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MGARCH models: tradeoff between feasibility and flexibility

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  • Almeida, Daniel de
  • Hotta, Luiz

Abstract

The parameters of popular multivariate GARCH (MGARCH) models are restricted so that their estimation is feasible in large systems and covariance stationarity and positive definiteness of conditional covariance matrices are guaranteed. These restrictions limit the dynamics that the models can represent, assuming, for example, that volatilities evolve in an univariate fashion, not being related neither among them nor with the correlations. This paper updates previous surveyson parametric MGARCH models focusing on their limitations to represent the dynamics observed in real systems of financial returns. The conclusions are illustrated using simulated data and a five-dimensional system of exchange rate returns.

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  • 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.
    • Handle: RePEc:cte:wsrepe:ws1516
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    1. Bollerslev, Tim & Engle, Robert F & Wooldridge, Jeffrey M, 1988. "A Capital Asset Pricing Model with Time-Varying Covariances," Journal of Political Economy, University of Chicago Press, vol. 96(1), pages 116-131, February.
    2. Christian M. Hafner, 2003. "Fourth Moment Structure of Multivariate GARCH Models," Journal of Financial Econometrics, Oxford University Press, vol. 1(1), pages 26-54.
    3. repec:hal:journl:peer-00815564 is not listed on IDEAS
    4. Bubák, Vít & Kocenda, Evzen & Zikes, Filip, 2011. "Volatility transmission in emerging European foreign exchange markets," Journal of Banking & Finance, Elsevier, vol. 35(11), pages 2829-2841, November.
    5. Annastiina Silvennoinen & Timo Teräsvirta, 2009. "Modeling Multivariate Autoregressive Conditional Heteroskedasticity with the Double Smooth Transition Conditional Correlation GARCH Model," Journal of Financial Econometrics, Oxford University Press, vol. 7(4), pages 373-411, Fall.
    6. Sébastien Laurent & Jeroen V. K. Rombouts & Francesco Violante, 2012. "On the forecasting accuracy of multivariate GARCH models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(6), pages 934-955, September.
    7. Nakatani, Tomoaki & Teräsvirta, Timo, 2008. "Positivity constraints on the conditional variances in the family of conditional correlation GARCH models," Finance Research Letters, Elsevier, vol. 5(2), pages 88-95, June.
    8. Aboura, Sofiane & Chevallier, Julien, 2015. "Volatility returns with vengeance: Financial markets vs. commodities," Research in International Business and Finance, Elsevier, vol. 33(C), pages 334-354.
    9. Cubadda, Gianluca & Guardabascio, Barbara & Hecq, Alain, 2017. "A vector heterogeneous autoregressive index model for realized volatility measures," International Journal of Forecasting, Elsevier, vol. 33(2), pages 337-344.
    10. Hafner, Christian M. & Herwartz, Helmut, 2006. "Volatility impulse responses for multivariate GARCH models: An exchange rate illustration," Journal of International Money and Finance, Elsevier, vol. 25(5), pages 719-740, August.
    11. Sbrana, Giacomo & Poloni, Federico, 2013. "A closed-form estimator for the multivariate GARCH(1,1) model," Journal of Multivariate Analysis, Elsevier, vol. 120(C), pages 152-162.
    12. Peter Boswijk, H. & van der Weide, Roy, 2011. "Method of moments estimation of GO-GARCH models," Journal of Econometrics, Elsevier, vol. 163(1), pages 118-126, July.
    13. Francis X. Diebold & Kamil Yilmaz, 2009. "Measuring Financial Asset Return and Volatility Spillovers, with Application to Global Equity Markets," Economic Journal, Royal Economic Society, vol. 119(534), pages 158-171, January.
    14. De Santis, Giorgio & Gerard, Bruno, 1997. "International Asset Pricing and Portfolio Diversification with Time-Varying Risk," Journal of Finance, American Finance Association, vol. 52(5), pages 1881-1912, December.
    15. Barndorff-Nielsen, Ole E. & Hansen, Peter Reinhard & Lunde, Asger & Shephard, Neil, 2011. "Multivariate realised kernels: Consistent positive semi-definite estimators of the covariation of equity prices with noise and non-synchronous trading," Journal of Econometrics, Elsevier, vol. 162(2), pages 149-169, June.
