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A General Framework for Observation Driven Time-Varying Parameter Models

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Cited by:

  1. Janus, Paweł & Koopman, Siem Jan & Lucas, André, 2014. "Long memory dynamics for multivariate dependence under heavy tails," Journal of Empirical Finance, Elsevier, vol. 29(C), pages 187-206.
  2. Blazsek, Szabolcs & Escribano, Alvaro, 2023. "Score-driven threshold ice-age models: Benchmark models for long-run climate forecasts," Energy Economics, Elsevier, vol. 118(C).
  3. Davide Delle Monache & Ivan Petrella & Fabrizio Venditti, 2021. "Price Dividend Ratio and Long-Run Stock Returns: A Score-Driven State Space Model," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(4), pages 1054-1065, October.
  4. Neves, César & Fernandes, Cristiano & Hoeltgebaum, Henrique, 2017. "Five different distributions for the Lee–Carter model of mortality forecasting: A comparison using GAS models," Insurance: Mathematics and Economics, Elsevier, vol. 75(C), pages 48-57.
  5. Andres, P. & Harvey, A., 2012. "The Dyanamic Location/Scale Model: with applications to intra-day financial data," Cambridge Working Papers in Economics 1240, Faculty of Economics, University of Cambridge.
  6. Nguyen, Hoang & Virbickaitė, Audronė, 2023. "Modeling stock-oil co-dependence with Dynamic Stochastic MIDAS Copula models," Energy Economics, Elsevier, vol. 124(C).
  7. David E. Allen & Michael McAleer & Marcel Scharth, 2009. "Realized Volatility Risk," CIRJE F-Series CIRJE-F-693, CIRJE, Faculty of Economics, University of Tokyo.
  8. David E. Allen & Michael McAleer & Marcel Scharth, 2014. "Asymmetric Realized Volatility Risk," JRFM, MDPI, vol. 7(2), pages 1-30, June.
  9. Virbickaitė, Audronė & Nguyen, Hoang & Tran, Minh-Ngoc, 2023. "Bayesian predictive distributions of oil returns using mixed data sampling volatility models," Resources Policy, Elsevier, vol. 86(PA).
  10. Karim, Sitara & Lucey, Brian M. & Naeem, Muhammad Abubakr & Vigne, Samuel A., 2023. "The dark side of Bitcoin: Do Emerging Asian Islamic markets help subdue the ethical risk?," Emerging Markets Review, Elsevier, vol. 54(C).
  11. Francq, Christian & Zakoian, Jean-Michel, 2023. "Local Asymptotic Normality Of General Conditionally Heteroskedastic And Score-Driven Time-Series Models," Econometric Theory, Cambridge University Press, vol. 39(5), pages 1067-1092, October.
  12. Neil Shephard, 2013. "Martingale unobserved component models," Economics Series Working Papers 644, University of Oxford, Department of Economics.
  13. repec:wrk:wrkemf:29 is not listed on IDEAS
  14. Harvey, Andrew & Sucarrat, Genaro, 2014. "EGARCH models with fat tails, skewness and leverage," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 320-338.
  15. Syed Jawad Hussain Shahzad & Elie Bouri & Mobeen Ur Rehman & Muhammad Abubakr Naeem & Tareq Saeed, 2022. "Oil price risk exposure of BRIC stock markets and hedging effectiveness," Annals of Operations Research, Springer, vol. 313(1), pages 145-170, June.
  16. Francisco Blasques & Vladim'ir Hol'y & Petra Tomanov'a, 2018. "Zero-Inflated Autoregressive Conditional Duration Model for Discrete Trade Durations with Excessive Zeros," Papers 1812.07318, arXiv.org, revised May 2024.
  17. Marco Bazzi & Francisco Blasques & Siem Jan Koopman & Andre Lucas, 2017. "Time-Varying Transition Probabilities for Markov Regime Switching Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(3), pages 458-478, May.
  18. Francisco Blasques & Siem Jan Koopman & André Lucas, 2014. "Information Theoretic Optimality of Observation Driven Time Series Models," Tinbergen Institute Discussion Papers 14-046/III, Tinbergen Institute.
  19. Hendrych, R. & Cipra, T., 2016. "On conditional covariance modelling: An approach using state space models," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 304-317.
  20. Tsyplakov, Alexander, 2015. "Quasifiltering for time-series modeling," MPRA Paper 66453, University Library of Munich, Germany.
  21. Harvey,Andrew C., 2013. "Dynamic Models for Volatility and Heavy Tails," Cambridge Books, Cambridge University Press, number 9781107034723, September.
  22. Naeem, Muhammad Abubakr & Bouri, Elie & Costa, Mabel D. & Naifar, Nader & Shahzad, Syed Jawad Hussain, 2021. "Energy markets and green bonds: A tail dependence analysis with time-varying optimal copulas and portfolio implications," Resources Policy, Elsevier, vol. 74(C).
  23. Szabolcs Blazsek & Alvaro Escribano, 2022. "Robust Estimation and Forecasting of Climate Change Using Score-Driven Ice-Age Models," Econometrics, MDPI, vol. 10(1), pages 1-29, February.
  24. Zhang, Yi & Cheng, Chuntian & Cao, Rui & Li, Gang & Shen, Jianjian & Wu, Xinyu, 2021. "Multivariate probabilistic forecasting and its performance’s impacts on long-term dispatch of hydro-wind hybrid systems," Applied Energy, Elsevier, vol. 283(C).
  25. Xin Zhang & Drew Creal & Siem Jan Koopman & Andre Lucas, 2011. "Modeling Dynamic Volatilities and Correlations under Skewness and Fat Tails," Tinbergen Institute Discussion Papers 11-078/2/DSF22, Tinbergen Institute.
  26. Bahcivan, Hulusi & Karahan, Cenk C., 2022. "High frequency correlation dynamics and day-of-the-week effect: A score-driven approach in an emerging market stock exchange," International Review of Financial Analysis, Elsevier, vol. 80(C).
  27. Hans Manner & Olga Reznikova, 2012. "A Survey on Time-Varying Copulas: Specification, Simulations, and Application," Econometric Reviews, Taylor & Francis Journals, vol. 31(6), pages 654-687, November.
  28. Giuseppe Buccheri & Giacomo Bormetti & Fulvio Corsi & Fabrizio Lillo, 2018. "A Score-Driven Conditional Correlation Model for Noisy and Asynchronous Data: an Application to High-Frequency Covariance Dynamics," Papers 1803.04894, arXiv.org, revised Mar 2019.
  29. Shinya Fukui, 2020. "Business Cycle Spatial Synchronization: Measuring a Synchronization Parameter," Discussion Papers 2009, Graduate School of Economics, Kobe University.
  30. Jiang, Kunliang & Ye, Wuyi, 2022. "Does the asymmetric dependence volatility affect risk spillovers between the crude oil market and BRICS stock markets?," Economic Modelling, Elsevier, vol. 117(C).
  31. Giovanni Angelini & Giuseppe Cavaliere & Enzo D'Innocenzo & Luca De Angelis, 2022. "Time-Varying Poisson Autoregression," Papers 2207.11003, arXiv.org.
  32. Julia Kielmann & Hans Manner & Aleksey Min, 2021. "Stock Market Returns and Oil Price Shocks: A CoVaR Analysis based on Dynamic Vine Copula Models," Graz Economics Papers 2021-01, University of Graz, Department of Economics.
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