Solutions for Mixed Frequency Dynamic Factor Models," Tinbergen Institute Discussion Papers 14-105/III, Tinbergen Institute.
    26. Francisco Blasques & Paolo Gorgi & Siem Jan Koopman & Olivier Wintenberger, 2016. "Feasible Invertibility Conditions and Maximum Likelihood Estimation for Observation-Driven Models," Tinbergen Institute Discussion Papers 16-082/III, Tinbergen Institute.
    27. Trucíos, Carlos, 2019. "Forecasting Bitcoin risk measures: A robust approach," International Journal of Forecasting, Elsevier, vol. 35(3), pages 836-847.
    28. David Ardia & Kris Boudt & Leopoldo Catania, 2016. "Generalized Autoregressive Score Models in R: The GAS Package," Papers 1609.02354, arXiv.org.
    29. Marco Bazzi & Francisco Blasques & Siem Jan Koopman & Andre Lucas, 2017. "Time-Varying Transition Probabilities for Markov Regime Switching Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(3), pages 458-478, May.
    30. Heil, Thomas L.A. & Peter, Franziska J. & Prange, Philipp, 2022. "Measuring 25 years of global equity market co-movement using a time-varying spatial model," Journal of International Money and Finance, Elsevier, vol. 128(C).
    31. Francisco Blasques & Siem Jan Koopman & André Lucas, 2014. "Optimal Formulations for Nonlinear Autoregressive Processes," Tinbergen Institute Discussion Papers 14-103/III, Tinbergen Institute.
    32. Andre Lucas & Bernd Schwaab & Xin Zhang, 2013. "Measuring Credit Risk in a Large Banking System: Econometric Modeling and Empirics," Tinbergen Institute Discussion Papers 13-063/IV/DSF56, Tinbergen Institute, revised 13 Oct 2014.
    33. Pawel Janus & André Lucas & Anne Opschoor & Dick J.C. van Dijk, 2014. "New HEAVY Models for Fat-Tailed Returns and Realized Covariance Kernels," Tinbergen Institute Discussion Papers 14-073/IV, Tinbergen Institute, revised 19 Aug 2015.
    34. Blasques, F. & Koopman, S.J. & Mallee, M. & Zhang, Z., 2016. "Weighted maximum likelihood for dynamic factor analysis and forecasting with mixed frequency data," Journal of Econometrics, Elsevier, vol. 193(2), pages 405-417.

  20. Francisco Blasques & Siem Jan Koopman & André Lucas, 2014. "Optimal Formulations for Nonlinear Autoregressive Processes," Tinbergen Institute Discussion Papers 14-103/III, Tinbergen Institute.

    Cited by:

    1. Delle Monache, Davide & Petrella, Ivan, 2017. "Adaptive models and heavy tails with an application to inflation forecasting," International Journal of Forecasting, Elsevier, vol. 33(2), pages 482-501.
    2. Roman Frydman & Soeren Johansen & Anders Rahbek & Morten Nyboe, 2017. "The Qualitative Expectations Hypothesis: Model Ambiguity, Consistent Representations Of Market Forecasts, And Sentiment," Discussion Papers 17-10, University of Copenhagen. Department of Economics.
    3. Petrella, Ivan & Delle Monache, Davide & Venditti, Fabrizio, 2019. "Price Dividend Ratio and Long-Run Stock Returns: a Score Driven State Space Model," CEPR Discussion Papers 14107, C.E.P.R. Discussion Papers.
    4. Mauro Bernardi & Leopoldo Catania, 2015. "Switching-GAS Copula Models With Application to Systemic Risk," Papers 1504.03733, arXiv.org, revised Jan 2016.
    5. F Blasques & P Gorgi & S Koopman & O Wintenberger, 2016. "Feasible Invertibility Conditions for Maximum Likelihood Estimation for Observation-Driven Models," Papers 1610.02863, arXiv.org.
    6. Mohamed CHIKHI & Claude DIEBOLT & Tapas MISHRA, 2019. "Does Predictive Ability of an Asset Price Rest in 'Memory'? Insights from a New Approach," Working Papers of BETA 2019-43, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
    7. Davide Delle Monache & Ivan Petrella, 2014. "Adaptive Models and Heavy Tails," Working Papers 720, Queen Mary University of London, School of Economics and Finance.
    8. Francisco Blasques & Paolo Gorgi & Siem Jan Koopman & Olivier Wintenberger, 2016. "Feasible Invertibility Conditions and Maximum Likelihood Estimation for Observation-Driven Models," Tinbergen Institute Discussion Papers 16-082/III, Tinbergen Institute.
    9. Ioanna-Yvonni Tsaknaki & Fabrizio Lillo & Piero Mazzarisi, 2023. "Online Learning of Order Flow and Market Impact with Bayesian Change-Point Detection Methods," Papers 2307.02375, arXiv.org.
    10. Martin Magris, 2019. "A Vine-copula extension for the HAR model," Papers 1907.08522, arXiv.org.