    16. Gardebroek, Cornelis & Hernandez, Manuel A., 2013. "Do energy prices stimulate food price volatility? Examining volatility transmission between US oil, ethanol and corn markets," Energy Economics, Elsevier, vol. 40(C), pages 119-129.
    17. Bauwens, Luc & Grigoryeva, Lyudmila & Ortega, Juan-Pablo, 2016. "Estimation and empirical performance of non-scalar dynamic conditional correlation models," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 17-36.
    18. Christian M. Hafner & Helmut Herwartz, 2008. "Testing for Causality in Variance Usinf Multivariate GARCH Models," Annals of Economics and Statistics, GENES, issue 89, pages 215-241.
    19. Massimiliano Caporin & Michael McAleer, 2011. "Thresholds, news impact surfaces and dynamic asymmetric multivariate GARCH," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 65(2), pages 125-163, May.
    20. Kroner, Kenneth F & Ng, Victor K, 1998. "Modeling Asymmetric Comovements of Asset Returns," The Review of Financial Studies, Society for Financial Studies, vol. 11(4), pages 817-844.
    21. Raffaella Giacomini & Halbert White, 2006. "Tests of Conditional Predictive Ability," Econometrica, Econometric Society, vol. 74(6), pages 1545-1578, November.
    22. 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.
    23. Rombouts, Jeroen & Stentoft, Lars & Violante, Franceso, 2014. "The value of multivariate model sophistication: An application to pricing Dow Jones Industrial Average options," International Journal of Forecasting, Elsevier, vol. 30(1), pages 78-98.
    24. 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.
    25. Bampinas Georgios & Panagiotidis Theodore, 2015. "On the relationship between oil and gold before and after financial crisis: linear, nonlinear and time-varying causality testing," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 19(5), pages 657-668, December.
    26. Ling, Shiqing & McAleer, Michael, 2003. "Asymptotic Theory For A Vector Arma-Garch Model," Econometric Theory, Cambridge University Press, vol. 19(2), pages 280-310, April.
    27. Boudt, Kris & Croux, Christophe, 2010. "Robust M-estimation of multivariate GARCH models," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2459-2469, November.
    28. Roy van der Weide, 2002. "GO-GARCH: a multivariate generalized orthogonal GARCH model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(5), pages 549-564.
    29. Torben G. Andersen & Tim Bollerslev & Peter Christoffersen & Francis X. Diebold, 2007. "Practical Volatility and Correlation Modeling for Financial Market Risk Management," NBER Chapters, in: The Risks of Financial Institutions, pages 513-544, National Bureau of Economic Research, Inc.
    30. Lee, Tae-Hwy & Long, Xiangdong, 2009. "Copula-based multivariate GARCH model with uncorrelated dependent errors," Journal of Econometrics, Elsevier, vol. 150(2), pages 207-218, June.
    31. BAUWENS, Luc & otranto, EDOARDO, 2013. "Modeling the dependence of conditional correlations on volatility," LIDAM Discussion Papers CORE 2013014, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    32. Engle, Robert F. & Kroner, Kenneth F., 1995. "Multivariate Simultaneous Generalized ARCH," Econometric Theory, Cambridge University Press, vol. 11(1), pages 122-150, February.
    33. Becker, R. & Clements, A.E. & Doolan, M.B. & Hurn, A.S., 2015. "Selecting volatility forecasting models for portfolio allocation purposes," International Journal of Forecasting, Elsevier, vol. 31(3), pages 849-861.
    34. Michael McAleer & Marcelo Medeiros, 2008. "Realized Volatility: A Review," Econometric Reviews, Taylor & Francis Journals, vol. 27(1-3), pages 10-45.
    35. Laurent, Sébastien & Rombouts, Jeroen V.K. & Violante, Francesco, 2013. "On loss functions and ranking forecasting performances of multivariate volatility models," Journal of Econometrics, Elsevier, vol. 173(1), pages 1-10.
    36. François Longin & Bruno Solnik, 2001. "Extreme Correlation of International Equity Markets," Journal of Finance, American Finance Association, vol. 56(2), pages 649-676, April.
    37. Otranto, Edoardo, 2010. "Identifying financial time series with similar dynamic conditional correlation," Computational Statistics & Data Analysis, Elsevier, vol. 54(1), pages 1-15, January.
