Generalized Autoregressive Score models




The following articles, papers, and books are related to the GAS model (alphabetic order per year).

File your own GAS paper via the form. Click here!

Download the main GAS Journal of Applied Econometrics paper (2013, open access) by clicking here!

If you find your contribution is missing, please let us know by sending an email to Andre Lucas (a.lucas@vu.nl) Before using any of the code, please read the disclaimer.



    in press

  1. Gorgi, Paolo., C.S.A. Lauria, Alessandra Luati (2024): "On the optimality of score-driven models", Biometrika, asad067.


  2. 2024

  3. Abdelkarim, Randa A., and Ibrahim A. Onour (2024) "Semiparametric Score-Driven Exponentially Weighted Moving Average Model", Proceedings ICGER 2023: Global Economic Revolutions: Big Data Governance and Business Analytics for Sustainability, Communications in Computer and Information Science book series (CCIS,volume 1999), Springer, 210-221.

  4. Alanya-Beltran, Willy (2024): "Factor-augmented QVAR models: an observation-driven approach" Macroeconomic Dynamics 4, 970 - 989.

  5. Blasques, Francisco, Christian Franq, and Sébastien Laurent (in press): "Autoregressive conditional betas", Journal of Econometrics, 238(2), 105630.

  6. Blasques, Francisco, Janneke van Brummelen, Paolo Gorgi, Siem Jan Koopman (2024): "Maximum Likelihood Estimation for Non-Stationary Location Models with Mixture of Normal Distributions", Journal of Econometrics, 238(1), 105575.

  7. Blasques, Francisco, Janneke van Brummelen, Siem Jan Koopman (2024): "A robust Beveridge-Nelson decomposition using a score-driven approach with an application", Economics Letters, 236(1), 111588.

  8. Blazsek, Szabolcs and Escribano, Álvaro and Kristóf, Erzsébet (2024) "Global, Arctic, and Antarctic Sea Ice Volume Predictions Using Score-Driven Threshold Climate Models", SSRN working paper, 4681562.

  9. Creal, Drew, Siem Jan Koopman, Andre Lucas, Marcin Zamojski (2024): "Observation-driven filtering of time-varying parameters using moment conditions", Journal of Econometrics, 238(2), 105635.

  10. Delle Monache, Davide, Andrea De Polis, and Ivan Petrella (2024): "Modeling and Forecasting Macroeconomic Downside Risk", Journal of Business and Economic Statistics, 42(3), 1010-1025.
    Click here for working paper version.

  11. D'Innocenzo, Enzo, and Andre Lucas (2024): "Dynamic partial correlation models", Journal of Econometrics, 241(2), 105747.

  12. D'Innocenzo, Enzo, Andre Lucas, Anne Opschoor, and Xingmin Zhang (2024): "Heterogeneity and dynamics in network models", Journal of Applied Econometrics, 39(1), 150-173.

  13. D'Innocenzo, Enzo, Andre Lucas, Bernd Schwaab, Xin Zhang (2024): "Modeling extreme events: time-varying extreme tail shape", Journal of Business and Economic Statistics 42(3), 903-917.

  14. Giroux, Thomas, Julien Royer, and Olivier David Zerbib (2024): "Empirical Asset Pricing with Score-Driven Conditional Betas" Journal of Financial Econometrics, nbae007.

  15. Harvey, Andrew, Tan Hurn, Dario Palumbo, and Stephen Thiele (2024): "Modelling circular time series", Journal of Econometrics, 239(1), 105450.

  16. Herrera, Rodrigo and Marco Piña (2024): "Market risk modeling with option-implied covariances and score-driven dynamics" The North American Journal of Economics and Finance, 72, 102136.

  17. Jiang, Kunliang and Luo, Pengfei and Song, Jiashan and Wei, Siyao (2024) "Measuring Systemic Risk Contributions in the Chinese Sector Using the Dynamic Dependence with Asymmetric Long Memory", SSRN working paper, 4720952.

  18. Makatjane, Katleho (2024): "Forecasting Time-varying Value-at-Risk and Expected Shortfall Dependence: A Markov-switching Generalized Autoregressive Score Copula Approach", Austrian Journal of Statistics, 53(2).

  19. Umlandt, Dennis (2024): "Moment Conditions and Time-Varying Risk Premia", SSRN Working paper, no. 4736807.

  20. van Os, Bram, and Dick van Dijk (2024): "Accelerating peak dating in a dynamic factor Markov-switching model", International Journal of Forecasting, 40(1), 313-323.

  21. Vidal-Gutiérrez, Pedro, Sergio Contreras-Espinoz, Fransico Novoa-Muños (2024): "Modeling High-Frequency Zeros in Time Series with Generalized Autoregressive Score Models with Explanatory Variables: An Application to Precipitation," Axioms, 13(1), 15.

  22. Zheng, Tingguo, and Shiqi Ye (2024): "Cholesky GAS models for large time-varying covariance matrices", Journal of Management Science and Engineering, 9(1), 115-142.


  23. 2023

  24. Agosto, Arianna, and Paolo Giudici (2023): "Cyber Risk Contagion", Risks, 11(9), 165.

  25. Alhussaini, Abdullah and Chikhi, Mohamed and Diebolt, Claude and Mishra, Tapas (2023): "Memory and Predictability of Bitcoin Prices: An Arfima-Aegas Approach," SSRN working paper, 4415160.

  26. Artemova, Mariia (2023): "An Order-Invariant Score-Driven Dynamic Factor Model", Tinbergen Institute working paper, TI 2023-067/III.

  27. Ballestra, Luca Vincenzo, Enzo D'Innocenzo, and Andrea Guizzardi (2023): "Score-driven modelling with jumps: an application to S&P500 returns and options" Journal of Financial Econometrics, nbad001 (with correction nbad004).

  28. Barnett, William A., Xue Wang, Hai-Chuan Xu, and Wei-Xing Zhou (2023): "The stable tail dependence and influence among the European stock markets: a score-driven dynamic copula approach" European Journal of Finance , 29(16), 1933-1956.

  29. Beutner, Eric, Yicong Lin, and Andre Lucas (2023): "Consistency, distributional convergence, and optimality of score-driven filters," Tinbergen Institute Discussion Paper, 2023-051/III.

  30. Blasques, Francisco, Christian Franq, and Sébastien Laurent (2023):"Quasi score-driven models", Journal of Econometrics, 234(1), 251-275.

  31. Blasques, Francisco, Marc Nientker (2023): "Stochastic properties of nonlinear locally-nonstationary filters", Journal of Econometrics, 235(2), 2082-2095.

  32. Blazsek, Szabolcs, and Richard Bowen (2023): "Score-driven cryptocurrency and equity portfolios" Applied Economics, 56(18), 2109-2128.

  33. Blazsek, Szabolcs, and Alvaro Escribano (2023): "Score-driven threshold ice-age models: Benchmark models for long-run climate forecasts" Energy Economics, 118, 106522.

  34. Blazsek, Szabolcs, Alvaro Escribano, and A. Licht (2023):"Co-integration with score-driven models: an application to US real GDP growth, US inflation rate, and effective federal funds rate", Macroeconomic Dynamics, 27(4), 589-634.

  35. Buccheri, Giuseppe, Stefano Grassi, and Giorgio Vocalelli (2023): "Estimating Risk in Illiquid Markets: A Model of Market Friction with Stochastic Volatility" Journal of Financial Econometrics, nbad006.

  36. Catania, Leopoldo, Alessandra Luati (2023): "Semiparametric modeling of multiple quantiles", Journal of Econometrics, 237(2, part B), 105365.

  37. Chang, Kuang-Liang (2023): "The low-magnitude and high-magnitude asymmetries in tail dependence structures in international equity markets and the role of bilateral exchange rate" Journal of International Money and Finance, 133, 102839.

  38. Contreras-Espinoza, Sergio, Christian Caamaño-Carrillo, and Javier E. Contreras-Reyes (2023): "Generalized autoregressive score models based on sinh-arcsinh distributions for time series analysis" Journal of Computational and Applied Mathematics, 423, 114975.

  39. Contreras Espinoza, Sergio, Francisco Novoa-Muñoz, Szabolcs Blazsek, Pedro Vidal, and Christian Caamaño-Carrillo. (2023): "COVID-19 active case forecasts in Latin American countries using score-driven models" Mathematics (1).

  40. Custodio João, Igor, Andre Lucas, Julia Schaumburg, and Bernd Schwaab (2023): "Dynamic Clustering of Multivariate Panel Data", Journal of Econometrics, 237(2, part B), 105281.
    Click for Python computer code or for Ox computer code or to download the working paper version.

  41. D'Innocenzo, Enzo, Alessandra Luati, and Mario Mazzocchi (2023): "A robust score-driven filter for multivariate time series", Econometric Reviews, 42(5), 441-470.

  42. De Polis, Andrea, Leonardo Melosi, Ivan Petrella (2023): "The Ever-Changing Challenges to Price Stability," Proceedings ECB Forecasting Techniques converence.

  43. de S. Pereira, Manoel F., and Alvaro Veiga (2023): "Nonparametric option pricing under Beta-t-GARCH process with dynamic conditional score," Brazilian Review of Finance, 21(3), 73-98.

  44. Ezer, Deniz (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, 38, 105-126.

  45. Friedrich, Marina, Yicong Lin, Pavitram Ramdaras, Sean Telg, Bernard van der Sluis (2023): "Time-varying effects of housing attributes and economic environment on housing prices", Tinbergen Institute Discussion Paper, No. TI 2023-039/III.

  46. Fuentes, Fernanda, Herrera, Rodrigo, and Adam Clements (2023): "Forecasting extreme financial risk: A score-driven approach" International Journal of Forecasting, 39(2), 720-735.

  47. Gaete, Michael, and Rodrigo Herrera (2023): "Diversification benefits of commodities in portfolio allocation: A dynamic factor copula approach", Journal of Commodity Markets, 32, 100363.

  48. Gasperoni, Francesca, Alessandra Luati, Lucia Paci, and Enzo D'Innocenzo (2023): "Score-Driven Modeling of Spatio-Temporal Data", Journal of the American Statistical Association, 118(542), 1066-1077.