  21. Marco Bazzi & Francisco Blasques & Siem Jan Koopman & Andre Lucas, 2014. "Time Varying Transition Probabilities for Markov Regime Switching Models," Tinbergen Institute Discussion Papers 14-072/III, Tinbergen Institute.

    Cited by:

    1. Chotipong Charoensom, 2024. "An Estimation of Regime Switching Models with Nonlinear Endogenous Switching," PIER Discussion Papers 217, Puey Ungphakorn Institute for Economic Research.
    2. Chang, Yoosoon & Choi, Yongok & Park, Joon Y., 2017. "A new approach to model regime switching," Journal of Econometrics, Elsevier, vol. 196(1), pages 127-143.
    3. Fédéric Holm-Hadulla & Kirstin Hubrich, 2017. "Macroeconomic Implications of Oil Price Fluctuations : A Regime-Switching Framework for the Euro Area," Finance and Economics Discussion Series 2017-063, Board of Governors of the Federal Reserve System (U.S.).
    4. Qingfu Liu & Yiuman Tse & Kaixin Zheng, 2021. "The impact of trading behavioral biases on market liquidity under different volatility levels: Evidence from the Chinese commodity futures market," The Financial Review, Eastern Finance Association, vol. 56(4), pages 671-692, November.
    5. Marie Bessec, 2019. "Revisiting the transitional dynamics of business-cycle phases with mixed-frequency data," Post-Print hal-02181552, HAL.
    6. Chang, Yoosoon & Maih, Junior & Tan, Fei, 2021. "Origins of monetary policy shifts: A New approach to regime switching in DSGE models," Journal of Economic Dynamics and Control, Elsevier, vol. 133(C).
    7. Andrei A. Sirchenko, 2017. "An endogenous regime-switching model of ordered choice with an application to federal funds rate target," 2017 Papers psi424, Job Market Papers.
    8. Mauro Bernardi & Leopoldo Catania, 2015. "Switching-GAS Copula Models With Application to Systemic Risk," Papers 1504.03733, arXiv.org, revised Jan 2016.
    9. Grassi, Stefano & Ravazzolo, Francesco & Vespignani, Joaquin & Vocalelli, Giorgio, 2023. "Global money supply and energy and non-energy commodity prices: A MS-TV-VAR approach," Working Papers 2023-01, University of Tasmania, Tasmanian School of Business and Economics.
    10. Paul Doukhan & Konstantinos Fokianos & Joseph Rynkiewicz, 2021. "Mixtures of Nonlinear Poisson Autoregressions," Journal of Time Series Analysis, Wiley Blackwell, vol. 42(1), pages 107-135, January.
    11. Stefan Fiesel & Marliese Uhrig-Homburg, 2016. "Illiquidity Transmission in a Three-Country Framework: A Conditional Approach," Schmalenbach Business Review, Springer;Schmalenbach-Gesellschaft, vol. 17(3), pages 261-284, December.
    12. Jonathan Olusegun Famoroti & Omolade Adeleke, 2023. "Analysis of Wamz’s Economic Growth and Monetary Policy Using the Markov Switching Approach," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 7(4), pages 142-156, April.
    13. Yoosoon Chang & Junior Maih & Fei Tan, 2018. "State Space Models with Endogenous Regime Switching," Working Papers No 9/2018, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    14. Alexander Georges Gretener & Matthias Neuenkirch & Dennis Umlandt, 2022. "Dynamic Mixture Vector Autoregressions with Score-Driven Weights," Research Papers in Economics 2022-02, University of Trier, Department of Economics.
    15. Goodness C. Aye & Tsangyao Chang & Rangan Gupta, 2015. "Is Gold an Inflation-Hedge? Evidence from an Interrupted Markov-Switching Cointegration Model," Working Papers 201559, University of Pretoria, Department of Economics.
    16. Tharcisio Leone, 2019. "Intergenerational Mobility in Education: Estimates of the Worldwide Variation," Journal of Economic Development, Chung-Ang Unviersity, Department of Economics, vol. 44(4), pages 1-42, December.
    17. Kirstin Hubrich & Daniel F. Waggoner, 2022. "The Transmission of Financial Shocks and Leverage of Financial Institutions: An Endogenous Regime-Switching Framework," FRB Atlanta Working Paper 2022-5, Federal Reserve Bank of Atlanta.
    18. Spezia, Luigi, 2020. "Bayesian variable selection in non-homogeneous hidden Markov models through an evolutionary Monte Carlo method," Computational Statistics & Data Analysis, Elsevier, vol. 143(C).
    19. Mohammad Enamul Hoque & Mohd Azlan Shah Zaidi & M. Kabir Hassan, 2021. "Geopolitical Uncertainties and Malaysian Stock Market Returns: Do Market Conditions Matter?," Mathematics, MDPI, vol. 9(19), pages 1-16, September.
    20. Marie Bessec, 2015. "Revisiting the transitional dynamics of business-cycle phases with mixed frequency data," Post-Print hal-01276824, HAL.
    21. Leone, Tharcisio, 2021. "The gender gap in intergenerational mobility," World Development Perspectives, Elsevier, vol. 21(C).
    22. Leopoldo Catania, 2016. "Dynamic Adaptive Mixture Models," Papers 1603.01308, arXiv.org, revised Jan 2023.
    23. Yoosoon Chang & Fei Tan & Xin Wei, 2018. "State Space Models with Endogenous Regime Switching," CAEPR Working Papers 2018-012, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    24. Huaping Chen & Qi Li & Fukang Zhu, 2022. "A new class of integer-valued GARCH models for time series of bounded counts with extra-binomial variation," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 106(2), pages 243-270, June.
    25. Leone, Tharcisio, 2017. "The gender gap in intergenerational mobility: Evidence of educational persistence in Brazil," Discussion Papers 2017/27, Free University Berlin, School of Business & Economics.
    26. Harvey, A. & Palumbo, D., 2021. "Regime switching models for directional and linear observations," Cambridge Working Papers in Economics 2123, Faculty of Economics, University of Cambridge.
    27. Tan, Chia-Yen & Koh, You-Beng & Ng, Kok-Haur & Ng, Kooi-Huat, 2021. "Dynamic volatility modelling of Bitcoin using time-varying transition probability Markov-switching GARCH model," The North American Journal of Economics and Finance, Elsevier, vol. 56(C).
    28. Pan, Zhiyuan & Xiao, Dongli & Dong, Qingma & Liu, Li, 2022. "Structural breaks, macroeconomic fundamentals and cross hedge ratio," Finance Research Letters, Elsevier, vol. 47(PA).
    29. Wang, Lu & Ma, Feng & Hao, Jianyang & Gao, Xinxin, 2021. "Forecasting crude oil volatility with geopolitical risk: Do time-varying switching probabilities play a role?," International Review of Financial Analysis, Elsevier, vol. 76(C).