    38. Rasmus S. Pedersen & Anders Rahbek, 2014. "Multivariate variance targeting in the BEKK–GARCH model," Econometrics Journal, Royal Economic Society, vol. 17(1), pages 24-55, February.
    39. Noureldin, Diaa & Shephard, Neil & Sheppard, Kevin, 2014. "Multivariate rotated ARCH models," Journal of Econometrics, Elsevier, vol. 179(1), pages 16-30.
    40. Weide, R. van der, 2002. "Generalized Orthogonal GARCH. A Multivariate GARCH model," CeNDEF Working Papers 02-02, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
    41. Massimiliano Caporin & Michael McAleer, 2012. "Do We Really Need Both Bekk And Dcc? A Tale Of Two Multivariate Garch Models," Journal of Economic Surveys, Wiley Blackwell, vol. 26(4), pages 736-751, September.
    42. Bauwens, L. & Hafner, C.M. & Rombouts, J.V.K., 2007. "Multivariate mixed normal conditional heteroskedasticity," Computational Statistics & Data Analysis, Elsevier, vol. 51(7), pages 3551-3566, April.
    43. Hecq Alain & Laurent Sébastien & Palm Franz C., 2016. "On the Univariate Representation of BEKK Models with Common Factors," Journal of Time Series Econometrics, De Gruyter, vol. 8(2), pages 91-113, July.
    44. Caporin, Massimiliano & McAleer, Michael, 2014. "Robust ranking of multivariate GARCH models by problem dimension," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 172-185.
    45. Poloni, Federico & Sbrana, Giacomo, 2014. "Feasible generalized least squares estimation of multivariate GARCH(1, 1) models," Journal of Multivariate Analysis, Elsevier, vol. 129(C), pages 151-159.
    46. Christian Francq & Lajos Horváth, 2011. "Merits and Drawbacks of Variance Targeting in GARCH Models," Journal of Financial Econometrics, Oxford University Press, vol. 9(4), pages 619-656.
    47. Tse, Y. K., 2000. "A test for constant correlations in a multivariate GARCH model," Journal of Econometrics, Elsevier, vol. 98(1), pages 107-127, September.
    48. Hansen, Peter Reinhard, 2005. "A Test for Superior Predictive Ability," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 365-380, October.
    49. Fulvio Corsi & Francesco Audrino, 2012. "Realized Covariance Tick-by-Tick in Presence of Rounded Time Stamps and General Microstructure Effects," Journal of Financial Econometrics, Oxford University Press, vol. 10(4), pages 591-616, September.
    50. Stelzer, Robert, 2008. "On The Relation Between The Vec And Bekk Multivariate Garch Models," Econometric Theory, Cambridge University Press, vol. 24(4), pages 1131-1136, August.
    51. Santos, André A.P. & Nogales, Francisco J. & Ruiz, Esther & Dijk, Dick Van, 2012. "Optimal portfolios with minimum capital requirements," Journal of Banking & Finance, Elsevier, vol. 36(7), pages 1928-1942.
    52. José Rangel & Robert Engle, 2012. "The Factor–Spline–GARCH Model for High and Low Frequency Correlations," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(1), pages 109-124.
    53. Francesco Audrino & Fabio Trojani, 2011. "A General Multivariate Threshold GARCH Model With Dynamic Conditional Correlations," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(1), pages 138-149, January.
    54. Hafner, Christian M. & Preminger, Arie, 2009. "On asymptotic theory for multivariate GARCH models," Journal of Multivariate Analysis, Elsevier, vol. 100(9), pages 2044-2054, October.
    55. Donald Lien & Y. K. Tse, 2002. "Some Recent Developments in Futures Hedging," Journal of Economic Surveys, Wiley Blackwell, vol. 16(3), pages 357-396, July.
    56. 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.
    57. Olivier Ledoit & Pedro Santa-Clara & Michael Wolf, 2003. "Flexible Multivariate GARCH Modeling with an Application to International Stock Markets," The Review of Economics and Statistics, MIT Press, vol. 85(3), pages 735-747, August.
    58. Burda Martin, 2015. "Constrained Hamiltonian Monte Carlo in BEKK GARCH with Targeting," Journal of Time Series Econometrics, De Gruyter, vol. 7(1), pages 95-113, January.