  49. Giroux, Thomas and Royer, Julien and Zerbib, Olivier David (2023): "Empirical Asset Pricing with Score-Driven Conditional Betas", SSRN Working paper 4368247.

  50. Gkillas, Konstantinos, Christoforos Konstantatos, Spyros Papathanasiou, Mark Wohar (2023): "Estimation of value at risk for copper", Journal of Commodity Markets, 32, 100351.

  51. Gorgi, Paolo, and Siem Jan Koopman (2023): "Beta observation-driven models with exogenous regressors: a joint analysis of realized correlation and leverage effects", Journal of Econometrics, 237(2, part B), 105177.
    Click here to download the working paper version.

  52. Gretener, Alexander Georges, Matthias Neuenkrish, and Dennis Umlandt (2023): "Dynamic Mixture Vector Autoregressions with Score-Driven Weights", . CESifo Working Paper No. 10366.

  53. Hafner, Christian, and Helmut Herwartz (2023): "Dynamic score driven independent component analysis" Journal of Business & Economic Statistics, 41(2), 298-308.

  54. Hafner, Chistian, and Linqi Wang (2023): "A dynamic conditional score model for the log correlation matrix", Journal of Econometrics, 237(2, part B), 105176.

  55. Harvey, Andrew, and Dario Palumbo (2023): "Score-driven models for realized volatility", Journal of Econometrics, 237(2, part B), 105448.

  56. Harvey, Andrew, and Dario Palumbo (2023): "Regime switching models for circular and linear time series", Journal of Time Series Analysis, 44(4), 374-392.

  57. Harvey, Andrew, and Yin Liao (2023): "Dynamic Tobit models", Econometrics and Statistics, 26, 72-83.

  58. Hetland, Simon, Rasmus Søndergaard Pedersen, and Anders Rahbek (2023): "Dynamic conditional eigenvalue GARCH", Journal of Econometrics, 237(2, part B), 105175.

  59. Holý, Vladimír (2023): "Ranking-Based Second Stage in Data Envelopment Analysis: An Application to Research Efficiency in Higher Education", Arxiv working paper.

  60. Ieracitano Viera, Leonardo, and Márcio Poletti Laurini (2023): "Time-varying higher moments in Bitcoin", Digital Finance (Springer), 5, 231-260.

  61. Labonne, Paul, and Leif Anders Thorsrud (2023): "Risky news and credit market sentiment", Norwegian Business School working paper, CAMP Working Paper Series No 14/2023.

  62. Lucas, Andre, Anne Opschoor (2023): "Time-varying variance and skewness in realized volatility measures", International Journal of Forecasting, 39(2), 827-840.
    Click here to download the working paper version.

  63. Mattera, Raffaele (2023): "Forecasting binary outcomes in soccer", Annals of Operations Research, 325, 115-134.

  64. Olanrewaju, Rasaki Olawale, Adejare Sodiq Olanrewaju, and Omodolapo Waliyat Isamot (2023): "Hyper-parametric Generalized Autoregressive Scores (GASs): an application to the price of United States cooking gas", Statistics in Transition, 24, 93-107.

  65. Patton, Andrew J., and Yasin Simsek (2023): "Generalized Autoregressive Score Trees and Forests", Arxiv working paper, 2305.18991.

  66. Piña, Marco and Hererra, Rodrigo (2023): "Market Risk Modeling with Option-Implied Covariances and Score-Driven Dynamics", SSRN working paper, 4359999.

  67. Reh, Laura, Fabian Krüger, and Roman Liesenfeld (2023): "Predicting the Global Minimum Variance Portfolio", Journal of Business and Economic Statistics, 41(2), 440-452.

  68. Sarlo, Rodrigo, Cristiano Fernandes, and Denis Borenstein (2023): "Lumpy and intermittent retail demand forecasts with score-driven models", European Journal of Operational Research, 307(3), 1146-1160.

  69. Song, Shijia, and Handong Li (2023): "A new model for forecasting VaR and ES using intraday returns aggregation", Journal of Forecasting, 42(5), 1039-1054.

  70. Song, Shijia, and Handong Li (2023): "A method for predicting VaR by aggregating generalized distributions driven by the dynamic conditional score", Quarterly Review of Economics and Finance, 88, 203-214.

  71. Telg, Sean, Anna Dubinova, and Andre Lucas (2023): "COVID-19, Credit Risk and Macro Fundamentals," Journal of Banking and Finance, 147, 106638.
    Click for computer code (R).

  72. Tsaknaki, Ioanna-Yvonni, Fabrizio Lillo, Piero Mazzarisi (2023): "Online Learning of Order Flow and Market Impact with Bayesian Change-Point Detection Methods", Arxiv working paper, 2307.02375.

  73. Umlandt, Dennis (2023): "Score-Driven Asset Pricing: Predicting Time-Varying Risk Premia based on Cross-Sectional Model Performance", Journal of Econometrics, 237(2, part C), 105470.
    Click for working paper version.

  74. Wang, Jie, and Yongqiao Wang (2023): "Jointly Forecasting Value-at-Risk and Expected Shortfall with Score-Driven Dynamic Relationships", SSRN working paper, 4495429.

  75. Yao, Zhong, and Min-Jian Li (2023): "GARCH-MIDAS-GAS-copula model for CoVaR and risk spillover in stock markets", North American Journal of Economics and Finance, 66, 101910.

  76. Zhang, Yong, Xinyue Li, Li Wang, Shurui Fan, Lei Zhu, Shuhao Jiang (2023): "An autocorrelation incremental fuzzy clustering framework based on dynamic conditional scoring model", Information Sciences, 648, 119567.

  77. Zheng, Tingguo, Shiqi Ye, and Yongmiao Hong (2023): "Fast estimation of a large TVP-VAR model with score-driven volatilities", Journal of Economic Dynamics and Control, 157, 104762.


  78. 2022

  79. Artemova, Mariia, Francisco Blasques, Janneke van Brummelen and Siem Jan Koopman (2022): "Score-Driven Models: Methodology and Theory" Oxford Research Encyclopedias: Economics and Finance.

  80. Artemova, Mariia, Francisco Blasques, Janneke van Brummelen and Siem Jan Koopman (2022): "Score-Driven Models: Methods and Applications" Oxford Research Encyclopedias: Economics and Finance.

  81. Alanya-Beltran, Willy (2022): "Modelling stock returns volatility with dynamic conditional score models and random shifts" Finance Research Letters, 102121.

  82. Alanya-Beltran, Willy (2022): "Modelling volatility dependence with score copula models" Studies in Nonlinear Dynamics & Econometrics.

  83. Ayala, Astrid, Szabolcs Blazsek, and Alvaro Escribano (2022): "Anticipating extreme losses using score-driven shape filters" Studies in Nonlinear Dynamics & Econometrics.

  84. Ayala, Astrid, Szabolcs Blazsek, and Adrian Licht (2022): "Score-driven stochastic seasonality of the Russian rouble: an application case study for the period of 1999 to 2020" Empirical Economics.

  85. Bai, Xiwen, and Manolis G. Kavussanos (2022): "Hedging IMO2020 compliant fuel price exposure using futures contracts", Energy Economics, 110, 106029.

  86. Blazsek, Szabolcs, Astrid Ayala, and Adrian Licht (2022): "Signal smoothing for score-driven models: a linear approach", Communications in Statistics - Simulation and Computation.

  87. Blazsek, Szabolcs, Virag Blazsek, and Adam Kobor (2022): "Conservatorship, quantitative easing, and mortgage spreads: a new multi-equation score-driven model of policy actions" Studies in Nonlinear Dynamics & Econometrics.

  88. Blazsek, Szabolcs, Alvaro Escribano (2022): "Robust estimation and forecasting of climate change using score-driven ice-age models" Econometrics (Special Issue: Econometric Analysis of Climate Change) (1).

  89. Blazsek, Szabolcs, Alvaro Escribano, and Adrian Licht (2022): "Score-driven location plus scale models: asymptotic theory and an application to forecasting Dow Jones volatility" Studies in Nonlinear Dynamics & Econometrics.

  90. Blazsek, Szabolcs, and Michel Haddad (2022): "Non-path-dependent score-driven multi-regime Markov-switching EGARCH: empirical evidence" Studies in Nonlinear Dynamics & Econometrics.

  91. Blasques, Francisco, Janneke van Brummelen, Siem Jan Koopman and Andre Lucas (2022): "Maximum Likelihood Estimation for Score-Driven Models", Journal of Econometrics, 227 (2), 325-346.
    Click here to download the working paper version.

  92. Campajola, Carlo, Domenico Di Gangi, Fabrizio Lillo and Daniele Tantari (2022): "Modelling time-varying interactions in complex systems: The score driven kinetic ising model" Scientific Reports (19339).

  93. Cerqueti, Roy, Pierpaolo D'Urso, Livia De Giovanni, Massimiliano Giacalone, Raffaele Mattera (2022): "Weighted score-driven fuzzy clustering of time series with a financial application", Expert Systems with Applications, 198, 116752.

  94. Dias Cordeiro de Castro, Carlos Henrique, and Fernando Antonio Lucena Aiube (2022): "Forecasting inflation time series using score-driven dynamic models and combination methods: The case of Brazil", Journal of Forecasting, 42(2), 369-401.

  95. Gong, Yuting, Zhongzhi He, and Wenjun Xue (2022): "EPU spillovers and stock return predictability: A cross-country study" Journal of International Financial Markets, Institutions and Money, 78, 101556.

  96. Franq, Christian, and Jean-Michel Zakoïan (2022): "Local asymptotic normality of general conditionally heteroskedastic and score-driven time-series models", Econometric Theory, 39(5), 1067-1092.

  97. Gong, Yuting, Chao Ma, and Qiang Chen (2022): "Exchange rate dependence and economic fundamentals: A Copula-MIDAS approach" Journal of International Money and Finance, 123, 102597.

  98. Han, Yingwei, and Jie Li (2022): "Should investors include green bonds in their portfolios? Evidence for the USA and Europe" International Review of Financial Analysis, 80, 101998.

  99. Hansen, Peter Reinhard , and Chen Tong(2022): "Option Pricing with Time-Varying Volatility Risk Aversion" Arxiv working paper, 2204.06943.