  22. Francisco Blasques & Andre Lucas & Erkki Silde, 2013. "Stationarity and Ergodicity Regions for Score Driven Dynamic Correlation Models," Tinbergen Institute Discussion Papers 13-097/IV/DSF59, Tinbergen Institute.

    Cited by:

    1. Mauro Bernardi & Leopoldo Catania, 2015. "Switching-GAS Copula Models With Application to Systemic Risk," Papers 1504.03733, arXiv.org, revised Jan 2016.

  23. Francisco Blasques & Siem Jan Koopman & Andre Lucas, 2012. "Stationarity and Ergodicity of Univariate Generalized Autoregressive Score Processes," Tinbergen Institute Discussion Papers 12-059/4, Tinbergen Institute.

    Cited by:

    1. Francisco Blasques & Siem Jan Koopman & André Lucas, 2014. "Maximum Likelihood Estimation for correctly Specified Generalized Autoregressive Score Models: Feedback Effects, Contraction Conditions and Asymptotic Properties," Tinbergen Institute Discussion Papers 14-074/III, Tinbergen Institute.
    2. Francisco Blasques & Siem Jan Koopman & André Lucas, 2014. "Information Theoretic Optimality of Observation Driven Time Series Models," Tinbergen Institute Discussion Papers 14-046/III, Tinbergen Institute.
    3. Irving Arturo De Lira Salvatierra & Andrew J. Patton, 2013. "Dynamic Copula Models and High Frequency Data," Working Papers 13-28, Duke University, Department of Economics.
    4. Mauro Bernardi & Leopoldo Catania, 2015. "Switching-GAS Copula Models With Application to Systemic Risk," Papers 1504.03733, arXiv.org, revised Jan 2016.
    5. F Blasques & P Gorgi & S Koopman & O Wintenberger, 2016. "Feasible Invertibility Conditions for Maximum Likelihood Estimation for Observation-Driven Models," Papers 1610.02863, arXiv.org.
    6. Lucas, André & Schwaab, Bernd & Zhang, Xin, 2015. "Modeling financial sector joint tail risk in the euro area," Working Paper Series 308, Sveriges Riksbank (Central Bank of Sweden).
    7. Jiangyu Ji & Andre Lucas, 2012. "A New Semiparametric Volatility Model," Tinbergen Institute Discussion Papers 12-055/2/DSF35, Tinbergen Institute.
    8. Blasques, Francisco & Ji, Jiangyu & Lucas, André, 2016. "Semiparametric score driven volatility models," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 58-69.
    9. Francisco Blasques & Paolo Gorgi & Siem Jan Koopman & Olivier Wintenberger, 2016. "Feasible Invertibility Conditions and Maximum Likelihood Estimation for Observation-Driven Models," Tinbergen Institute Discussion Papers 16-082/III, Tinbergen Institute.
    10. David Ardia & Kris Boudt & Leopoldo Catania, 2016. "Generalized Autoregressive Score Models in R: The GAS Package," Papers 1609.02354, arXiv.org.
    11. Francisco Blasques & Andre Lucas & Erkki Silde, 2013. "Stationarity and Ergodicity Regions for Score Driven Dynamic Correlation Models," Tinbergen Institute Discussion Papers 13-097/IV/DSF59, Tinbergen Institute.
    12. Marco Bazzi & Francisco Blasques & Siem Jan Koopman & Andre Lucas, 2017. "Time-Varying Transition Probabilities for Markov Regime Switching Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(3), pages 458-478, May.
    13. Andre Lucas & Bernd Schwaab & Xin Zhang, 2013. "Measuring Credit Risk in a Large Banking System: Econometric Modeling and Empirics," Tinbergen Institute Discussion Papers 13-063/IV/DSF56, Tinbergen Institute, revised 13 Oct 2014.

