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Maximum likelihood estimates for positive valued dynamic score models; The DySco package

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  • Andres, Philipp

Abstract

Recently, the Dynamic Conditional Score (DCS) or Generalized Autoregressive Score (GAS) time series models have attracted considerable attention. This motivates the need for a software package to estimate and evaluate these new models. A straightforward to operate program called the Dynamic Score (DySco) package is introduced for estimating models for positive variables, in which the location/scale evolves over time. Its capabilities are demonstrated using a financial application.

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  • Andres, Philipp, 2014. "Maximum likelihood estimates for positive valued dynamic score models; The DySco package," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 34-42.
    • Handle: RePEc:eee:csdana:v:76:y:2014:i:c:p:34-42
    DOI: 10.1016/j.csda.2013.11.004
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    Cited by:

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    2. Francisco Blasques & Siem Jan Koopman & André Lucas, 2014. "Information Theoretic Optimality of Observation Driven Time Series Models," Tinbergen Institute Discussion Papers 14-046/III, Tinbergen Institute.
    3. David Ardia & Kris Boudt & Leopoldo Catania, 2016. "Generalized Autoregressive Score Models in R: The GAS Package," Papers 1609.02354, arXiv.org.
    4. Blasques, Francisco & Ji, Jiangyu & Lucas, André, 2016. "Semiparametric score driven volatility models," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 58-69.
    5. Olusanya E. Olubusoye & OlaOluwa S. Yaya, 2016. "Time series analysis of volatility in the petroleum pricing markets: the persistence, asymmetry and jumps in the returns series," OPEC Energy Review, Organization of the Petroleum Exporting Countries, vol. 40(3), pages 235-262, September.
    6. Z. Sun & P. A. Hamill & Y. Li & Y. C. Yang & S. A. Vigne, 2019. "Did long-memory of liquidity signal the European sovereign debt crisis?," Annals of Operations Research, Springer, vol. 282(1), pages 355-377, November.
    7. Lin Zhao & Sweder van Wijnbergen, 2017. "Decision-making in incomplete markets with ambiguity—a case study of a gas field acquisition," Quantitative Finance, Taylor & Francis Journals, vol. 17(11), pages 1759-1782, November.
    8. Mauro Bernardi & Leopoldo Catania, 2016. "Comparison of Value-at-Risk models using the MCS approach," Computational Statistics, Springer, vol. 31(2), pages 579-608, June.
    9. Francisco (F.) Blasques & Paolo Gorgi & Siem Jan (S.J.) Koopman, 2017. "Accelerating GARCH and Score-Driven Models: Optimality, Estimation and Forecasting," Tinbergen Institute Discussion Papers 17-059/III, Tinbergen Institute.
    10. Mauro Bernardi & Leopoldo Catania, 2015. "Switching-GAS Copula Models With Application to Systemic Risk," Papers 1504.03733, arXiv.org, revised Jan 2016.
    11. Blasques, F. & Gorgi, P. & Koopman, S.J., 2019. "Accelerating score-driven time series models," Journal of Econometrics, Elsevier, vol. 212(2), pages 359-376.
    12. Andre Lucas & Bernd Schwaab & Xin Zhang, 2013. "Measuring Credit Risk in a Large Banking System: Econometric Modeling and Empirics," Tinbergen Institute Discussion Papers 13-063/IV/DSF56, Tinbergen Institute, revised 13 Oct 2014.
    13. Pawel Janus & André Lucas & Anne Opschoor & Dick J.C. van Dijk, 2014. "New HEAVY Models for Fat-Tailed Returns and Realized Covariance Kernels," Tinbergen Institute Discussion Papers 14-073/IV, Tinbergen Institute, revised 19 Aug 2015.

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