  100. Heil, Thomas, Peter, Franziska J., and Philipp Prange (2022): "Measuring 25 years of global equity market co-movement using a time-varying spatial model" Journal of International Money and Finance, 128, 102708.

  101. Holý, Vladimír, and Petra Tomanová (2022): "Modeling price clustering in high-frequency prices" Quantitative Finance (9), 1649-1663.

  102. Holý, Vladimír, and Jan Zouhar (2022): "Modelling time-varying rankings with autoregressive and score-driven dynamics" Journal of the Royal Statistical Society: Series C (Applied Statistics) (5), 1427-1450.

  103. Jiang, Kunliang, Linhui Zeng, Jiashan Song, Yimeng Liu (2022): "Forecasting Value-at-Risk of cryptocurrencies using the time-varying mixture-accelerating generalized autoregressive score model" Research in International Business and Finance 61, 101634.

  104. Labonne, Paul (2022): "Asymmetric Uncertainty: Nowcasting Using Skewness in Real-time Data", Economic Statistics Centre of Excellence (ESCoE) Discussion Papers, ESCoE DP-2022-23.

  105. Lu, Yang and Wei, Pengyu and Xu, Mengyi (2022): "Modeling Longevity and Disability with Generalized Autoregressive Score Models", SSRN working paper, 4282024.

  106. Ouyang, Ruolan, Chengkai Zhuang, Tingting Wang, and Xuan Zhang (2022): "Network analysis of risk transmission among energy futures: An industrial chain perspectiv", Energy Economics, 107, 105798.

  107. Owusu, Peterson Junior, Aviral Kumar Tiwari, George Tweneboah, and Emmanuel Asafo-Adjeia (2022): "GAS and GARCH based value-at-risk modeling of precious metals", Resources Policy, 75 (102456).

  108. Sarlo, Rodrigo, Cristiano Fernandes, and Denis Borenstein (2022): "Lumpy and intermittent retail demand forecasts with score-driven models", European Journal of Operational Research.

  109. Song, Shijia, and Handong Li (2022): "Predicting VaR for China's stock market: A score-driven model based on normal inverse Gaussian distribution", International Review of Financial Analysis, 82, 102180.

  110. Sucarrat, Genaro, and Steffen Grønneberg (2022): "Risk Estimation with a Time-Varying Probability of Zero Returns", Journal of Financial Econometrics, 20(2), 278-309.

  111. Xu, Yingying, and Donald Lien (2022): "Forecasting volatilities of oil and gas assets: A comparison of GAS, GARCH, and EGARCH models", Journal of Forecasting, 41(2), 259-278.


  112. 2021

  113. Ayala, A., Blazsek, S., and Licht, A. (2021): "Volatility forecasting for the coronavirus pandemic using quasi-score-driven models", Discussion Paper 2/2021, Francisco Marroquin University, School of Business.

  114. Ayala, A., Blazsek, S., and Licht, A. (2021): "Optimal signal extraction for score-driven models", Discussion Paper 5/2020, Francisco Marroquin University, School of Business.

  115. Blasques, Francisco, Paolo Gorgi and Siem Jan Koopman (2021): "Missing Observations in Observation-Driven Time Series Models", Journal of Econometrics, 221 (2), 542-568.
    Click here to download the working paper version.

  116. Blasques, Francisco, Meindert Heres Hoogerkamp, Siem Jan Koopman, and Ilka van de Werve (2021): "Dynamic factor models with clustered loadings: Forecasting education flows using unemployment data", International Journal of Forecasting, 37 (4), 1426-1441.

  117. Blasques, Francisco, Andre Lucas, and Andries C. van Vlodrop (2021): "Finite Sample Optimality of Score-Driven Volatility Models: Some Monte Carlo Evidence", 19, 47-57.
    Click here to download the working paper version.

  118. Blazsek, S., Blazsek, V., and Kobor, A. (2021): "Conservatorship, quantitative easing, and mortgage spreads: A new multi-equation score-driven model of policy actions", Discussion Paper 3/2021, Francisco Marroquin University, School of Business.

  119. Blazsek, S., Escribano, A., and Licht, A. (2021): "Multivariate Markov-switching score-driven models: an application to the global crude oil market", Studies in Nonlinear Dynamics & Econometrics.

  120. Blazsek, S., and Licht, A. (2021): "Prediction accuracy of volatility using the score-driven Meixner distribution: an application to the Dow Jones", Applied Economics Letters, (), -.

  121. Buccheri, Giuseppe, Giacomo Bormetti, Fulvio Corsi, and Fabrizio Lillo (2021): "A Score-driven Conditional Correlation Model for Noisy and Asynchronous Data: An Application to High-frequency Covariance Dynamics", Journal of Business and Economic Statistics, 39 (4), 920-936.
    Click here to download the working paper version.

  122. Buccheri, Giuseppe, and Fulvio Corsi (2021): "Hark the Shark: Realized Volatility Modelling with Measurement Errors and Nonlinear Dependencies", Journal of Financial Econometrics, 19 (4), 614-649.
    Click here to download the working paper version.

  123. Buccheri, Giuseppe, Fulvio Corsi, Franco Flandoli, Giulia Livieri (2021): "The Continuous-Time Limit of Score-Driven Volatility Models", Journal of Econometrics, 221 (2), 655-675.
    Click here to download the working paper version.

  124. Catania, Leopoldo (2021): "Dynamic Adaptive Mixture Models with an Application to Volatility and Risk", Journal of Financial Econometrics, 19 (4), 531-564.
    Click here to download the working paper version.

  125. Cerqueti, Roy, Massimiliano Giacalone, and Raffaele Mattera (2021): "Model-based fuzzy time series clustering of conditional higher moments", International Journal of Approximate Reasoning, 134, 34-52.

  126. Custodio João}, Igor, Andre Lucas, Julia Schaumburg, and Bernd Schwaab (2021): "Dynamic Clustering of Multivariate Panel Data", ECB Working Paper No. 2021/2577.
    Click for Python computer code or for Ox computer code.

  127. Delle Monache, Davide, Andrea De Polis, and Ivan Petrella (2021): "Modeling and Forecasting Macroeconomic Downside Risk", Bank of Italy Working Paper, No. 1324.

  128. Delle Monache, Davide, Ivan Petrella and Fabrizio Venditti (2021): "Price Dividend Ratio and Long-Run Stock Returns: A Score-Driven State Space Model", Journal of Business and Economic Statistics, 39 (4), 1054-1065.

  129. Dubinova, Anna, Andre Lucas, and Sean Telg (2021): "COVID-19, Credit Risk and Macro Fundamentals", Tinbergen Institute Discussion Paper, 2021-059/III.

  130. Gammoudi, Imed, Asma Nani, and Mohamed El Ghourabi (2021): "Generalized quasi maximum likelihood estimation for generalized autoregressive score models: simulations and real applications", Communications in Statistics - Simulation and Computation, 50 (11), 3338-3363.

  131. Hansen, Peter R., and Matthias Schmidtblaicher (2021): "A Dynamic Model of Vaccine Compliance: How Fake News Undermined the Danish HPV Vaccine Program", Journal of Business and Economic Statistics, 39, 259-271.
    Click here to download the working paper version.

  132. Hoeltgebaum, Henrique, Denis Borenstein, Cristiano Fernandes, and álvaro Veiga (2021): "A score-driven model of short-term demand forecasting for retail distribution centers", Journal of Retailing, 97 (4), 715-725.

  133. Jeribi, Ahmed, and Achraf Ghorbel (2021): "Forecasting developed and BRICS stock markets with cryptocurrencies and gold: generalized orthogonal generalized autoregressive conditional heteroskedasticity and generalized autoregressive score analysis", International Journal of Emerging Markets,.

  134. Labonne, Paul (2021): "Capturing GDP nowcast uncertainty in real time", arXiv 2012.02601.

  135. Lauria, Christopher Sacha Aristide (2021): "On Optimality of Score Driven Models", DOTTORATO DI RICERCA IN: Scienze Statistiche, Ciclo: XXXIII Settore Concorsuale: 13/D1 Statistica, Settore Scientifico Disciplinare: SECS-S/01 Statistica.

  136. Opschoor, Anne, Andre Lucas, Istvan Barra, Dick J. van Dijk (2021): "Closed-Form Multi-Factor Copula Models with Observation-Driven Dynamic Factor Loadings", Journal of Business and Economic Statistics, 39 (4), 1066-1079.
    Click here to download the working paper version.

  137. Palumbo, Dario (2021): "Testing and Modelling Time Series with Time Varying Tails", University of Cambridge Working Paper in Economics, CWPE-2111.

  138. Prange, Philipp (2021): "News impact dynamics in the correlations of global equity returns for systemic risk measurement", SSRN working paper, 3912188.

  139. Tomanová, Petra, and Vladimír Holý (2021): "Clustering of arrivals in queueing systems: autoregressive conditional duration approach" Central European Journal of Operations Research (3), 859-874.

  140. Yarovaya, Larisa, Roman Matkovskyy,and Akanksha Jalan (2021): "The effects of a "black swan" event (COVID-19) on herding behavior in cryptocurrency markets," Journal of International Financial Markets, Institutions and Money, 75 (101321),

  141. Zhang, Xingmin, Andre Lucas, and Anne Opschoor (2021): "The Importance of Heterogeneity in Dynamic Network Models Applied to European Systemic Risk", Tinbergen Institute Discussion Paper, 2021-085/III.


  142. 2020

  143. Ayala, A., and Blazsek, S. (2020): "Score-driven panel data models of the capital structure of US firms", Applied Economics Letters, (), -.

  144. Blazsek, S., Escribano, A, and Licht, A. (2020): "Identification of seasonal effects in impulse responses using score-driven multivariate location models", Journal of Econometric Methods, 10 (1), 53-66.

  145. Blazsek, S., Escribano, A., and Licht, A. (2020): "Prediction accuracy of bivariate score-driven risk premium and volatility filters: an illustration for the Dow Jones", Working Paper 2020-10, University Carlos III of Madrid, Department of Economics.

