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Robust Ranking of Multivariate GARCH Models by Problem Dimension

Citations

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Cited by:

  1. Shang, Han Lin & Kearney, Fearghal, 2022. "Dynamic functional time-series forecasts of foreign exchange implied volatility surfaces," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1025-1049.
  2. Gargallo, Pilar & Lample, Luis & Miguel, Jesús A. & Salvador, Manuel, 2024. "Sequential management of energy and low-carbon portfolios," Research in International Business and Finance, Elsevier, vol. 69(C).
  3. Ralf Becker & Adam Clements & Robert O'Neill, 2018. "A Multivariate Kernel Approach to Forecasting the Variance Covariance of Stock Market Returns," Econometrics, MDPI, vol. 6(1), pages 1-27, February.
  4. Dean Fantazzini & Stephan Zimin, 2020. "A multivariate approach for the simultaneous modelling of market risk and credit risk for cryptocurrencies," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 47(1), pages 19-69, March.
  5. Amendola, Alessandra & Braione, Manuela & Candila, Vincenzo & Storti, Giuseppe, 2020. "A Model Confidence Set approach to the combination of multivariate volatility forecasts," International Journal of Forecasting, Elsevier, vol. 36(3), pages 873-891.
  6. Nikolaus Hautsch & Lada M. Kyj & Peter Malec, 2015. "Do High‐Frequency Data Improve High‐Dimensional Portfolio Allocations?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(2), pages 263-290, March.
  7. de Almeida, Daniel & Hotta, Luiz K. & Ruiz, Esther, 2018. "MGARCH models: Trade-off between feasibility and flexibility," International Journal of Forecasting, Elsevier, vol. 34(1), pages 45-63.
  8. Marc Hallin & Carlos Trucíos, 2020. "Forecasting Value-at-Risk and Expected Shortfall in Large Portfolios: a General Dynamic Factor Approach," Working Papers ECARES 2020-50, ULB -- Universite Libre de Bruxelles.
  9. Takayuki Morimoto & Yoshinori Kawasaki, 2017. "Forecasting Financial Market Volatility Using a Dynamic Topic Model," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 24(3), pages 149-167, September.
  10. Gian Piero Aielli & Massimiliano Caporin, 2015. "Dynamic Principal Components: a New Class of Multivariate GARCH Models," "Marco Fanno" Working Papers 0193, Dipartimento di Scienze Economiche "Marco Fanno".
  11. Li, Di & Wu, Zhige & Tang, Yixuan, 2024. "Do climate risks affect dirty–clean energy stock price dynamic correlations?," Energy Economics, Elsevier, vol. 136(C).
  12. Aboura, Sofiane & Chevallier, Julien, 2015. "A cross-volatility index for hedging the country risk," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 38(C), pages 25-41.
  13. 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.
  14. Karim M Abadir, 2023. "Explicit minimal representation of variance matrices, and its implication for dynamic volatility models," The Econometrics Journal, Royal Economic Society, vol. 26(1), pages 88-104.
  15. Carlos Trucíos & Mauricio Zevallos & Luiz K. Hotta & André A. P. Santos, 2019. "Covariance Prediction in Large Portfolio Allocation," Econometrics, MDPI, vol. 7(2), pages 1-24, May.
  16. 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.
  17. Massimiliano Caporin & Paolo Paruolo, 2015. "Proximity-Structured Multivariate Volatility Models," Econometric Reviews, Taylor & Francis Journals, vol. 34(5), pages 559-593, May.
  18. Audrino, Francesco, 2014. "Forecasting correlations during the late-2000s financial crisis: The short-run component, the long-run component, and structural breaks," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 43-60.
  19. Fresoli, Diego E. & Ruiz, Esther, 2016. "The uncertainty of conditional returns, volatilities and correlations in DCC models," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 170-185.
  20. Alexakis, Christos & Pappas, Vasileios, 2018. "Sectoral dynamics of financial contagion in Europe - The cases of the recent crises episodes," Economic Modelling, Elsevier, vol. 73(C), pages 222-239.
  21. Ashfaq, Saleha & Tang, Yong & Maqbool, Rashid, 2023. "Insights of energy and its trade networking impacts on sustainable economic development," Energy, Elsevier, vol. 265(C).
  22. repec:hum:wpaper:sfb649dp2013-014 is not listed on IDEAS
  23. Jacobs, Michael & Karagozoglu, Ahmet K., 2014. "On the characteristics of dynamic correlations between asset pairs," Research in International Business and Finance, Elsevier, vol. 32(C), pages 60-82.
  24. Adam Clements & Ayesha Scott & Annastiina Silvennoinen, 2019. "Volatility-dependent correlations: further evidence of when, where and how," Empirical Economics, Springer, vol. 57(2), pages 505-540, August.
  25. Timo Dimitriadis & Yannick Hoga, 2022. "Dynamic CoVaR Modeling," Papers 2206.14275, arXiv.org, revised Feb 2024.
  26. Sylvain Barde, 2015. "A fast algorithm for finding the confidence set of large collections of models," Studies in Economics 1519, School of Economics, University of Kent.
  27. Adam Clements & Ayesha Scott & Annastiina Silvennoinen, 2016. "Volatility Dependent Dynamic Equicorrelation," NCER Working Paper Series 111, National Centre for Econometric Research.
  28. Yujia Hu, 2023. "A Heuristic Approach to Forecasting and Selection of a Portfolio with Extra High Dimensions," Mathematics, MDPI, vol. 11(6), pages 1-21, March.
  29. Pappas, Vasileios & Ingham, Hilary & Izzeldin, Marwan & Steele, Gerry, 2016. "Will the crisis “tear us apart”? Evidence from the EU," International Review of Financial Analysis, Elsevier, vol. 46(C), pages 346-360.
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