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Generalized Dynamic Factor Models and Volatilities. Recovering the Market Volatility Shocks

Citations

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Cited by:

  1. Christian Genest, 2024. "A Conversation With Marc Hallin," International Statistical Review, International Statistical Institute, vol. 92(2), pages 137-159, August.
  2. Carlos Cesar Trucios-Maza & João H. G Mazzeu & Luis K. Hotta & Pedro L. Valls Pereira & Marc Hallin, 2019. "On the robustness of the general dynamic factor model with infinite-dimensional space: identification, estimation, and forecasting," Working Papers ECARES 2019-32, ULB -- Universite Libre de Bruxelles.
  3. Zhou, Xuewei & Ouyang, Zisheng & Lu, Min & Ouyang, Zhongzhe, 2024. "Multilayer network analysis of idiosyncratic volatility connectedness: Evidence from China," Pacific-Basin Finance Journal, Elsevier, vol. 88(C).
  4. Matteo Barigozzi & Christian Brownlees, 2019. "NETS: Network estimation for time series," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(3), pages 347-364, April.
  5. Carlos Trucíos & João H. G. Mazzeu & Marc Hallin & Luiz K. Hotta & Pedro L. Valls Pereira & Mauricio Zevallos, 2022. "Forecasting Conditional Covariance Matrices in High-Dimensional Time Series: A General Dynamic Factor Approach," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 41(1), pages 40-52, December.
  6. Jianqing Fan & Ricardo Masini & Marcelo C. Medeiros, 2021. "Bridging factor and sparse models," Papers 2102.11341, arXiv.org, revised Sep 2022.
  7. Bouri, Elie & Gabauer, David & Gupta, Rangan & Tiwari, Aviral Kumar, 2021. "Volatility connectedness of major cryptocurrencies: The role of investor happiness," Journal of Behavioral and Experimental Finance, Elsevier, vol. 30(C).
  8. Ergemen, Yunus Emre & Rodríguez-Caballero, C. Vladimir, 2023. "Estimation of a dynamic multi-level factor model with possible long-range dependence," International Journal of Forecasting, Elsevier, vol. 39(1), pages 405-430.
  9. Liang Chen & Juan J. Dolado & Jesús Gonzalo, 2021. "Quantile Factor Models," Econometrica, Econometric Society, vol. 89(2), pages 875-910, March.
  10. Jianqing Fan & Donggyu Kim & Minseok Shin & Yazhen Wang, 2024. "Factor and Idiosyncratic VAR-Ito Volatility Models for Heavy-Tailed High-Frequency Financial Data," Working Papers 202415, University of California at Riverside, Department of Economics.
  11. Lucchetti, Riccardo & Venetis, Ioannis A., 2020. "A replication of "A quasi-maximum likelihood approach for large, approximate dynamic factor models" (Review of Economics and Statistics, 2012)," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 14, pages 1-14.
  12. Barigozzi, Matteo & Hallin, Marc, 2017. "Generalized dynamic factor models and volatilities: estimation and forecasting," Journal of Econometrics, Elsevier, vol. 201(2), pages 307-321.
  13. Demetrescu, Matei & Hacıoğlu Hoke, Sinem, 2019. "Predictive regressions under asymmetric loss: Factor augmentation and model selection," International Journal of Forecasting, Elsevier, vol. 35(1), pages 80-99.
  14. Kwangmin Jung & Donggyu Kim & Seunghyeon Yu, 2022. "Next generation models for portfolio risk management: An approach using financial big data," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 89(3), pages 765-787, September.
  15. Matteo Barigozzi & Luca Trapin, 2025. "Estimation of large approximate dynamic matrix factor models based on the EM algorithm and Kalman filtering," Papers 2502.04112, arXiv.org, revised May 2025.
  16. Matteo Barigozzi & Marc Hallin, 2017. "A network analysis of the volatility of high dimensional financial series," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(3), pages 581-605, April.
  17. Trucíos, Carlos & Mazzeu, João H.G. & Hotta, Luiz K. & Valls Pereira, Pedro L. & Hallin, Marc, 2021. "Robustness and the general dynamic factor model with infinite-dimensional space: Identification, estimation, and forecasting," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1520-1534.
  18. Matteo Barigozzi & Marc Hallin, 2023. "Dynamic Factor Models: a Genealogy," Papers 2310.17278, arXiv.org, revised Jan 2024.
  19. Jari Miettinen & Markus Matilainen & Klaus Nordhausen & Sara Taskinen, 2020. "Extracting Conditionally Heteroskedastic Components using Independent Component Analysis," Journal of Time Series Analysis, Wiley Blackwell, vol. 41(2), pages 293-311, March.
  20. Marc Hallin, 2022. "Manfred Deistler and the General Dynamic Factor Model Approach to the Analysis of High-Dimensional Time Series," Working Papers ECARES 2022-30, ULB -- Universite Libre de Bruxelles.
  21. Mehmet Balcilar & David Gabauer & Rangan Gupta & Christian Pierdzioch, 2021. "Uncertainty and Forecastability of Regional Output Growth in the United Kingdom: Evidence from Machine Learning," Working Papers 202111, University of Pretoria, Department of Economics.
  22. Gong, Xiao-Li & Liu, Xi-Hua & Xiong, Xiong & Zhang, Wei, 2020. "Research on China's financial systemic risk contagion under jump and heavy-tailed risk," International Review of Financial Analysis, Elsevier, vol. 72(C).