  146. Blazsek, S., Escribano, A., and Licht, A. (2020): "Dynamic stochastic general equilibrium inference using a score-driven approach", Working Paper 20-05, University Carlos III of Madrid, Department of Economics.

  147. Blazsek, S., and Haddad, M. (2020): "Estimation and statistical performance of Markov-switching score-driven volatility models: the case of G20 stock markets", Discussion Paper 1/2020, Francisco Marroquin University, School of Business.

  148. Blazsek, S., and Licht, A. (2020): "Robust score-driven inference of stochastic seasonality of the Russian rouble for different currency exchange rate regimes from 1999 to 2020", Discussion Paper 4/2020, Francisco Marroquin University, School of Business.

  149. Blazsek, Szabolcs, and Adrian Licht (2020): "Dynamic conditional score models: a review of their applications," Applied Economics, 52 (11), 1181-1199.

  150. Caballero, Diego, Andre Lucas, Bernd Schwaab, and Xin Zhang (2020): "Risk endogeneity at the lender/investor-of-last-resort", Journal of Monetary Economics, 116, 283-297.
    Click here to download the ECB working paper version.

  151. Catania, Leopoldo, Alessandra Luati, and Emil Bach Mikkelsen (2020): "Dynamic Multiple Quantile Models", SSRN Working Paper, 3727513.

  152. Catania, Leopoldo, and Nima Nonejad (2020): "Density forecasts and the leverage effect: Evidence from Observation and parameter-driven volatility models", European Journal of Finance, 26 (2-3), 100-118.

  153. Gorgi, Paolo, and 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.

  154. Lasak, Katarzyna and Johannes Lont (2020): "Observation Driven Long Run Equilibria", Computational Economics, 55(2), 551-575.

  155. Lazar, Emese, and Xiaohan Xue (2020): "Forecasting risk measures using intraday data in a generalized autoregressive score framework", International Journal of Forecasting, 36 (3), 1057-1072.

  156. Li, Haiping, Artur Semeyutin, Chi Keung Marco Lau, and Giray Gozgor (2020): "The relationship between oil and financial markets in emerging economies: The significant role of Kazakhstan as the oil exporting country ", Finance Research Letters, 32, 101171.

  157. Linton, Oliver, Jianbin Wu (2020): "A coupled component DCS-EGARCH model for intraday and overnight volatility", Journal of Econometrics, 217 (1), 176-201.

  158. Nitoi, Mihai, and Maria Miruna Pochea (2020): "Time-varying dependence in European equity markets: A contagion and investor sentiment driven analysis", Economic Modelling, 86, 133-147.

  159. Ouyang, Ruolan, and Xuan Zhang (2020): "Financialization of agricultural commodities: Evidence from China", Economic Modelling, 85, 381-389.

  160. Owusu, Peterson Junior, and Imhotep Alagidede (2020): "Risks in emerging markets equities: Time-varying versus spatial risk analysis", Physica A, 542, 123474.

  161. Thiele, Stephen (2020): "Modeling the conditional distribution of financial returns with asymmetric tails ", Journal of Applied Econometrics, 35 (1), 46-60.

  162. Tomanová, Petra, and Vladimír Holý (2020): "Clustering of Arrivals in Queueing Systems: Autoregressive Conditional Duration Approach", Arxiv 2003.14382 working paper.

  163. Umlandt, Dennis (2020): "Score-Driven Asset Pricing: Predicting Time-Varying Risk Premia based on Cross-Sectional Model Performance", SSRN Working paper 3666324.

  164. Schwaab, Bernd, Xin Zhang, and Andre Lucas (2020): "Modeling extreme events: time-varying extreme tail shape", Tinbergen Institute Discussion Papers 20-076/III, Tinbergen Institute.

  165. Xu, Yingying, and Donald Lien (2020): "Dynamic exchange rate dependences: The effect of the U.S.-China trade war", Journal of International Financial Markets, Institutions and Money, 68 (101238).

  166. Xu, Yingying, and Donald Lien (2020): "Optimal futures hedging for energy commodities: An application of the GAS model", Journal of Futures Markets, 40 (7), 1090-1108.

  167. Yuan, Nannan, and Lu Yang (2020): "Asymmetric risk spillover between financial market uncertainty and the carbon market: A GAS-DCS-copula approach", Journal of Cleaner Production, 259, 120750.


  168. 2019

  169. Ardia, David, Kris Boudt, and Leopoldo Catania (2019): "Generalized Autoregressive Score Models in R: The GAS Package", Journal of Statistical Software, 88(6), 1-28.
    Click here to download the working paper version.

  170. Ayala, Astrid, and Szabolcs Blazsek (2019): "Score-driven currency exchange rate seasonality as applied to the Guatemalan Quetzal/US Dollar", SERIEs (Journal of the Spanish Economic Association), 10(1), 65-92.

  171. Ayala, Astrid, and Szabolcs Blazsek (2019): "Score-driven models of stochastic seasonality in location and scale: an application case study of the Indian rupee to USD exchange rate", Applied Economics, 51(37), 4083-4103.

  172. Ayala, Astrid, Szabolcs Blazsek, and Alvaro Escribano (2019): "Score-driven time series models with dynamic shape: An application to the Standard & Poor's 500 index," Universidad Carlos III de Madrid. Departamento de Economia, Working paper 19-05.

  173. Ayala, Astrid, Szabolcs Blazsek, and Alvaro Escribano (2019): "Maximum likelihood estimation of score-driven models with dynamic shape parameters: an application to Monte Carlo value-at-risk," Universidad Carlos III de Madrid. Departamento de Economia, Working paper 19-12.

  174. Babatunde, Oluwagbenga, OlaOluwa Simon Yaya, and Damola M. Akinlana (2019): "Misspecification of Generalized Autoregressive Score Models: Monte Carlo Simulations and Applications", International Journal of Mathematics Trends and Technology (IJMTT), 65(3), 72-80.

  175. Babii, Andrii, Xi Chen, and Eric Ghysels (2019): "Commercial and Residential Mortgage Defaults: Spatial Dependence with Frailty", Journal of Econometrics, 212(1), 47-77.

  176. Bekar, Bekar (2019): "Les Prévisions Statiques et Dynamiques des Valeurs A Risque (VaR) des Actions de Banque: Le Modele de Dépassements de Seuil (POT) et Les Modeles de Score Autorégressifs Généralisés (GAS)," The Journal of Academic Social Sciences (97), 142-169.

  177. Blasques, Francisco, Paolo Gorgi, and Siem Jan Koopman (2019): "Accelerating GARCH and Score-Driven Models: Optimality, Estimation and Forecasting", Journal of Econometrics, 212(2), 359-376.
    Click here to download the working paper version.

  178. Blasques, Francisco, Vladimir Holy, and Petra Tomanova (2019): "Zero-Inflated Autoregressive Conditional Duration Model for Discrete Trade Durations with Excessive Zeros", Tinbergen Institute Discussion Paper, 19-004/III.

  179. Blazsek, Szabolcs, Alvaro Escribano, and Adrian Licht (2019): "Co-integration and common trends analysis with score-driven models : an application to the federal funds effective rate and US inflation," Universidad Carlos III de Madrid. Departamento de Economia, Working paper 19-08.

  180. Blazsek, Szabolcs, Alvaro Escribano, and Adrian Licht (2019): " Markov-switching score-driven multivariate models: outlier-robust measurement of the relationships between world crude oil production and US industrial production, " Universidad Carlos III de Madrid. Departamento de Economia, Working paper 19-16.

  181. Bernardi, Mauro, and Leopoldo Catania (2019): "Switching generalized autoregressive score copula models with application to systemic risk", Journal of Applied Econometrics, 34(1), 43-65.

  182. Buccheri, Giuseppe, Fulvio Corsi, Franco Flandoli, Giulia Livieri (2019): "The Continuous-Time Limit of Score-Driven Volatility Models", SSRN Working Paper, No. 3382479.

  183. Caballero, Diego, Andre Lucas, Bernd Schwaab, and Xin Zhang (2019): "Risk endogeneity at the lender/investor-of-last-resort", ECB Working Paper, No. 2225.

  184. Chen, Rongda, and Jianjun Xu (2019): "Forecasting volatility and correlation between oil and gold prices using a novel multivariate GAS model", Energy Economics, 78, 379-391.

  185. Chikhi, Mohamed, Claude Diebolt, and Tapas Mishra (2019): " Memory that Drives! New Insights into Forecasting Performance of Stock Prices from SEMIFARMA-AEGAS Model ", Working Paper BETA, Bureau d'economie theorique et appliquee, 2019-24.

  186. Chou, Ray Y., Tso-Jung Yen, and Yu-Min Yen (2019): "Forecasting Expected Shortfall and Value-at-Risk with the FZ Loss and Realized Variance Measures", SSRN 3448882.

  187. Di Gangi, Domenico, Giacomo Bormetti, and Fabrizio Lillo (2019): "Score-Driven Exponential Random Graphs: A New Class of Time-Varying Parameter Models for Dynamical Networks", Working Paper University of Bologna.

  188. Gong, Xiao-Li, Xi-Hua Liu, and Xiong Xiong (2019): "Measuring tail risk with GAS time varying copula, fat tailed GARCH model and hedging for crude oil futures", Pacific-Basin Finance Journal, 55, 95-109.

  189. Gorgi, Paolo, Peter R. Hansen, Pawel Janus and Siem Jan Koopman (2019): "Realized Wishart-GARCH: A Score-driven Multi-Asset Volatility Model", Journal of Financial Econometrics, 17(1), 1-32.
    Click here to download the working paper version.
    Click here to download the R code.

  190. Gorgi, P., Koopman, S.J. and Lit, R. (2019): "The analysis and forecasting of tennis matches by using a high dimensional dynamic model", Journal of the Royal Statistical Society, Series A, 182(4), 1393-1409.
    Click here to download the working paper version.

  191. Harvey, Andrew, and Ryoko Ito (2019): " Modeling time series when some observations are zero ", Journal of Econometrics, 214(1), 33-45.
    Click here to download the working paper version.

  192. Harvey, Andrew, and Yin Liao (2019): "Dynamic TOBIT models", University of Cambridge Working Paper, CWPE-1913.

  193. Harvey, Andrew, and Dario Palumbo (2019): "Score-driven models for realized volatility", University of Cambridge Working Paper, CWPE-1950.