    59. Christophe Croux & Sébastien Laurent, 2011. "Outlyingness Weighted Covariation," Journal of Financial Econometrics, Oxford University Press, vol. 9(4), pages 657-684.
    60. Carey, Mark & Stulz, René M. (ed.), 2007. "The Risks of Financial Institutions," National Bureau of Economic Research Books, University of Chicago Press, number 9780226092850, September.
    61. Gian Piero Aielli, 2013. "Dynamic Conditional Correlation: On Properties and Estimation," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(3), pages 282-299, July.
    62. Luc Bauwens & Edoardo Otranto, 2016. "Modeling the Dependence of Conditional Correlations on Market Volatility," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(2), pages 254-268, April.
    63. Massimiliano Caporin & Michael McAleer, 2008. "Scalar BEKK and indirect DCC," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(6), pages 537-549.
    64. Fiorentini, Gabriele & Sentana, Enrique & Calzolari, Giorgio, 2003. "Maximum Likelihood Estimation and Inference in Multivariate Conditionally Heteroscedastic Dynamic Regression Models with Student t Innovations," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(4), pages 532-546, October.
    65. Palandri, Alessandro, 2009. "Sequential conditional correlations: Inference and evaluation," Journal of Econometrics, Elsevier, vol. 153(2), pages 122-132, December.
    66. Rossi, E. & Spazzini, F., 2010. "Model and distribution uncertainty in multivariate GARCH estimation: A Monte Carlo analysis," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2786-2800, November.
    67. repec:taf:jnlbes:v:30:y:2012:i:2:p:212-228 is not listed on IDEAS
    68. Luc Bauwens & Christian M. Hafner & Diane Pierret, 2013. "Multivariate Volatility Modeling Of Electricity Futures," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(5), pages 743-761, August.
    69. Bauwens, Luc & Laurent, Sebastien, 2005. "A New Class of Multivariate Skew Densities, With Application to Generalized Autoregressive Conditional Heteroscedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 346-354, July.
    70. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    71. Lorenzo Cappiello & Robert F. Engle & Kevin Sheppard, 2006. "Asymmetric Dynamics in the Correlations of Global Equity and Bond Returns," Journal of Financial Econometrics, Oxford University Press, vol. 4(4), pages 537-572.
    72. Colacito, Riccardo & Engle, Robert F. & Ghysels, Eric, 2011. "A component model for dynamic correlations," Journal of Econometrics, Elsevier, vol. 164(1), pages 45-59, September.
    73. Luc Bauwens & Sébastien Laurent & Jeroen V. K. Rombouts, 2006. "Multivariate GARCH models: a survey," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(1), pages 79-109, January.
    74. Avarucci, Marco & Beutner, Eric & Zaffaroni, Paolo, 2013. "On Moment Conditions For Quasi-Maximum Likelihood Estimation Of Multivariate Arch Models," Econometric Theory, Cambridge University Press, vol. 29(3), pages 545-566, June.
    75. Hafner, Christian M. & Preminger, Arie, 2009. "Asymptotic Theory For A Factor Garch Model," Econometric Theory, Cambridge University Press, vol. 25(2), pages 336-363, April.
    76. John Beirne & Guglielmo Maria Caporale & Marianne Schulze-Ghattas & Nicola Spagnolo, 2013. "Volatility Spillovers and Contagion from Mature to Emerging Stock Markets," Review of International Economics, Wiley Blackwell, vol. 21(5), pages 1060-1075, November.
    77. Cristina Amado & Timo Teräsvirta, 2014. "Conditional Correlation Models of Autoregressive Conditional Heteroscedasticity With Nonstationary GARCH Equations," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 32(1), pages 69-87, January.
    78. Bai, Jushan & Chen, Zhihong, 2008. "Testing multivariate distributions in GARCH models," Journal of Econometrics, Elsevier, vol. 143(1), pages 19-36, March.
    79. Comte, F. & Lieberman, O., 2003. "Asymptotic theory for multivariate GARCH processes," Journal of Multivariate Analysis, Elsevier, vol. 84(1), pages 61-84, January.
    80. 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.