  24. Francisco Blasques, 2012. "Transformed Polynomials for Nonlinear Autoregressive Models of the Conditional Mean," Tinbergen Institute Discussion Papers 12-133/III, Tinbergen Institute.

    Cited by:

    1. Francisco (F.) Blasques & Marc Nientker, 2019. "Transformed Perturbation Solutions for Dynamic Stochastic General Equilibrium Models," Tinbergen Institute Discussion Papers 19-012/III, Tinbergen Institute, revised 09 Feb 2020.

Articles

  1. Blasques, F. & Gorgi, P. & Koopman, S.J., 2021. "Missing observations in observation-driven time series models," Journal of Econometrics, Elsevier, vol. 221(2), pages 542-568.
    See citations under working paper version above.
  2. Francisco Blasques & Siem Jan Koopman & André Lucas, 2020. "Nonlinear autoregressive models with optimality properties," Econometric Reviews, Taylor & Francis Journals, vol. 39(6), pages 559-578, July.

    Cited by:

    1. Giacomo Bormetti & Fulvio Corsi, 2021. "A Lucas Critique Compliant SVAR model with Observation-driven Time-varying Parameters," Papers 2107.05263, arXiv.org, revised Feb 2022.
    2. Martí de Castro-Cros & Manel Velasco & Cecilio Angulo, 2021. "Machine-Learning-Based Condition Assessment of Gas Turbines—A Review," Energies, MDPI, vol. 14(24), pages 1-27, December.

  3. Blasques, F. & Gorgi, P. & Koopman, S.J., 2019. "Accelerating score-driven time series models," Journal of Econometrics, Elsevier, vol. 212(2), pages 359-376.

    Cited by:

    1. Deniz Erer, 2023. "The Impact of News Related Covid-19 on Exchange Rate Volatility:A New Evidence From Generalized Autoregressive Score Model," EKOIST Journal of Econometrics and Statistics, Istanbul University, Faculty of Economics, vol. 0(38), pages 105-126, June.
    2. Diana Escandon-Barbosa & Agustin Ramirez & Jairo Salas-Paramo, 2022. "The Effect of Cultural Orientations on Country Innovation Performance: Hofstede Cultural Dimensions Revisited?," Sustainability, MDPI, vol. 14(10), pages 1-13, May.
    3. Jiang, Kunliang & Zeng, Linhui & Song, Jiashan & Liu, Yimeng, 2022. "Forecasting Value-at-Risk of cryptocurrencies using the time-varying mixture-accelerating generalized autoregressive score model," Research in International Business and Finance, Elsevier, vol. 61(C).
    4. Bram van Os & Dick van Dijk, 2020. "Accelerating Peak Dating in a Dynamic Factor Markov-Switching Model," Tinbergen Institute Discussion Papers 20-057/VI, Tinbergen Institute, revised 14 Dec 2020.
    5. Giovanni Angelini & Giuseppe Cavaliere & Enzo D'Innocenzo & Luca De Angelis, 2022. "Time-Varying Poisson Autoregression," Papers 2207.11003, arXiv.org.

  4. Blasques, Francisco & Bräuning, Falk & Lelyveld, Iman van, 2018. "A dynamic network model of the unsecured interbank lending market," Journal of Economic Dynamics and Control, Elsevier, vol. 90(C), pages 310-342.
    See citations under working paper version above.
  5. Francisco Blasques & André Lucas & Erkki Silde, 2018. "A stochastic recurrence equations approach for score driven correlation models," Econometric Reviews, Taylor & Francis Journals, vol. 37(2), pages 166-181, February.