  194. He, Yijin, and Shigeyuki Hamori (2019): " Conditional Dependence between Oil Prices and Exchange Rates in BRICS Countries: An Application of the Copula-GARCH Model ", Journal of Risk and Financial Management 12(2).

  195. Koopman, Siem Jan, and Rutger Lit (2019): "Forecasting football match results in national league competitions using score-driven time series models", International Journal of Forecasting 35(2), 797-809.
    Click here to download the working paper version.

  196. Kushwah, Anil K., and Rajesh Wadhvani (2019): "Performance monitoring of wind turbines using advanced statistical methods", Engineering (Sadhana) 44(163), CWPE-1913.

  197. Lazar, Esmese, and Xiaohan Xue (2019): " Forecasting Risk Measures Using Intraday Data in a Generalized Autoregressive Score (GAS) Framework", SSRN Working Paper 3395888, University of Reading.

  198. Lucas, Andre, Anne Opschoor (2019): "Fractional Integration and Fat Tails for Realized Covariance Kernels and Returns", Journal of Financial Econometrics, 17(1), 66-90.
    Click here to download the working paper version.

  199. Lucas, Andre, Anne Opschoor (2019): "Observation-driven Models for Realized Variances and Overnight Returns", Tinbergen Institute Discussion Papers 19-052/IV.

  200. Lucas, Andre, Anne Opschoor (2019): "Time-varying tail behavior for realized kernels", Tinbergen Institute Discussion Papers 19-051/IV.

  201. Lucas, Andre, Bernd Schwaab, and Julia Schaumburg (2019): "Bank business models at zero interest rates", Journal of Business and Economic Statistics, 37(3), 542-555.
    Click here to download the working paper version.
    Go here for example computer code (in Ox).

  202. Lucas, Andre, Julia Schaumburg, and Bernd Schwaab (2019): "Dynamic clustering of multivariate panel data", working paper.

  203. Mandla Manguzvane, Mathias, and John Weirstrass Muteba Mwamba (2019): "GAS-Copula Models on who's systemically important in South Africa: Banks or Insurers?", Empirical Economics, 15 April.

  204. Matkovskyy, Roman, and Akanksha Jalan (2019): "Effects of economic policy uncertainty shocks on the interdependence between cryptocurrency and traditional financial markets", Working paper Rennes School of Business.

  205. Nani, Asma, Imed Gammoudi, and Mohamed El Ghourabi (2019): "Value-at-risk estimation by LS-SVR and FS-LS-SVR based on GAS model", Journal of Applied Statistics, 46(12), 2237-2253.

  206. Opschoor, Anne, Andre Lucas, Istvan Barra, and Dick J. van Dijk (2019): "Closed-Form Multi-Factor Copula Models with Observation-Driven Dynamic Factor Loadings", Tinbergen Institute Discussion Paper 19-013/IV.

  207. Patton, Andrew J., Johanna F. Ziegel, and Rui Chen (2019): "Dynamic Semiparametric Models for Expected Shortfall (and Value-at-Risk)", Journal of Econometrics, 211(2), 388-413.
    Click here to download the working paper version.
    Computer code can be found in the code section.

  208. Tranberg, Bo, Rasmus Thrane Hansen, and Leopoldo Catania (2019): "Managing Volumetric Risk of Long-term Power Purchase Agreements", SSRN Paper 3278789.

  209. Troster, Victor, Aviral Kumar Tiwarib, Muhammad Shahbazb, and Demian Nicolás Macedoc (2019): "Bitcoin returns and risk: A general GARCH and GAS analysis," Finance Research Letters, 30, 187-193.

  210. Zamojski, Marcin (2019): "Self-driving score filters", University of Gothenburg. Includes computer code.


  211. 2018

  212. Angelini, Giovanni, and Paolo Gorgi (2018): "DSGE Models with Observation-Driven Time-Varying parameters", Economics Letters, 171, 169-171.
    Click here to download the working paper version.

  213. Ardia, David, Kris Boudt, and Leopoldo Catania (2018): "Downside risk evaluation with the R package GAS", R Journal, 10(2), 410-421.
    Click here to download the working paper version.

  214. Asare, Nyamekye (2018): "Essays on Time-Varying Volatility and Structural Breaks in Macroeconomics and Econometrics," (Ch.3+4) PhD Thesis, Department of Economics, Faculty of Social Sciences, University of Ottawa .

  215. Ayala, Astrid, and Szabolcs Blazsek (2018): "Equity market neutral hedge funds and the stock market: an application of score-driven copula models", Applied Economics, 50(37), 4005-4023.

  216. Ayala, Astrid, and Szabolcs Blazsek (2018): "Score-driven copula models for portfolios of two risky assets", European Journal of Finance, 24(18), 181-1884.

  217. Bernardi A., Bernardi M. (2018): "Two-Sided Skew and Shape Dynamic Conditional Score Models," in: Corazza M., Durbán M., Grané A., Perna C., Sibillo M. (eds) Mathematical and Statistical Methods for Actuarial Sciences and Finance, Springer, Cham, pp. 121-124.

  218. Bernardi, Mauro, and Leopoldo Catania (2018): "Portfolio Optimisation Under Flexible Dynamic Dependence Modelling", Journal of Empirical Finance, 48, 1-18.
    Click here to download the working paper version.

  219. Bisaglia, Luisa, and Matteo Grigoletto (2018): "A new time-varying model for forecasting long-memory series", Available at Arxiv.

  220. Bille, Anna Gloria, Francisco Blasques, and Leopoldo Catania (2018): "Dynamic Spatial Autoregressive Models with Time-Varying Spatial Weighting Matrices", Available at SSRN.

  221. Blazsek, Szabolcs, Alvaro Escribano, and Adrian Licht (2018): "Seasonality Detection in Small Samples using Score-Driven Nonlinear Multivariate Dynamic Location Models," Universidad Carlos III de Madrid. Departamento de Economia, Working paper 18-09.

  222. Blazsek, S., Ho, H.-C., and Liu, S.-P. (2018): " Score-driven Markov-switching EGARCH models: an application to systematic risk analysis", Applied Economics, 50(56), 6047-6060.

  223. Blazsek, S., and Hernandez, H. (2018): " Analysis of electricity prices for Central American countries using dynamic conditional score models", Empirical Economics, 55(4), 1807-1848.

  224. Blazsek, S., and A. Licht (2018): " Robustness of score-driven location and scale models to extreme observations: An application to the Chinese stock market", Financial Statistical Journal, 1(2), 1-10.

  225. Buccheri, Giuseppe, Giacomo Bormetti, Fulvio Corsi, and Fabrizio Lillo (2018): "Filtering and Smoothing with Score-Driven Models", SSRN Discussion Paper 3139666.

  226. Blasques, Francisco, Paolo Gorgi and Siem Jan Koopman (2018): "Missing Observations in Observation-Driven Time Series Models", Tinbergen Institute Discussion Paper, TI 18-013/III.

  227. Blasques, Francisco, Paolo Gorgi, Siem Jan Koopman and Olivier Wintenberger (2018): "Feasible invertibility conditions and maximum likelihood estimation for observation-driven models", Electronic Journal of Statistics, 12, 1019-1052.

  228. Blasques, Francisco, Andre Lucas and Erkki Silde (2018): "A Stochastic Recurrence Equations Approach for Score Driven Correlation Models", Econometric Reviews 37(2), 166-181.
    Click here to download the working paper version.

  229. Blazsek, S., Carrizo, D., Eskildsen, R., and Gonzalez, H. (2018): "Forecasting rate of return after extreme values when using AR-t-GARCH and QAR-Beta-t-EGARCH", Finance Research Letters , 24, 193-198.

  230. Catania, Leopoldo; Grassi, Stefano; Ravazzolo, Francesco (2018): "Predicting the Volatility of Cryptocurrency Time Series", in Mathematical and Statistical Methods for Actuarial Sciences and Finance, M. Corazza, M. Durban, A. Grane, C. Perna, M. Sibillo (eds.), pp. 203-207.
    Click here to download the working paper version.

  231. Caballero, Diego, Andre Lucas, Bernd Schwaab, and Xin Zhang (2018): "Risk endogeneity at the lender/investor-of-last-resort", Preliminary working paper.

  232. de Melo, Mariana Arozo B., Cristiano A.C. Fernandes, and Eduardo F.L. de Melo (2018): "Forecasting aggregate claims using score-driven time series models", Statistica Neerlandica, 72(3), 354-374.

  233. Eckernkemper, Tobias (2018): "Modeling Systemic Risk: Time-Varying Tail Dependence When Forecasting Marginal Expected Shortfall", Journal of Financial Econometrics 16(1), 63-117.

  234. Fonseca, Rodney V., and Francisco Cribari-Neto (2018): "Bimodal Birnbaum Saunders generalized autoregressive score model", Journal of Applied Statistics, 45(14), 2585-2606.

  235. Gorgi, Paolo (2018): "Integer-valued autoregressive models with survival probability driven by a stochastic recurrence equation", Journal of Time Series Analysis, 39, 150-171.
    Click here to download the working paper version.

  236. Gorgi, Paolo, Siem Jan Koopman and Mengheng Li (2018): "Forecasting economic time series using score-driven dynamic models with mixed-data sampling", Tinbergen Institute Discussion Paper, TI 18-026/III.

  237. Gorgi, Paolo, Siem Jan Koopman and Rutger Lit (2018): "The analysis and forecasting of ATP tennis matches using a high-dimensional dynamic model", Tinbergen Institute Discussion Paper, TI #18-009/III.

  238. Hansen, Peter R., and Matthias Schmidtblaicher (2018): "A Dynamic Model of Vaccine Compliance: How Fake News Undermined the Danish HPV Vaccine Program", Working paper. Published in JBES 2019.

  239. Harvey, Andrew, and Rutger-Jan Lange (2018): "Modelling the Interactions between Volatility and Returns using EGARCH-M", Journal of Time Series Analysis, 39(6), 909-919.
    Click here to download the working paper version.