  23. Barigozzi, Matteo & Hallin, Marc, 2020. "Generalized dynamic factor models and volatilities: Consistency, rates, and prediction intervals," Journal of Econometrics, Elsevier, vol. 216(1), pages 4-34.
  24. Kim, Donggyu & Fan, Jianqing, 2019. "Factor GARCH-Itô models for high-frequency data with application to large volatility matrix prediction," Journal of Econometrics, Elsevier, vol. 208(2), pages 395-417.
  25. Ergemen, Yunus Emre, 2023. "Parametric estimation of long memory in factor models," Journal of Econometrics, Elsevier, vol. 235(2), pages 1483-1499.
  26. Ruofan Yu & Rong Chen & Han Xiao & Yuefeng Han, 2024. "Dynamic Matrix Factor Models for High Dimensional Time Series," Papers 2407.05624, arXiv.org.
  27. Matteo Barigozzi & Marc Hallin, 2015. "Networks, Dynamic Factors, and the Volatility Analysis of High-Dimensional Financial Series," Working Papers ECARES ECARES 2015-34, ULB -- Universite Libre de Bruxelles.
  28. Mehmet Balcilar & David Gabauer & Rangan Gupta & Christian Pierdzioch, 2022. "Uncertainty and forecastability of regional output growth in the UK: Evidence from machine learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(6), pages 1049-1064, September.
  29. Cipollini, Fabrizio & Gallo, Giampiero M., 2019. "Modeling Euro STOXX 50 volatility with common and market-specific components," Econometrics and Statistics, Elsevier, vol. 11(C), pages 22-42.
  30. Hallin, Marc & Trucíos, Carlos, 2023. "Forecasting value-at-risk and expected shortfall in large portfolios: A general dynamic factor model approach," Econometrics and Statistics, Elsevier, vol. 27(C), pages 1-15.
  31. Pourkhanali, Armin & Tafakori, Laleh & Bee, Marco, 2023. "Forecasting Value-at-Risk using functional volatility incorporating an exogenous effect," International Review of Financial Analysis, Elsevier, vol. 89(C).
  32. Shahin Tavakoli & Gilles Nisol & Marc Hallin, 2023. "Factor models for high‐dimensional functional time series II: Estimation and forecasting," Journal of Time Series Analysis, Wiley Blackwell, vol. 44(5-6), pages 601-621, September.
  33. Ma, Yan-Ran & Ji, Qiang & Wu, Fei & Pan, Jiaofeng, 2021. "Financialization, idiosyncratic information and commodity co-movements," Energy Economics, Elsevier, vol. 94(C).
  34. Trucíos, Carlos & Hotta, Luiz K. & Valls Pereira, Pedro L., 2019. "On the robustness of the principal volatility components," Journal of Empirical Finance, Elsevier, vol. 52(C), pages 201-219.
  35. Li, Y-N. & Chen, J. & Linton, O., 2021. "Estimation of Common Factors for Microstructure Noise and Efficient Price in a High-frequency Dual Factor Model," Cambridge Working Papers in Economics 2150, Faculty of Economics, University of Cambridge.
  36. Xinyu Song, 2019. "Large Volatility Matrix Prediction with High-Frequency Data," Papers 1907.01196, arXiv.org, revised Sep 2019.
  37. Ma, Yan-Ran & Zhang, Dayong & Ji, Qiang & Pan, Jiaofeng, 2019. "Spillovers between oil and stock returns in the US energy sector: Does idiosyncratic information matter?," Energy Economics, Elsevier, vol. 81(C), pages 536-544.
  38. Boudt, Kris & Cornilly, Dries & Verdonck, Tim, 2020. "Nearest comoment estimation with unobserved factors," Journal of Econometrics, Elsevier, vol. 217(2), pages 381-397.
  39. Osman Dou{g}an & Raffaele Mattera & Philipp Otto & Suleyman Tac{s}p{i}nar, 2024. "A Dynamic Spatiotemporal and Network ARCH Model with Common Factors," Papers 2410.16526, arXiv.org.
  40. Marc Hallin, 2022. "Manfred Deistler and the General-Dynamic-Factor-Model Approach to the Statistical Analysis of High-Dimensional Time Series," Econometrics, MDPI, vol. 10(4), pages 1-9, December.
  41. Karmous, Aida & Boubaker, Heni & Belkacem, Lotfi, 2019. "A dynamic factor model with stylized facts to forecast volatility for an optimal portfolio," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
  42. Liu, Yixuan & Kirch, Claudia & Lee, Jeong Eun & Meyer, Renate, 2024. "A nonparametrically corrected likelihood for Bayesian spectral analysis of multivariate time series," Computational Statistics & Data Analysis, Elsevier, vol. 199(C).
  43. Yunus Emre Ergemen, 2022. "Parametric Estimation of Long Memory in Factor Models," CREATES Research Papers 2022-10, Department of Economics and Business Economics, Aarhus University.
  44. Matteo Barigozzi & Marc Hallin & Stefano Soccorsi, 2017. "Identification of Global and National Shocks in International Financial Markets via General Dynamic Factor Models," Working Papers ECARES ECARES 2017-10, ULB -- Universite Libre de Bruxelles.
  45. Marc Hallin & Gilles Nisol & Shahin Tavakoli, 2023. "Factor models for high‐dimensional functional time series I: Representation results," Journal of Time Series Analysis, Wiley Blackwell, vol. 44(5-6), pages 578-600, September.
  46. Lübbers, Johannes & Posch, Peter N., 2016. "Commodities' common factor: An empirical assessment of the markets' drivers," Journal of Commodity Markets, Elsevier, vol. 4(1), pages 28-40.
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