Generalized Autoregressive Score models




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

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. Ayala, Astrid, and Szabolcs Blazsek (in press): "Equity market neutral hedge funds and the stock market: an application of score-driven copula models", Applied Economics.

  2. Ayala, Astrid, and Szabolcs Blazsek (in press): "Score-driven copula models for portfolios of two risky assets", European Journal of Finance.

  3. Bernardi, Mauro, and Leopoldo Catania (in press): "Switching generalized autoregressive score copula models with application to systemic risk", Journal of Applied Econometrics.

  4. Blazsek, S., Ho, H.-C., and Liu, S.-P. (in press): " Score-driven Markov-switching EGARCH models: an application to systematic risk analysis", Applied Economics.

  5. Blazsek, S., and Hernandez, H. (in press): " Analysis of electricity prices for Central American countries using dynamic conditional score models", Empirical Economics.

  6. Blazsek, S., and A. Licht (in press): " Robustness of score-driven location and scale models to extreme observations: An application to the Chinese stock market", Financial Statistical Journal.

  7. de Melo, Mariana Arozo B., Cristiano A.C. Fernandes, and Eduardo F.L. de Melo (in press): "Forecasting aggregate claims using score-driven time series models", Statistica Neerlandica.

  8. Fonseca, Rodney V., and Francisco Cribari-Neto (in press): "Bimodal Birnbaum–Saunders generalized autoregressive score model", Applied Statistics.

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

  10. Harvey, Andrew, and Rutger-Jan Lange (in press): "Modelling the Interactions between Volatility and Returns using EGARCH-M", Journal of Time Series Analysis.
    Click here to download the working paper version.

  11. Harvey, Andrew, and Ryoko Ito (in press): "Modeling time series when some observations are zero", Journal of Econometrics.
    Click here to download the working paper version.

  12. Koopman, Siem Jan, Rutger Lit, Andre Lucas, and Anne Opschoor (in press): "Intraday Stock Price Dependence using Dynamic Discrete Copula Distributions", Journal of Applied Econometrics.
    Click here to download the working paper version.

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

  14. Lucas, Andre, Anne Opschoor (in press): "Fractional Integration and Fat Tails for Realized Covariance Kernels and Returns", Journal of Financial Economicetrics.
    Click here to download the working paper version.

  15. Troster, Victor, Aviral Kumar Tiwarib, Muhammad Shahbazb, and Demian Nicolás Macedoc (in press): "Bitcoin returns and risk: A general GARCH and GAS analysis," Finance Research Letters.


  16. 2018

  17. 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.

  18. 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.

  19. 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.

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

  21. Buccheri, Giuseppe, Giacomo Bormetti, Fulvio Corsi, and Fabrizio Lillo (2018): "A General Class of Score-Driven Smoothers", SSRN Discussion Paper 3139666.

  22. 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.

  23. 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.

  24. 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.

  25. 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.

  26. 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
    Catania, Leopoldo; Grassi, Stefano; Ravazzolo, Francesco (2018): "Predicting the Volatility of Cryptocurrency Time–Series", Working paper Norwegian Business School.

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

  28. 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.

  29. 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.

  30. 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.

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

  32. 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.

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

  34. 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.

  35. 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.

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

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

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

  39. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3119538


    2017

  40. Barbosa de Alencar, David, Carolina de Mattos Affonso, Roberto Célio Limăo 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.

  41. 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.

  42. 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.

  43. 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.

  44. 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.

  45. 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.

  46. 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.

  47. 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.

  48. 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.

  49. 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.

  50. 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.

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

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

  53. 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.

  54. 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.

  55. 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.

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

  57. 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.

  58. 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.

  59. Eckernkemper, Tobias (2017): "Modeling Systemic Risk: Time-Varying Tail Dependence When Forecasting Marginal Expected Shortfall", Journal of Financial Econometrics 16(1), 63-117.

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

  61. 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.

  62. 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.

  63. 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.

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

  65. 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.

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

  67. 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.

  68. 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.

  69. 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.

  70. 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. onomics 75, 48-57.

  71. 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.

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

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

  74. 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.

  75. 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.


  76. 2016

  77. 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.

  78. 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.

  79. 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.

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

  81. 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.

  82. 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.

  83. 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.

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

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

  86. 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.

  87. 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.

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

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

  90. 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.

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

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

  93. Catania, Leopoldo (2016): "Dynamic Adaptive Mixture Models", eprint arXiv:1603.01308.

  94. 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.

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

  96. 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.

  97. 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.

  98. 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.

  99. 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.

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

  101. 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.

  102. 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.

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

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

  105. 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.

  106. 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.

  107. 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.

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

  109. 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.

  110. 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).

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

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

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


  114. 2015

  115. 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.

  116. 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.

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

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

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

  120. 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.

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

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

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

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

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

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

  127. 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).

  128. 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.

  129. 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.


  130. 2014

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

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

  133. 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.

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

  135. 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.

  136. 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.

  137. 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.

  138. 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.

  139. 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.

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

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

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

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

  144. 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.

  145. 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.

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

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

  148. 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.

  149. 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.

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


  151. 2013

  152. 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.

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

  154. 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.

  155. 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.

  156. 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.

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

  158. 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.

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

  160. 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.

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


  162. 2012

  163. 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.

  164. 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.

  165. 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.


  166. 2011

  167. 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.

  168. 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.

  169. 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.

  170. 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.


  171. 2010

  172. 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.

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


  174. 2008

  175. 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.

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