On the estimation of dynamic conditional correlation models

Citations

Cited by:

1. Massimiliano Caporin & Michael McAleer, 2010. "Ranking Multivariate GARCH Models by Problem Dimension," "Marco Fanno" Working Papers 0124, Dipartimento di Scienze Economiche "Marco Fanno".
   • Caporin, M. & McAleer, M.J., 2010. "Ranking multivariate GARCH models by problem dimension," Econometric Institute Research Papers EI 2010-34, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
   • Massimiliano Caporin & Michael McAleer, 2010. "Ranking Multivariate GARCH Models by Problem Dimension," CIRJE F-Series CIRJE-F-742, CIRJE, Faculty of Economics, University of Tokyo.
   • Massimiliano Caporin & Michael McAleer, 2010. "Ranking Multivariate GARCH Models by Problem Dimension," Working Papers in Economics 10/34, University of Canterbury, Department of Economics and Finance.
   • Massimiliano Caporin & Michael McAleer, 2010. "Ranking Multivariate GARCH Models by Problem Dimension," CARF F-Series CARF-F-219, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
2. Caporin, M. & McAleer, M.J., 2011. "Ranking Multivariate GARCH Models by Problem Dimension: An Empirical Evaluation," Econometric Institute Research Papers EI 2011-18, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
   • Michael McAleer & Massimiliano Caporin, 2011. "Ranking Multivariate GARCH Models by Problem Dimension:An Empirical Evaluation," KIER Working Papers 778, Kyoto University, Institute of Economic Research.
   • Massimiliano Caporin & Michael McAleer, 2011. "Ranking Multivariate GARCH Models by Problem Dimension: An Empirical Evaluation," Working Papers in Economics 11/23, University of Canterbury, Department of Economics and Finance.
   • Massimiliano Caporin & Michael McAleer, 2011. "Ranking Multivariate GARCH Models by Problem Dimension: An Empirical Evaluation," Documentos de Trabajo del ICAE 2011-20, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
3. Roxana Halbleib & Valerie Voev, 2011. "Forecasting Covariance Matrices: A Mixed Frequency Approach," Working Papers ECARES ECARES 2011-002, ULB -- Universite Libre de Bruxelles.
   • Roxana Halbleib & Valeri Voev, 2012. "Forecasting Covariance Matrices: A Mixed Frequency Approach," Working Paper Series of the Department of Economics, University of Konstanz 2012-30, Department of Economics, University of Konstanz.
   • Roxana Halbleib & Valeri Voev, 2011. "Forecasting Covariance Matrices: A Mixed Frequency Approach," CREATES Research Papers 2011-03, Department of Economics and Business Economics, Aarhus University.
4. 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.
5. Ledoit, Olivier & Wolf, Michael, 2017. "Numerical implementation of the QuEST function," Computational Statistics & Data Analysis, Elsevier, vol. 115(C), pages 199-223.
   • Olivier Ledoit & Michael Wolf, 2016. "Numerical implementation of the QuEST function," ECON - Working Papers 215, Department of Economics - University of Zurich, revised Jan 2017.
6. 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.
   • Audrino, Francesco, 2011. "Forecasting correlations during the late-2000s financial crisis: short-run component, long-run component, and structural breaks," Economics Working Paper Series 1112, University of St. Gallen, School of Economics and Political Science.
7. Boudt, Kris & Daníelsson, Jón & Laurent, Sébastien, 2013. "Robust forecasting of dynamic conditional correlation GARCH models," International Journal of Forecasting, Elsevier, vol. 29(2), pages 244-257.
8. Elie Bouri & Naji Jalkh & Peter Molnár & David Roubaud, 2017. "Bitcoin for energy commodities before and after the December 2013 crash: diversifier, hedge or safe haven?," Applied Economics, Taylor & Francis Journals, vol. 49(50), pages 5063-5073, October.
   • Elie Bouri & Naji Jalkh & Peter Molnár & David Roubaud, 2017. "Bitcoin for energy commodities before and after the December 2013 crash: diversifier, hedge or safe haven?," Post-Print hal-02008553, HAL.