  240. Hoeltgebaum, H., Street, A., & Fernandes, C. (2018): " Generating Joint Scenarios for Renewable Generation: The Case for non-Gaussian Models with Time-Varying Parameters", IEEE Transactions on Power Systems.

  241. Koopman, Siem Jan, Rutger Lit, Andre Lucas, and Anne Opschoor (2018): "Intraday Stock Price Dependence using Dynamic Discrete Copula Distributions", Journal of Applied Econometrics, 33(7), 966-985.
    Click here to download the working paper version.

  242. Laporta, Alessandro G., Luca Merlo, and Lea Petrella (2018): " Selection of Value at Risk Models for Energy Commodities", Energy Economics, 74, 628-643.

  243. Oh, Dong Hwan and Andrew Patton (2018): "Time-varying systemic risk: evidence from a dynamic copula model of CDS spreads", Journal of Business and Economic Statistics, 36(2), 181-195.

  244. Opschoor, Anne, Pawel Janus, Andre Lucas, and Dick J. van Dijk (2018): "New HEAVY Models for Fat-Tailed Realized Covariances and Returns", Journal of Business and Economic Statistics, 36(4), 643-657.
    Click here to download the working paper version.
    Click here to download COMPUTER CODE.

  245. Qiu, Feng (2018): "Dynamic Conditional Correlations and Tail Dependencies: Copula-Based Vertical Price Co-movement Analysis", Working paper.

  246. Rasaki, Olanrewaju O., and Folorunsho A. Serifat (2018): "Generalized autoregressive score (GAS) functions under Gaussian and student-t distributions", International Journal of Statistics and Applied Mathematics, 3(5), 56-61.

  247. Tafakori, Laleh, Armin Pourkhanali, and Farzad Alavi Fard (2018): "Forecasting spikes in electricity return innovations", Energy, 150, 508-526.

  248. Vassallo, Danilo, Giuseppe Buccheri, and Fulvio Corsi (2018): "A DCC-type approach for Realized Covariance modelling with score-driven dynamics", Research Gate working paper.

  249. Yueshen, Bart Z., and Zhang, Jinyuan (2018): "Dynamic Trade Informativeness", SSRN Working paper 3119538.


  250. 2017

  251. Alanya, Willy (2017): Modelling the Uncertainty in the Peruvian Stock and Forex markets with Dynamic Conditional Score Models", PhD Dissertation, University of Cambridge, UK.

  252. Ayala, Astrid, Szabolcs Blazsek, and Alvaro Escribano (2017): "Dynamic Conditional Score Models with Time-Varying Location, Scale and Shape Parameters," Universidad Carlos III de Madrid. Departamento de Economia, Working paper 17-08.

  253. Barbosa de Alencar, David, Carolina de Mattos Affonso, Roberto Célio Limão de Oliveira, Jorge Laureano Moya Rodríguez, Jandecy Cabral Leite, and José Carlos Reston Filho (2017): "Different Models for Forecasting Wind Power Generation: Case Study", Energies 10(12), 1976.

  254. Banulescu, Denisa, Peter Reinhard Hansen, Zhuo Huang, and Marius Matei (2017): "Volatility During the Financial Crisis Through the Lens of High Frequency Data: A Realized GARCH Approach", ADEMU Working Paper Series, WP2017/063.

  255. Bazzi, Marco, Francisco Blasques, Siem Jan Koopman and Andre Lucas (2017): "Time Varying Transition Probabilities for Markov Regime Switching Models", Journal of Time Series Analysis 38, 458-478.
    Click here to download the working paper version.
    There is also R code accompanying this paper.

  256. Blasques, Francisco, Paolo Gorgi, and Siem Jan Koopman (2017): "Accelerating GARCH and Score-Driven Models: Optimality, Estimation and Forecasting", Tinbergen Institute Discussion Paper, TI 2017-061/III.

  257. Blasques, Francisco, Andre Lucas, and Andries C. van Vlodrop (2017): "Finite Sample Optimality of Score-Driven Volatility Models", Tinbergen Institute Discussion Paper, TI 2017-111/III.

  258. Blazsek, Szabolcs, Alvaro Escribano, and Adrian Licht (2017): "Score-driven non linear multivariate dynamic location models," Universidad Carlos III de Madrid. Departamento de Economia, Working paper 17-14.

  259. Blazsek, Szabolcs, and Han-Chiang Ho (2017): "Markov regime-switching Beta-t-EGARCH", Applied Economics 49(47), 4793-4805.
    Click here to download the working paper version.

  260. Blazsek, Szabolcs, and Luis Antonio Monteros (2017): "Event-study analysis by using dynamic conditional score models", Applied Economics 49(45), 4530-4541.
    Click here to download the working paper version.

  261. Blazsek, Szabolcs, and Luis Antonio Monteros (2017): "Dynamic conditional score models of degrees of freedom: filtering with score-driven heavy tails", Applied Economics 49(53), 5426-5440.
    Click here to download the working paper version.

  262. Buccheri, Giuseppe, Giacomo Bormetti, Fulvio Corsi, and Fabrizio Lillo (2017): "A Score-driven Conditional Correlation Model for Noisy and Asynchronous Data: An Application to High-frequency Covariance Dynamics", Available at SSRN: https://ssrn.com/abstract=2912438.

  263. Buccheri, Giuseppe, and Fulvio Corsi (2017): "Hark the Shark: Realized Volatility Modelling with Measurement Errors and Nonlinear Dependencies", Available at SSRN: https://ssrn.com/abstract=3089929.

  264. Calvori, Francesco, Drew Creal, Siem Jan Koopman and Andre Lucas (2017): "Testing for Parameter Instability in Competing Modeling Frameworks", Journal of Financial Econometrics 15(2), 223-246.
    Click here to download the working paper version.

  265. Catania, Leopoldo, and Anna Gloria Billé (2017): Dynamic Spatial Autoregressive Models with Autoregressive and Heteroskedastic Disturbances", Journal of Applied Econometrics 32(6), 1178-1196.

  266. Catania, Leopoldo, and Stefano Grassi (2017): "Modelling Crypto-Currencies Financial Time-Series", SSRN working paper.

  267. Cerrato, Mario, John Crosby, Minjoo Kim, an Yang Zhao (2017): "Relation between higher order comoments and dependence structure of equity portfolio", Journal of Empirical Finance, 40, 101-120.

  268. Cerrato, Mario, John Crosby, Minjoo Kim, an Yang Zhao (2017): "The joint credit risk of UK global-systemically important banks", Journal of Futures Markets, 37(10), 964-988.

  269. Chen, Hua, Richard D. MacMinn and Tao Sun (2017): "Mortality Dependence and Longevity Bond Pricing: A Dynamic Factor Copula Mortality Model with the GAS Structure", Journal of Risk and Insurance, 84, 393-415.
    Click here to download the working paper version.

  270. Chen, Xi(2017): , PhD Dissertation, University of North Carolina at Chapel Hill.

  271. Chong, Terence Tai Leung, Yue Ding, and Tianxiao Pang (2017): "Extreme Risk Value and Dependence Structure of the China Securities Index 300," MPRA Paper 80556, University Library of Munich, Germany.

  272. Delle Monache, Davide and Ivan Petrella (2017): "Adaptive models and heavy tails with an application to inflation forecasting", International Journal of Forecasting 33(2), 482-501.
    Click here to download the working paper version.

  273. Harvey, Andrew, and Ryoko Ito (2017): "Modeling time series with zero observations", Nuffield College working paper, University of Oxford.

  274. Harvey, Andrew and Rutger-Jan Lange (2017): "Volatility Modeling with a Generalized t-distribution", Journal of Time Series Analysis 38, 175-190.
    Click here to download the working paper version.

  275. Kolev, Nikolai, and Jayme Pinto (2017): "Dependence Modeling in Energy Markets using Sibuya-type Copulas", International Journal of Statistics and Probability 6(3), 43-50.

  276. Koopman, Siem Jan, and Rutger Lit (2017): "Forecasting football match results in national league competitions using score-driven time series models", Tinbergen Institute Discussion Paper, TI 2017-062/III.

  277. Lin, Qi, and Haiyue Yu (2017): "Option Pricing with Generalized Autoregressive Score Models", Working paper, University of Hong Kong.

  278. Lucas, Andre, Bernd Schwaab and Xin Zhang (2017): "Measuring Credit Risk in a Large Banking System: Econometric Modeling and Empirics", Journal of Applied Econometrics 32(1), 171-191.
    Click here to download the working paper version.

  279. Massacci, Daniele (2017): "Tail Risk Dynamics in Stock Returns: Links to the Macroeconomy and Global Markets Connectedness", Management Science, 63(9), 3072-3089.

  280. Neves, César, Cristiano Fernandes, and Henrique Hoeltgebaum (2017): "Five different distributions for the Lee Carter model of mortality forecasting: A comparison using GAS models", Insurance: Mathematics and Economics 75, 48-57.

  281. Nystrup, Peter, Henrik Madsen, and Erik Lindstrom (2017): "Long Memory of Financial Time Series and Hidden Markov Models with Time-Varying Parameters", Journal of Forecasting 36(8), 989-1002.

  282. Odei Mensah, Jones, and Paul Alagidede (2017): "How are Africa's emerging stock markets related to advanced markets? Evidence from copulas", Economic Modelling 60, 1-10.

  283. Patton, Andrew J., Johanna F. Ziegel, and Rui Chen (2017): "Dynamic Semiparametric Models for Expected Shortfall (and Value-at-Risk)", Papers 1707.05108, arXiv.org. Updates found here.
    Computer code can be found in the code section.

  284. Saavedra, Raphael Augusto Proença Rosa (2017): "A study on the impact of El Nino southern oscillation on hydro power generation in Brazil", Master project, Departemento de Engenharia Electrica, PUC-Rio - Pontifical Catholic University of Rio de Janeiro.

  285. Silde, Erkki (2017): "The Econometrics of Financial Comovement", PhD Thesis, Tinbergen Institute and Vrije Universiteit Amsterdam.

  286. Wen, Xiaoqian, Elie Bouri, and David Roubaud (2017): "Can energy commodity futures add to the value of carbon assets?", Economic Modelling 62, 194-206.