    Cited by:

    1. Blasques, Francisco & van Brummelen, Janneke & Koopman, Siem Jan & Lucas, André, 2022. "Maximum likelihood estimation for score-driven models," Journal of Econometrics, Elsevier, vol. 227(2), pages 325-346.
    2. Buccheri, Giuseppe & Corsi, Fulvio & Flandoli, Franco & Livieri, Giulia, 2021. "The continuous-time limit of score-driven volatility models," Journal of Econometrics, Elsevier, vol. 221(2), pages 655-675.
    3. Jean-Claude Hessing & Rutger-Jan Lange & Daniel Ralph, 2022. "This article establishes the Poisson optional stopping times (POST) method by Lange et al. (2020) as a near-universal method for solving liquidity-constrained American options, or, equivalently, penal," Tinbergen Institute Discussion Papers 22-007/IV, Tinbergen Institute.
    4. Eric A. Beutner & Yicong Lin & Andre Lucas, 2023. "Consistency, distributional convergence, and optimality of score-driven filters," Tinbergen Institute Discussion Papers 23-051/III, Tinbergen Institute.
    5. Huaping Chen & Qi Li & Fukang Zhu, 2022. "A new class of integer-valued GARCH models for time series of bounded counts with extra-binomial variation," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 106(2), pages 243-270, June.

  6. Blasques, Francisco & Duplinskiy, Artem, 2018. "Penalized indirect inference," Journal of Econometrics, Elsevier, vol. 205(1), pages 34-54.
    See citations under working paper version above.
  7. Marco Bazzi & Francisco Blasques & Siem Jan Koopman & Andre Lucas, 2017. "Time-Varying Transition Probabilities for Markov Regime Switching Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(3), pages 458-478, May.
    See citations under working paper version above.
  8. Blasques, Francisco & Ji, Jiangyu & Lucas, André, 2016. "Semiparametric score driven volatility models," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 58-69.

    Cited by:

    1. Lucas, André & Zhang, Xin, 2015. "Score Driven Exponentially Weighted Moving Averages and Value-at-Risk Forecasting," Working Paper Series 309, Sveriges Riksbank (Central Bank of Sweden).
    2. Gorgi, Paolo & Koopman, Siem Jan & Li, Mengheng, 2019. "Forecasting economic time series using score-driven dynamic models with mixed-data sampling," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1735-1747.

  9. Blasques, Francisco & Koopman, Siem Jan & Łasak, Katarzyna & Lucas, André, 2016. "In-sample confidence bands and out-of-sample forecast bands for time-varying parameters in observation-driven models," International Journal of Forecasting, Elsevier, vol. 32(3), pages 875-887.
    See citations under working paper version above.
  10. Blasques, F. & Koopman, S.J. & Mallee, M. & Zhang, Z., 2016. "Weighted maximum likelihood for dynamic factor analysis and forecasting with mixed frequency data," Journal of Econometrics, Elsevier, vol. 193(2), pages 405-417.

    Cited by:

    1. Markus Leippold & Hanlin Yang, 2023. "Mixed‐frequency predictive regressions with parameter learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(8), pages 1955-1972, December.
    2. Foroni, Claudia & Guérin, Pierre & Marcellino, Massimiliano, 2018. "Using low frequency information for predicting high frequency variables," International Journal of Forecasting, Elsevier, vol. 34(4), pages 774-787.
    3. Foroni, Claudia & Ravazzolo, Francesco & Rossini, Luca, 2023. "Are low frequency macroeconomic variables important for high frequency electricity prices?," Economic Modelling, Elsevier, vol. 120(C).
    4. Xu Gong & Boqiang Lin, 2021. "Effects of structural changes on the prediction of downside volatility in futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(7), pages 1124-1153, July.
    5. Michael W. McCracken & Michael T. Owyang & Tatevik Sekhposyan, 2021. "Real-Time Forecasting and Scenario Analysis Using a Large Mixed-Frequency Bayesian VAR," International Journal of Central Banking, International Journal of Central Banking, vol. 17(71), pages 1-41, December.
    6. Giovanni Ballarin & Petros Dellaportas & Lyudmila Grigoryeva & Marcel Hirt & Sophie van Huellen & Juan-Pablo Ortega, 2022. "Reservoir Computing for Macroeconomic Forecasting with Mixed Frequency Data," Papers 2211.00363, arXiv.org, revised Jan 2024.
    7. Foroni, Claudia & Ravazzolo, Francesco & Rossini, Luca, 2019. "Forecasting daily electricity prices with monthly macroeconomic variables," Working Paper Series 2250, European Central Bank.
    8. Dong Hwan Oh & Andrew J. Patton, 2021. "Better the Devil You Know: Improved Forecasts from Imperfect Models," Finance and Economics Discussion Series 2021-071, Board of Governors of the Federal Reserve System (U.S.).
    9. Malin Song & Qianjiao Xie, 2021. "Evaluation of Urban Competitiveness of the Huaihe River Eco-Economic Belt Based on Dynamic Factor Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 58(3), pages 615-639, October.
    10. Nikolaos Zirogiannis & Yorghos Tripodis, 2018. "Dynamic factor analysis for short panels: estimating performance trajectories for water utilities," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 27(1), pages 131-150, March.
    11. Gorgi, Paolo & Koopman, Siem Jan & Li, Mengheng, 2019. "Forecasting economic time series using score-driven dynamic models with mixed-data sampling," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1735-1747.
    12. Thomas B. Götz & Alain W. Hecq, 2019. "Granger Causality Testing in Mixed‐Frequency VARs with Possibly (Co)Integrated Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 40(6), pages 914-935, November.
    13. Camacho, Maximo & Perez-Quiros, Gabriel & Pacce, Matías, 2020. "Spillover effects in international business cycles," Working Paper Series 2484, European Central Bank.
    14. Li, Mengheng & Koopman, Siem Jan & Lit, Rutger & Petrova, Desislava, 2020. "Long-term forecasting of El Niño events via dynamic factor simulations," Journal of Econometrics, Elsevier, vol. 214(1), pages 46-66.
    15. Paul Labonne, 2020. "Capturing GDP nowcast uncertainty in real time," Papers 2012.02601, arXiv.org, revised Oct 2021.
    16. Nikolaos Zirogiannis & Kerry Krutilla & Yorghos Tripodis & Kathryn Fledderman, 2019. "Human Development Over Time: An Empirical Comparison of a Dynamic Index and the Standard HDI," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 142(2), pages 773-798, April.