9. Roxana Halbleib & Valeri Voev, 2016. "Forecasting Covariance Matrices: A Mixed Approach," Journal of Financial Econometrics, Oxford University Press, vol. 14(2), pages 383-417.
10. Kurose, Yuta & Omori, Yasuhiro, 2016. "Dynamic equicorrelation stochastic volatility," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 795-813.
    • Yuta Kurose & Yasuhiro Omori, 2013. "Dynamic Equicorrelation Stochastic Volatility," CIRJE F-Series CIRJE-F-907, CIRJE, Faculty of Economics, University of Tokyo.
    • Yuta Kurose & Yasuhiro Omori, 2014. "Dynamic Equicorrelation Stochastic Volatility," CIRJE F-Series CIRJE-F-941, CIRJE, Faculty of Economics, University of Tokyo.
11. Jouchi Nakajima & Tsuyoshi Kunihama & Yasuhiro Omori, 2017. "Bayesian modeling of dynamic extreme values: extension of generalized extreme value distributions with latent stochastic processes," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(7), pages 1248-1268, May.
    • Jouchi Nakajima & Tsuyoshi Kunihama & Yasuhiro Omori, 2015. "Bayesian Modeling of Dynamic Extreme Values: Extension of Generalized Extreme Value Distributions with Latent Stochastic Processes ," CIRJE F-Series CIRJE-F-953, CIRJE, Faculty of Economics, University of Tokyo.
    • Jouchi Nakajima & Tsuyoshi Kunihama & Yasuhiro Omori, 2015. "Bayesian Modeling of Dynamic Extreme Values: Extension of Generalized Extreme Value Distributions with Latent Stochastic Processes ," CIRJE F-Series CIRJE-F-952, CIRJE, Faculty of Economics, University of Tokyo.
    • Jouchi Nakajima & Tsuyoshi Kunihama & Yasuhiro Omori, 2015. "Bayesian Modeling of Dynamic Extreme Values: Extension of Generalized Extreme Value Distributions with Latent Stochastic Processes ," CIRJE F-Series CIRJE-F-952, CIRJE, Faculty of Economics, University of Tokyo.
12. Sriananthakumar, Sivagowry & Narayan, Seema, 2015. "Are prolonged conflict and tension deterrents for stock market integration? The case of Sri Lanka," International Review of Economics & Finance, Elsevier, vol. 39(C), pages 504-520.
13. Sun, Xiaolin & Haralambides, Hercules & Liu, Hailong, 2019. "Dynamic spillover effects among derivative markets in tanker shipping," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 122(C), pages 384-409.
14. Ali, Fahad & Khurram, Muhammad Usman & Sensoy, Ahmet & Vo, Xuan Vinh, 2024. "Green cryptocurrencies and portfolio diversification in the era of greener paths," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).
15. Saker Sabkha & Christian de Peretti, 2018. "On the performances of Dynamic Conditional Correlation models in the Sovereign CDS market and the corresponding bond market," Working Papers hal-01710398, HAL.
16. 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.
    • Fresoli, Diego Eduardo, 2014. "The uncertainty of conditional returns, volatilities and correlations in DCC models," DES - Working Papers. Statistics and Econometrics. WS ws140202, Universidad Carlos III de Madrid. Departamento de Estadística.
17. Aielli, Gian Piero & Caporin, Massimiliano, 2014. "Variance clustering improved dynamic conditional correlation MGARCH estimators," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 556-576.
    • Gian Piero Aielli & Massimiliano Caporin, 2011. "Variance Clustering Improved Dynamic Conditional Correlation MGARCH Estimators," "Marco Fanno" Working Papers 0133, Dipartimento di Scienze Economiche "Marco Fanno".
18. Narayan, S. & Sriananthakumar, S. & Islam, S.Z., 2014. "Stock market integration of emerging Asian economies: Patterns and causes," Economic Modelling, Elsevier, vol. 39(C), pages 19-31.