  287. Wu, Weiou, Marco Chi Keung Lau, and Samuel A. Vigne (2017): "Modelling asymmetric conditional dependence between Shanghai and Hong Kong stock markets ", Research in International Business and Finance 42, 1137-1149.

  288. Zhao, Lin, and Sweder Van Wijnbergen (2017): "Decision Making in Incomplete Markets with Ambiguity: A case study of a gas field acquisition", Quantitative Finance 17(11), 1759-1782.
    Click here to download the working paper version.


  289. 2016

  290. Almeida, Carlos, Claudia Czado, and Hans Manner (2016): "Modeling high-dimensional time-varying dependence using dynamic D-vine models", Applied Stochastic Models in Business and Industry, 32(5), 621-638.

  291. Ardia, David, Kris Boudt, and Leopoldo Catania (2016): "Generalized Autoregressive Score Models in R: The GAS Package", Available at SSRN: https://ssrn.com/abstract=2825380.

  292. Ardia, David, Kris Boudt, and Leopoldo Catania (2016): "Value-at-Risk Prediction in R with the GAS Package", Available at SSRN: https://ssrn.com/abstract=2825380.

  293. Atskanov, I.A. (2016): "Application of GAS copulas for optimization of investment portfolio shares of Russian comopanies", Finance and Credit, 32, 25-37.

  294. Ayala, A., Blazsek, S., Cunado, J., and Gil-Alana, L. A. (2016): "Regime-switching purchasing power parity in Latin America: Monte Carlo unit root tests with dynamic conditional score", Applied Economics, 48(29), 2675-2696.

  295. Bartels, M., and F.A. Ziegelmann (2016): "Market risk forecasting for high dimensional portfolios via factor copulas with GAS dynamics", Insurance: Mathematics and Economics, 70, 66-79.

  296. Barunik, J., T. Krehlik, and L. Vacha (2016): "Modeling and forecasting exchange rate volatility in time-frequency domain", European Journal of Operational Research, 251(1), 329-340.

  297. Bernardi, Mauro, and Leopoldo Catania (2016): "Portfolio Optimisation Under Flexible Dynamic Dependence Modelling", Papers 1601.05199, arXiv.org.

  298. Blasques, Francisco, Jiangyu Ji, and Andre Lucas (2016): "Semiparametric score driven volatility models", Journal of Computational Statistics and Data Analysis 100, 58-69.

  299. Blasques, Francisco, Siem Jan Koopman, Katarzyna Lasak and Andre Lucas (2016): "In-Sample Confidence Bounds and Out-of-Sample Forecast Bands for Time-Varying Parameters in Observation Driven Models", International Journal of Forecasting, 32(3), 875-887.
    Click here to download the working paper version.

  300. Blasques, Francisco, Siem Jan Koopman, Andre Lucas and Julia Schaumburg (2016): "Spillover Dynamics for Systemic Risk Measurement using Spatial Financial Time Series Models", Journal of Econometrics, 195, 211-223.
    Click here to download the working paper version.

  301. Blazsek, S., Chavez, H., and Mendez, C. (2016): "Model stability and forecast performance of Beta-t-EGARCH", . Applied Economics Letters, 23 (17), 1219-1223.

  302. Blazsek, Szabolcs, and Alvaro Escribano (2016): "Score-driven dynamic patent count panel data models", Economics Letters, 149, 116-119.

  303. Blazsek, Szabolcs, and Vicente Mendoza (2016): "QARMA-Beta-t-EGARCH versus ARMA-GARCH: an application to S&P 500", Applied Economics, 48(12), 1119-1129.

  304. Boswijk, Peter, and Yang Liu (2016): "Score-driven variance-factor models", working paper available via research site Yang Liu.

  305. Caivano, Michele, Andrew Harvey and Alessandra Luati (2016): Robust time series models with trend and seasonal components, SERIEs, 7, 99-120.

  306. Catania, Leopoldo (2016): "Dynamic Adaptive Mixture Models with an Application to Volatility and Risk", eprint arXiv:1603.01308.

  307. Catania, Leopoldo, and Nima Nonejad (2016): "Density Forecasts and the Leverage Effect: Some Evidence from Observation and Parameter-Driven Volatility Models", eprint arXiv:1605.00230.

  308. Chen, Xi, Eric Ghysels, and Roland Telfeyan (2016): "Frailty Models for Commercial Mortgages", Journal of Fixed Income 26(2), 16-31.

  309. Delle Monache, Davide, Ivan Petrella and Fabrizio Venditti (2016): "Adaptive state space models with applications to the business cycle and financial stress", CEPR Discussion Paper 11599.

  310. Delle Monache, Davide, Ivan Petrella and Fabrizio Venditti (2016): "Common Faith or Parting Ways? A Time Varying Parameters Factor Analysis of Euro-Area Inflation", Book Series: Advances in Econometrics, 35, 539-565.

  311. Derbali, Abdelkader, and Aida Sy (2016): "The volatility of exchange rate between the US dollar and African emerging currencies: analysing by GAS-GARCH-Student-t model", International Journal of Critical Accounting 8(2), 132-143.

  312. Gao, Chun-Ting and Xiao-Hua Zhou (2016): "Forecasting VaR and ES using dynamic conditional score models and skew Student distribution", Economic Modelling, 53, 216-223.

  313. Gorgi, Paolo (2016): "Integer-valued autoregressive models with survival probability driven by a stochastic recurrence equation", Working paper.

  314. Hansen, Pieter Reinhard, Pawel Janus, and Siem Jan Koopman (2016): "Realized Wishart-GARCH: A Score-driven Multi-Asset Volatility Model", Tinbergen Institute Discussion Paper, TI 2016-061/III.

  315. Harvey, Andrew C. and Stephen Thiele (2016): "Testing against Changing Correlation", Journal of Empirical Finance, 38, 575-589.
    Click here to download the working paper version.

  316. Ito, Ryoko (2016a): "Asymptotic Theory for Beta-t-GARCH", Cambridge Working Papers in Economics CWPE1607.

  317. Ito, Ryoko (2016b): "Spline-DCS for Forecasting Trade Volume in High Frequency Financial Data", Cambridge Working Papers in Economics CWPE1606.

  318. Koopman, Siem Jan, Andre Lucas and Marcel Scharth (2016): "Predicting time-varying parameters with parameter-driven and observation-driven models", Review of Economics and Statistics, 98(1), 97-110.
    Click here to download the working paper version.

  319. Koopman, Siem Jan, Andre Lucas and Marcin Zamojski (2016): "Dynamic Term Structure Models with Score-Driven Time-Varying Parameters: Estimation and Forecasting", Vrije Universiteit Amsterdam, working paper.

  320. Lange, Rutger Jan, Andre Lucas, and Arjen H. Siegmann (2016): "Systemic Risk Signaling using Scores", in M. Billio, L. Pelizzon, R. Savona (eds.) Systemic Risk Tomography: Signals, Measurements and Transmission Channels. ISTE-Elsevier.
    See also the working paper version:
    Lange, Rutger-Jan, Andre Lucas, and Arjen H. Siegmann (2016): "Score-Driven Systemic Risk Signaling for European Sovereign Bond Yields and CDS Spreads", Tinbergen Institute Discussion Paper, TI 2016-064/IV.

  321. Lucas, Andre, Anne Opschoor (2016): "Fractional Integration and Fat Tails for Realized Covariance Kernels and Returns", TI Discussion paper 16-069/IV.

  322. Lucas, Andre, Anne Opschoor, Julia Schaumburg (2016): "Accounting for Missing Values in Score-Driven Time-Varying Parameter Models", Economics Letters, 148, 96-98.
    Click here to download the working paper version, Tinbergen Institute Discussion Paper, TI 2016-067/IV.

  323. Lucas, Andre, Bernd Schwaab, and Julia Schaumburg (2016): "Bank business models at zero interest rates", Tinbergen Institute Discussion Paper, TI 2016-066/IV.
    Click here to download the published version (JBES).
    Go here for example computer code (in Ox).

  324. Quaedvlieg, Rogier and Peter C. Schotman (2016): "Score-Driven Nelson Siegel: Hedging Long-Term Liabilities", Working Paper.

  325. Sucarrat, Genaro and Steffen Gronneberg (2016): "Models of Financial Return With Time-Varying Zero Probability", MPRA Paper 68931, University Library of Munich, Germany.

  326. Zhang, Xin and Bernd Schwaab (2016): "Tail risk in government bond markets and ECB unconventional policies", working paper.


  327. 2015

  328. Avdulaj, Krenar and Jozef Barunik (2015): "Are benefits from oil-stocks diversification gone? New evidence from a dynamic copula and high frequency data", Energy Economics, 51, 31-44.

  329. Ayala, A., Blazsek, S., and Gonzalez, R. B. (2015): "Default risk of sovereign debt in Central America", In: Nigel Finch (Ed.), Emerging Markets and Sovereign Risk (pp. 18-44). ISBN 978-1-137-45065-4, Palgrave Macmillan UK.

  330. Azam, Kazim, and Andre Lucas (2015): "Mixed Density based Copula Likelihood", Tinbergen Institute Discussion Paper, TI 15-003/III.

  331. Bernardi, Mauro and Leopoldo Catania (2015a): "Switching-GAS Copula Models for Systemic Risk Assessment", working papers series.

  332. Bernardi, Mauro and Leopoldo Catania (2015b): "Comparison of Value-at-Risk models: the MCS package", working papers series.

  333. Blasques, Francisco, Siem Jan Koopman and Andre Lucas (2015): "Information Theoretic Optimality of Observation Driven Time Series Models for Continuous Responses", Biometrika, 102(2), 325-343.
    Click here to download the working paper version.

  334. Blazsek, Szabolcs, and Marco Villatoro (2015): "Is Beta-t-EGARCH(1,1) superior to GARCH(1,1)?", Applied Economics, 47(17), 1764-1774.

  335. Caivano, Michele and Andrew Harvey (2015): "Time-series models with an EGB2 conditional distribution", Journal of Time Series Analysis, 35(6), 558-571.

  336. Creal, Drew, Siem Jan Koopman, Andre Lucas, and Marcin Zamojski (2015): "Generalized Autoregressive Method of Moments", Tinbergen Institute Discussion Paper, TI 2015-138/III.