  11. Blasques, Francisco & Koopman, Siem Jan & Lucas, Andre & Schaumburg, Julia, 2016. "Spillover dynamics for systemic risk measurement using spatial financial time series models," Journal of Econometrics, Elsevier, vol. 195(2), pages 211-223.
    See citations under working paper version above.
  12. F. Blasques & S. J. Koopman & A. Lucas, 2015. "Information-theoretic optimality of observation-driven time series models for continuous responses," Biometrika, Biometrika Trust, vol. 102(2), pages 325-343.

    Cited by:

    1. Enzo D'Innocenzo & André Lucas & Anne Opschoor & Xingmin Zhang, 2024. "Heterogeneity and dynamics in network models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(1), pages 150-173, January.
    2. Mariana Arozo B. de Melo & Cristiano A. C. Fernandes & Eduardo F. L. de Melo, 2018. "Forecasting aggregate claims using score‐driven time series models," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 72(3), pages 354-374, August.
    3. Nguyen, Hoang & Ausín Olivera, María Concepción & Galeano San Miguel, Pedro, 2017. "Parallel Bayesian Inference for High Dimensional Dynamic Factor Copulas," DES - Working Papers. Statistics and Econometrics. WS 24552, Universidad Carlos III de Madrid. Departamento de Estadística.
    4. Anne Opschoor & André Lucas, 2019. "Time-varying tail behavior for realized kernels," Tinbergen Institute Discussion Papers 19-051/IV, Tinbergen Institute.
    5. Aknouche, Abdelhakim & Francq, Christian, 2023. "Two-stage weighted least squares estimator of the conditional mean of observation-driven time series models," Journal of Econometrics, Elsevier, vol. 237(2).
    6. Roman Frydman & Soeren Johansen & Anders Rahbek & Morten Nyboe, 2017. "The Qualitative Expectations Hypothesis: Model Ambiguity, Consistent Representations Of Market Forecasts, And Sentiment," Discussion Papers 17-10, University of Copenhagen. Department of Economics.
    7. Song, Shijia & Li, Handong, 2022. "Predicting VaR for China's stock market: A score-driven model based on normal inverse Gaussian distribution," International Review of Financial Analysis, Elsevier, vol. 82(C).
    8. Blazsek, Szabolcs & Escribano, Álvaro, 2022. "Score-driven threshold ice-age models: benchmark models for long-run climate forecasts," UC3M Working papers. Economics 34757, Universidad Carlos III de Madrid. Departamento de Economía.
    9. Drew Creal & Siem Jan Koopman & André Lucas & Marcin Zamojski, 2015. "Generalized Autoregressive Method of Moments," Tinbergen Institute Discussion Papers 15-138/III, Tinbergen Institute, revised 06 Jul 2018.
    10. Caballero, Diego & Lucas, André & Schwaab, Bernd & Zhang, Xin, 2019. "Risk endogeneity at the lender/investor-of-last-resort," Working Paper Series 2225, European Central Bank.
    11. Nguyen, Hoang & Javed, Farrukh, 2021. "Dynamic relationship between Stock and Bond returns: A GAS MIDAS copula approach," Working Papers 2021:15, Örebro University, School of Business.
    12. Michel Ferreira Cardia Haddad & Szabolcs Blazsek & Philip Arestis & Franz Fuerst & Hsia Hua Sheng, 2023. "The two-component Beta-t-QVAR-M-lev: a new forecasting model," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 37(4), pages 379-401, December.
    13. Lucas, André & Zhang, Xin, 2015. "Score Driven Exponentially Weighted Moving Averages and Value-at-Risk Forecasting," Working Paper Series 309, Sveriges Riksbank (Central Bank of Sweden).
    14. Francisco (F.) Blasques & Andre (A.) Lucas & Andries van Vlodrop, 2017. "Finite Sample Optimality of Score-Driven Volatility Models," Tinbergen Institute Discussion Papers 17-111/III, Tinbergen Institute.
    15. Song, Shijia & Tian, Fei & Li, Handong, 2021. "An intraday-return-based Value-at-Risk model driven by dynamic conditional score with censored generalized Pareto distribution," Journal of Asian Economics, Elsevier, vol. 74(C).
    16. Domenico Di Gangi & Giacomo Bormetti & Fabrizio Lillo, 2022. "Score Driven Generalized Fitness Model for Sparse and Weighted Temporal Networks," Papers 2202.09854, arXiv.org, revised Mar 2022.