19. M. Hakan Eratalay & Evgenii V. Vladimirov, 2020. "Mapping the stocks in MICEX: Who is central in the Moscow Stock Exchange?," Economics of Transition and Institutional Change, John Wiley & Sons, vol. 28(4), pages 581-620, October.
    • M. Hakan Eratalay & Evgenii Vladimirov, 2017. "Mapping the Stocks in MICEX: Who Is Central in Moscow Stock Exchange?," EUSP Department of Economics Working Paper Series 2017/01, European University at St. Petersburg, Department of Economics.
    • M. Hakan Eratalay; Evgenii V. Vladimirov, 2018. "Mapping The Stocks In Micex: Who Is Central To The Moscow Stock Exchange?," University of Tartu - Faculty of Economics and Business Administration Working Paper Series 111, Faculty of Economics and Business Administration, University of Tartu (Estonia).
20. BAUWENS, Luc & HAFNER, Christian & LAURENT, Sébastien, 2011. "Volatility models," LIDAM Discussion Papers CORE 2011058, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    • Bauwens, L. & Hafner, C. & Laurent, S., 2012. "Volatility Models," LIDAM Reprints ISBA 2012028, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    • Bauwens, L. & Hafner C. & Laurent, S., 2011. "Volatility Models," LIDAM Discussion Papers ISBA 2011044, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
21. Baumohl, Eduard & Lyocsa, Stefan, 2013. "Volatility and dynamic conditional correlations of European emerging stock markets," MPRA Paper 49898, University Library of Munich, Germany.
22. Saker Sabkha & Christian de Peretti, 2022. "On the performances of Dynamic Conditional Correlation models in the Sovereign CDS market and the corresponding bond market," Post-Print hal-01710398, HAL.
23. Staer, Arsenio & Sottile, Pedro, 2018. "Equivalent volume and comovement," The Quarterly Review of Economics and Finance, Elsevier, vol. 68(C), pages 143-157.
24. Christian Francq & Jean-Michel Zakoïan, 2016. "Estimating multivariate volatility models equation by equation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(3), pages 613-635, June.
25. Kočenda, Evžen & Moravcová, Michala, 2019. "Exchange rate comovements, hedging and volatility spillovers on new EU forex markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 58(C), pages 42-64.
    • Evzen Kocenda & Michala Moravcova, 2017. "Exchange Rate Co-movements, Hedging and Volatility Spillovers in New EU Forex Markets," Working Papers IES 2017/27, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Nov 2017.
26. João F. Caldeira & Guilherme V. Moura & Francisco J. Nogales & André A. P. Santos, 2017. "Combining Multivariate Volatility Forecasts: An Economic-Based Approach," Journal of Financial Econometrics, Oxford University Press, vol. 15(2), pages 247-285.
27. Ali, Fahad & Bouri, Elie & Naifar, Nader & Shahzad, Syed Jawad Hussain & AlAhmad, Mohammad, 2022. "An examination of whether gold-backed Islamic cryptocurrencies are safe havens for international Islamic equity markets," Research in International Business and Finance, Elsevier, vol. 63(C).
28. Stavrakoudis, Athanassios & Panagiotou, Dimitrios, 2016. "Price dependence between coffee qualities: a copula model to evaluate asymmetric responses," MPRA Paper 75994, University Library of Munich, Germany.
29. Eduard Baum??hl & ??tefan Ly??csa, 2014. "How smooth is the stock market integration of CEE-3?," William Davidson Institute Working Papers Series wp1079, William Davidson Institute at the University of Michigan.
30. Paola Leone & Pasqualina Porretta & Luca Riccetti, 2021. "European Significant Bank Stock Market Volatility: Is there a Bail-In Effect?," International Journal of Business and Management, Canadian Center of Science and Education, vol. 14(5), pages 1-32, July.
31. Robert F. Engle & Olivier Ledoit & Michael Wolf, 2019. "Large Dynamic Covariance Matrices," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 37(2), pages 363-375, April.