  337. De Lira Salvatierra, Irving and Andrew J. Patton (2015): "Dynamic Copula Models and High Frequency Data", Journal of Empirical Finance, 30, 120-135.

  338. Harvey, Andrew and Rutger-Jan Lange (2015a): "Volatility Modeling with a Generalized t-distribution", Cambridge Working Papers in Economics, CWPE 1517.

  339. Harvey, Andrew and Rutger-Jan Lange (2015b): "Modeling the Interactions between Volatility and Returns", Cambridge Working Papers in Economics, CWPE 1518.

  340. Koopman, Siem Jan, Rutger Lit and Andre Lucas (2015): "Intraday Stock Price Dependence using Dynamic Discrete Copula Distributions", Tinbergen Institute Discussion Paper, TI 15-037/III.
    Click here to download the published version (JAEctr).

  341. Laurent, Sebastien, Christelle Lecourt and Franz C. Palm (2015): "Testing for jumps in conditionally Gaussian ARMA-GARCH models, a robust approach", Computational Statistics and Data Analysis, forthcoming.

  342. Lucas, Andre and Xin Zhang (2015): "Score Driven Exponentially Weighted Moving Average and Value-at-Risk Forecasting", International Journal of Forecasting, 32, 293-302.
    Click here to download the working paper version.


  343. 2014

  344. Andres, P. (2014): "Maximum likelihood estimates for positive valued dynamic score models; The Dysco package", Computational Statistics and Data Analysis, 76, 34-43.

  345. Blasques, Francisco (2014): GAS workshop slides, 2014 International Symposium on Forecasting, Rotterdam.

  346. Blasques, Francisco, Siem Jan Koopman and Andre Lucas (2014a): "Stationarity and Ergodicity of Univariate Generalized Autoregressive Score Processes", Electronic Journal of Statistics, 8, 1088-1112.

  347. Blasques, Francisco, Siem Jan Koopman and Andre Lucas (2014b): "Optimal Formulations for Nonlinear Autoregressive Processes", Tinbergen Institute Discussion Paper, TI 14-103/III.

  348. Blasques, Francisco, Siem Jan Koopman and Andre Lucas (2014c): "Maximum Likelihood Estimation for correctly Specified Generalized Autoregressive Score Models:Feedback Effects, Contraction Conditions and Asymptotic Properties", Tinbergen Institute Discussion Paper, TI 14-074/III.

  349. Blasques, Francisco, Siem Jan Koopman and Andre Lucas (2014e): "Maximum Likelihood Estimation for Generalized Autoregressive Score Models", Tinbergen Institute Discussion Paper, TI 14-029/III.

  350. Calvori, Francesco, Drew Creal, Siem Jan Koopman and Andre Lucas (2014): "Testing for Parameter Instability in Competing Modeling Frameworks", Duisenberg school of finance - Tinbergen Institute Discussion Paper, TI 14-010/IV/DSF71.

  351. Chen, Hua, Richard D. MacMinn and Tao Sun (2014): "Mortality Dependence and Longevity Bond Pricing: A Dynamic Factor Copula Mortality Model with the GAS Structure", working papers series.

  352. Creal, Drew D., Bernd Schwaab, Siem Jan Koopman and Andre Lucas (2014): "Observation Driven Mixed-Measurement Dynamic Factor Models with an Application to Credit Risk", Review of Economics and Statistics, 96(5), 898-915.
    You can also download the working paper version or download the online Appendix.
    Computer code : Ox code with macro data.

  353. Delle Monache, Davide and Ivan Petrella (2014): "Adaptive Models and Heavy Tails", Birkbeck working paper, BWPEF 1409.

  354. Harvey, Andrew C. and Alessandra Luati (2014): "Filtering with heavy tails", Journal of the American Statistical Association, 109, 1112-1122.

  355. Harvey, Andrew C. and Genaro Sucarrat (2014): "EGARCH models with fat tails, skewness and leverage", Computational Statistics and Data Analysis, 76, 320-339.

  356. Huang, Zhuo, Tianyi Wang and Xin Zhang (2014): "Generalized Autoregressive Score Model with Realized Measures of Volatility", working papers series.

  357. Janus, Pawel, Andre Lucas, Anne Opschoor, and Dick J. van Dijk (2014): "New HEAVY Models for Fat-Tailed Returns and Realized Covariance Kernels", Tinbergen Institute Discussion Paper, TI 14-073/III.
    Click here to download COMPUTER CODE.

  358. Janus, Pawel, Siem Jan Koopman and Andre Lucas (2014) "Long Memory Dynamics for Multivariate Dependence under Heavy Tails", Journal of Empirical Finance, 29, 187-206.
    Click here to download the working paper version.

  359. Jeyasreedharan, Nagaratnam, David E. Allen, and Joey W. Yang (2014): , Annals of Financial Economics 9(1), -.
    Click
    here to download the working paper version.

  360. Koopman, Siem Jan (2014): GAS workshop slides, 2014 International Symposium on Forecasting, Rotterdam.

  361. Lucas, Andre, Bernd Schwaab and Xin Zhang (2014): "Conditional euro area sovereign default risk", Journal of Business and Economic Statistics, 32(2), 271-284.
    You can also download the working paper version.

  362. Mao, Xiuping, Esther Ruiz and Helena Veiga (2014a): "Score Driven Asymmetric Stochastic Volatility Models", Universidad Carlos III de Madrid, Statistics and Econometrics Series 18, working paper 14-26.

  363. Massacci, D. (2014): "Tail Risk and the Macroeconomy", Einaudi Institute for Economics and Finance, working paper.


  364. 2013

  365. Avdulaj, Krenar and Jozef Barunik (2013): "Can we still benefit from international diversification? The case of the Czech and German stock markets", Cornell University Library.

  366. Caivano, Michele and Andrew Harvey (2013): "Two EGARCH models and one fat tail", CWPE 1326.

  367. Calvori, Francesco, Fabrizio Cipollini and Giampiero M. Gallo (2013): "High-frequency modeling for VWAP based trading strategies: a Generalized Autoregressive Score approach", SIS 2013 Statistical Conference, Advances in Latent Variables - Methods, Models and Applications, Brescia.

  368. Calvori, Francesco, Fabrizio Cipollini and Giampiero M. Gallo (2013): "Go with the Flow: A GAS Model For Predicting Intra-Daily Volume Shares", working papers series.

  369. Creal, Drew D., Siem Jan Koopman and Andre Lucas (2013, available as OPEN ACCESS): "Generalized Autoregressive Score Models with Applications", Journal of Applied Econometrics, 28(5), 777-795.
    Computer code : Matlab code and Ox code.

  370. Goncalves de Matos, Gilson (2013): "GAS Models Applied to Time Series of Streamflow and Wind.", dissertation from Pontificia Universidade Catolica do Rio de Janeiro.

  371. Harvey, Andrew C. (2013): Dynamic Models for Volatility and Heavy Tails: With Applications to Financial and Economic Time Series. Econometric Series Monographs. Cambridge University Press.

  372. Ito, Ryoko (2013): "Modeling Dynamic Diurnal Patterns in High Frequency Financial Data", Cambridge Working Papers in Economics CWPE1315.

  373. Oh, Dong Hwan and Andrew J. Patton (2013): "Time-Varying Systemic Risk: Evidence from a Dynamic Copula Model of CDS Spreads", Duke University Working Paper.

  374. Sucarrat, Genaro (2013): "betategarch: An R Package for the Simulation and Estimation of Beta-skew-t-EGARCH Models", BI Norwegian Business School Working Paper.


  375. 2012

  376. Andres, Philipp and Andrew C. Harvey (2012): "The Dynamic Location/Scale Model: with applications to intra-day financial data", Cambridge Working Papers in Economics CWPE 1240.

  377. Blasques, Francisco, Siem Jan Koopman and Andre Lucas (2012): "Stationarity and Ergodicity of Univariate Generalized Autoregressive Score Processes", Tinbergen Institute Discussion Paper 12-059/4.

  378. Boudt, Kris, Jon Danielsson, Siem Jan Koopman and Andre Lucas (2012): "Regime Switches in the Volatility and Correlation of Financial Institutions", National Bank of Belgium Working Paper No. 227.


  379. 2011

  380. Creal, Drew D., Siem Jan Koopman and Andre Lucas (2011): "A Dynamic Multivariate Heavy-Tailed Model for Time-Varying Volatilities and Correlations", Journal of Business and Economic Statistics, 29, 552-563.

  381. Creal, Drew D., Bernd Schwaab, Siem Jan Koopman and Andre Lucas (2011): "Observation Driven Mixed-Measurement Dynamic Factor Models with an Application to Credit Risk", Tinbergen Institute Discussion Paper 11-042/2/DSF16.
    Final version published in the Review of Economics and Statistics.

  382. Zhang, Xin, Drew D. Creal, Siem Jan Koopman and Andre Lucas (2011): "Modeling Dynamic Volatilities and Correlations under Skewness and Fat Tails", Tinbergen Institute Discussion Paper 11-078/2/DSF22.

  383. Zhang, Xin, Bernd Schwaab and, Andre Lucas (2011): "Conditional Probabilities and Contagion Measures for Euro Area Sovereign Default Risk", Tinbergen Institute Discussion Paper 11-176/2/DSF29.


  384. 2010

  385. Creal, Drew D., Siem Jan Koopman and Andre Lucas (2010): "A dynamic multivariate heavy-tailed model for time-varying volatilities and correlations", Tinbergen Institute Discussion Paper 10-032/2. You can also download the final version as published in the Journal of Business and Economic Statistics.

  386. Harvey, Andrew C. (2010): "Exponential Conditional Volatility Models", Cambridge Working Papers in Economics CWPE 1040.


  387. 2008

  388. Harvey, Andrew C. and Tirthankar Chakravarty (2008): "Beta-t-(E)GARCH", Discussion Paper University of Cambridge CWPE 08340.

  389. Creal, Drew D., Siem Jan Koopman and Andre Lucas (2008): "A General Framework for Observation Driven Time-Varying Parameter Models", Tinbergen Institute Discussion Paper 08-108/4.
    You can also download the final version as published version in the Journal of Applied Econometrics.