    17. Neves, César & Fernandes, Cristiano & Hoeltgebaum, Henrique, 2017. "Five different distributions for the Lee–Carter model of mortality forecasting: A comparison using GAS models," Insurance: Mathematics and Economics, Elsevier, vol. 75(C), pages 48-57.
    18. Mauro Bernardi & Leopoldo Catania, 2015. "Switching-GAS Copula Models With Application to Systemic Risk," Papers 1504.03733, arXiv.org, revised Jan 2016.
    19. Francisco Blasques & Christian Francq & Sébastien Laurent, 2020. "A New Class of Robust Observation-Driven Models," Tinbergen Institute Discussion Papers 20-073/III, Tinbergen Institute.
    20. Blasques, Francisco & van Brummelen, Janneke & Koopman, Siem Jan & Lucas, André, 2022. "Maximum likelihood estimation for score-driven models," Journal of Econometrics, Elsevier, vol. 227(2), pages 325-346.
    21. Buccheri, Giuseppe & Corsi, Fulvio & Flandoli, Franco & Livieri, Giulia, 2021. "The continuous-time limit of score-driven volatility models," Journal of Econometrics, Elsevier, vol. 221(2), pages 655-675.
    22. Leopoldo Catania & Anna Gloria Billé, 2016. "Dynamic Spatial Autoregressive Models with Autoregressive and Heteroskedastic Disturbances," CEIS Research Paper 375, Tor Vergata University, CEIS, revised 31 Mar 2016.
    23. Giacomo Bormetti & Fulvio Corsi, 2021. "A Lucas Critique Compliant SVAR model with Observation-driven Time-varying Parameters," Papers 2107.05263, arXiv.org, revised Feb 2022.
    24. Lucas, André & Opschoor, Anne & Schaumburg, Julia, 2016. "Accounting for missing values in score-driven time-varying parameter models," Economics Letters, Elsevier, vol. 148(C), pages 96-98.
    25. Xu, Yingying & Lien, Donald, 2022. "COVID-19 and currency dependences: Empirical evidence from BRICS," Finance Research Letters, Elsevier, vol. 45(C).
    26. Lucas, André & Schwaab, Bernd & Zhang, Xin, 2015. "Modeling financial sector joint tail risk in the euro area," Working Paper Series 308, Sveriges Riksbank (Central Bank of Sweden).
    27. Anne Opschoor & André Lucas & István Barra & Dick van Dijk, 2021. "Closed-Form Multi-Factor Copula Models With Observation-Driven Dynamic Factor Loadings," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(4), pages 1066-1079, October.
    28. Siem Jan Koopman & Rutger Lit & André Lucas, 2015. "Intraday Stock Price Dependence using Dynamic Discrete Copula Distributions," Tinbergen Institute Discussion Papers 15-037/III/DSF90, Tinbergen Institute.
    29. P. Gorgi & Siem Jan (S.J.) Koopman & R. Lit, 2018. "The analysis and forecasting of ATP tennis matches using a high-dimensional dynamic model," Tinbergen Institute Discussion Papers 18-009/III, Tinbergen Institute.
    30. Alexander Georges Gretener & Matthias Neuenkirch & Dennis Umlandt, 2022. "Dynamic Mixture Vector Autoregressions with Score-Driven Weights," Research Papers in Economics 2022-02, University of Trier, Department of Economics.
    31. Telg, Sean & Dubinova, Anna & Lucas, Andre, 2023. "Covid-19, credit risk management modeling, and government support," Journal of Banking & Finance, Elsevier, vol. 147(C).
    32. Vladim'ir Hol'y, 2022. "An Intraday GARCH Model for Discrete Price Changes and Irregularly Spaced Observations," Papers 2211.12376, arXiv.org, revised Sep 2023.
    33. Anne Opschoor & André Lucas, 2019. "Observation-driven Models for Realized Variances and Overnight Returns," Tinbergen Institute Discussion Papers 19-052/IV, Tinbergen Institute.
    34. Shijia Song & Handong Li, 2023. "A new model for forecasting VaR and ES using intraday returns aggregation," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(5), pages 1039-1054, August.
    35. Francisco Blasques & Siem Jan Koopman & Andre Lucas & Julia Schaumburg, 2014. "Spillover Dynamics for Systemic Risk Measurement using Spatial Financial Time Series Models," Tinbergen Institute Discussion Papers 14-107/III, Tinbergen Institute.
    36. Blasques, Francisco & Lucas, André & van Vlodrop, Andries C., 2021. "Finite Sample Optimality of Score-Driven Volatility Models: Some Monte Carlo Evidence," Econometrics and Statistics, Elsevier, vol. 19(C), pages 47-57.