    • Robert F. Engle & Olivier Ledoit & Michael Wolf, 2016. "Large dynamic covariance matrices," ECON - Working Papers 231, Department of Economics - University of Zurich, revised Apr 2017.
32. 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.
    • Trucíos Maza, Carlos César & Hotta, Luiz Koodi & Pereira, Pedro L. Valls, 2018. "On the robustness of the principal volatility components," Textos para discussão 474, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
33. 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.
    • Almeida, Daniel de & Hotta, Luiz, 2015. "MGARCH models: tradeoff between feasibility and flexibility," DES - Working Papers. Statistics and Econometrics. WS ws1516, Universidad Carlos III de Madrid. Departamento de Estadística.
34. Carnero M. Angeles & Eratalay M. Hakan, 2014. "Estimating VAR-MGARCH models in multiple steps," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 18(3), pages 339-365, May.
    • M. Angeles Carnero Fernández & M. Hakan Eratalay, 2012. "Estimating VAR-MGARCH models in multiple steps," Working Papers. Serie AD 2012-10, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
35. 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.
36. Pedersen, Rasmus Søndergaard, 2016. "Targeting Estimation Of Ccc-Garch Models With Infinite Fourth Moments," Econometric Theory, Cambridge University Press, vol. 32(2), pages 498-531, April.
    • Rasmus Søndergaard Pedersen, 2014. "Targeting estimation of CCC-Garch models with infinite fourth moments," Discussion Papers 14-04, University of Copenhagen. Department of Economics.
37. Michael Curran & Patrick O'Sullivan & Ryan Zalla, 2020. "Can Volatility Solve the Naive Portfolio Puzzle?," Papers 2005.03204, arXiv.org, revised Feb 2022.
    • Michael Curran & Patrick O'Sullivan & Ryan Zalla, 2022. "Can Volatility Solve the Naive Portfolio Puzzle?," Villanova School of Business Department of Economics and Statistics Working Paper Series 52, Villanova School of Business Department of Economics and Statistics.
38. Bouri, Elie & Molnár, Peter & Azzi, Georges & Roubaud, David & Hagfors, Lars Ivar, 2017. "On the hedge and safe haven properties of Bitcoin: Is it really more than a diversifier?," Finance Research Letters, Elsevier, vol. 20(C), pages 192-198.
    • Elie Bouri & Peter Molnár & Georges Azzi & David Roubaud & Lars Ivar Hagfors, 2017. "On the hedge and safe haven properties of Bitcoin: Is it really more than a diversifier?," Post-Print hal-02000697, HAL.
39. Baumöhl, Eduard & Lyócsa, Štefan, 2014. "Volatility and dynamic conditional correlations of worldwide emerging and frontier markets," Economic Modelling, Elsevier, vol. 38(C), pages 175-183.
40. Aslanidis, Nektarios & Casas, Isabel, 2013. "Nonparametric correlation models for portfolio allocation," Journal of Banking & Finance, Elsevier, vol. 37(7), pages 2268-2283.
41. 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.
42. Santos, André A.P. & Moura, Guilherme V., 2014. "Dynamic factor multivariate GARCH model," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 606-617.
43. Heaney, Richard & Sriananthakumar, Sivagowry, 2012. "Time-varying correlation between stock market returns and real estate returns," Journal of Empirical Finance, Elsevier, vol. 19(4), pages 583-594.
44. Kei Nakagawa & Mitsuyoshi Imamura & Kenichi Yoshida, 2018. "Risk-Based Portfolios with Large Dynamic Covariance Matrices," IJFS, MDPI, vol. 6(2), pages 1-14, May.
45. Santos, André Alves Portela & Ferreira, Alexandre R., 2017. "On the choice of covariance specifications for portfolio selection problems," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 37(1), May.
    • R. Ferreira, Alexandre & A. P. Santos, Andre, 2016. "On the choice of covariance specifications for portfolio selection problems," MPRA Paper 73259, University Library of Munich, Germany.
46. Haishu Qiao & Yue Xia & Ying Li, 2016. "Can Network Linkage Effects Determine Return? Evidence from Chinese Stock Market," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-25, June.