    37. Francisco (F.) Blasques & Paolo Gorgi & Siem Jan (S.J.) Koopman, 2017. "Accelerating GARCH and Score-Driven Models: Optimality, Estimation and Forecasting," Tinbergen Institute Discussion Papers 17-059/III, Tinbergen Institute.
    38. Blazsek, Szabolcs Istvan & Escribano, Álvaro & Kristof, Erzsebet, 2024. "Global, Arctic, and Antarctic sea ice volume predictions: using score-driven threshold climate models," UC3M Working papers. Economics 39546, Universidad Carlos III de Madrid. Departamento de Economía.
    39. Rutger-Jan Lange & Bram van Os & Dick van Dijk, 2022. "Robust Observation-Driven Models Using Proximal-Parameter Updates Abstract We propose an observation-driven modelling framework that permits time variation in the model’s parameters using a proximal-p," Tinbergen Institute Discussion Papers 22-066/III, Tinbergen Institute, revised 20 Dec 2022.
    40. Yu‐Sheng Lai, 2021. "Generalized autoregressive score model with high‐frequency data for optimal futures hedging," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(12), pages 2023-2045, December.
    41. Ayala Astrid & Blazsek Szabolcs & Escribano Alvaro, 2023. "Anticipating extreme losses using score-driven shape filters," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 27(4), pages 449-484, September.
    42. Blasques, F. & Gorgi, P. & Koopman, S.J., 2019. "Accelerating score-driven time series models," Journal of Econometrics, Elsevier, vol. 212(2), pages 359-376.
    43. Anna Gloria Billé & Leopoldo Catania, 2018. "Dynamic Spatial Autoregressive Models with Time-varying Spatial Weighting Matrices," BEMPS - Bozen Economics & Management Paper Series BEMPS55, Faculty of Economics and Management at the Free University of Bozen.
    44. Anna Dubinova & Andre Lucas & Sean Telg, 2021. "COVID-19, Credit Risk and Macro Fundamentals," Tinbergen Institute Discussion Papers 21-059/III, Tinbergen Institute.
    45. Opschoor, Anne & Lucas, André, 2023. "Time-varying variance and skewness in realized volatility measures," International Journal of Forecasting, Elsevier, vol. 39(2), pages 827-840.
    46. Gorgi, Paolo & Koopman, Siem Jan & Li, Mengheng, 2019. "Forecasting economic time series using score-driven dynamic models with mixed-data sampling," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1735-1747.
    47. Blasques, Francisco & Ji, Jiangyu & Lucas, André, 2016. "Semiparametric score driven volatility models," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 58-69.
    48. Opschoor, Anne & Lucas, André, 2021. "Observation-driven models for realized variances and overnight returns applied to Value-at-Risk and Expected Shortfall forecasting," International Journal of Forecasting, Elsevier, vol. 37(2), pages 622-633.
    49. Andre Lucas & Anne Opschoor, 2016. "Fractional Integration and Fat Tails for Realized Covariance Kernels and Returns," Tinbergen Institute Discussion Papers 16-069/IV, Tinbergen Institute, revised 07 Jul 2017.
    50. Blazsek, Szabolcs & Escribano, Álvaro & Licht, Adrian, 2019. "Markov-switching score-driven multivariate models: outlier-robust measurement of the relationships between world crude oil production and US industrial production," UC3M Working papers. Economics 29030, Universidad Carlos III de Madrid. Departamento de Economía.
    51. Eric A. Beutner & Yicong Lin & Andre Lucas, 2023. "Consistency, distributional convergence, and optimality of score-driven filters," Tinbergen Institute Discussion Papers 23-051/III, Tinbergen Institute.
    52. Giuseppe Buccheri & Stefano Grassi & Giorgio Vocalelli, 2021. "Estimating Risk in Illiquid Markets: a Model of Market Friction with Stochastic Volatility," CEIS Research Paper 506, Tor Vergata University, CEIS, revised 08 Nov 2021.
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  13. Francisco Blasques, 2014. "Transformed Polynomials For Nonlinear Autoregressive Models Of The Conditional Mean," Journal of Time Series Analysis, Wiley Blackwell, vol. 35(3), pages 218-238, May.
    See citations under working paper version above.
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