    81. Boudt, Kris & Cornelissen, Jonathan & Croux, Christophe, 2012. "Jump robust daily covariance estimation by disentangling variance and correlation components," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 2993-3005.
    82. Christian Hafner & Philip Hans Franses, 2009. "A Generalized Dynamic Conditional Correlation Model: Simulation and Application to Many Assets," Econometric Reviews, Taylor & Francis Journals, vol. 28(6), pages 612-631.
    83. Hafner, Christian M. & Reznikova, Olga, 2012. "On the estimation of dynamic conditional correlation models," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3533-3545.
    84. Massimiliano Caporin & Paolo Paruolo, 2015. "Proximity-Structured Multivariate Volatility Models," Econometric Reviews, Taylor & Francis Journals, vol. 34(5), pages 559-593, May.
    85. S. Haug & C. Klüppelberg & A. Lindner & M. Zapp, 2007. "Method of moment estimation in the COGARCH(1,1) model," Econometrics Journal, Royal Economic Society, vol. 10(2), pages 320-341, July.
    86. Patton, Andrew J., 2011. "Volatility forecast comparison using imperfect volatility proxies," Journal of Econometrics, Elsevier, vol. 160(1), pages 246-256, January.
    87. Boussama, Farid & Fuchs, Florian & Stelzer, Robert, 2011. "Stationarity and geometric ergodicity of BEKK multivariate GARCH models," Stochastic Processes and their Applications, Elsevier, vol. 121(10), pages 2331-2360, October.
    88. repec:bla:jecsur:v:16:y:2002:i:3:p:357-96 is not listed on IDEAS
    89. Jeantheau, Thierry, 1998. "Strong Consistency Of Estimators For Multivariate Arch Models," Econometric Theory, Cambridge University Press, vol. 14(1), pages 70-86, February.
    90. Francq, Christian & Zakoïan, Jean-Michel, 2012. "Qml Estimation Of A Class Of Multivariate Asymmetric Garch Models," Econometric Theory, Cambridge University Press, vol. 28(1), pages 179-206, February.
    91. He, Changli & Teräsvirta, Timo, 2004. "An Extended Constant Conditional Correlation Garch Model And Its Fourth-Moment Structure," Econometric Theory, Cambridge University Press, vol. 20(5), pages 904-926, October.
    92. Kawakatsu, Hiroyuki, 2006. "Matrix exponential GARCH," Journal of Econometrics, Elsevier, vol. 134(1), pages 95-128, September.
    93. 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.
    94. Haixia, Wu & Shiping, Li, 2013. "Volatility spillovers in China’s crude oil, corn and fuel ethanol markets," Energy Policy, Elsevier, vol. 62(C), pages 878-886.
    95. Yi-Ting Chen, 2015. "On the Optimal Estimating Function Method for Conditional Correlation Models," Journal of Financial Econometrics, Oxford University Press, vol. 13(1), pages 83-125.
    96. Pedersen, Rasmus Søndergaard, 2017. "Inference and testing on the boundary in extended constant conditional correlation GARCH models," Journal of Econometrics, Elsevier, vol. 196(1), pages 23-36.
    97. Longin, Francois & Solnik, Bruno, 1995. "Is the correlation in international equity returns constant: 1960-1990?," Journal of International Money and Finance, Elsevier, vol. 14(1), pages 3-26, February.
    98. Conrad, Christian & Karanasos, Menelaos, 2010. "Negative Volatility Spillovers In The Unrestricted Eccc-Garch Model," Econometric Theory, Cambridge University Press, vol. 26(3), pages 838-862, June.
    99. Tim Bollerslev & Julia Litvinova & George Tauchen, 2006. "Leverage and Volatility Feedback Effects in High-Frequency Data," Journal of Financial Econometrics, Oxford University Press, vol. 4(3), pages 353-384.
    100. Engle, Robert F. & Ng, Victor K. & Rothschild, Michael, 1990. "Asset pricing with a factor-arch covariance structure : Empirical estimates for treasury bills," Journal of Econometrics, Elsevier, vol. 45(1-2), pages 213-237.
    101. Jooyoung Jeon & James W. Taylor, 2012. "Using Conditional Kernel Density Estimation for Wind Power Density Forecasting," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(497), pages 66-79, March.
    102. Alessandra Amendola & Giuseppe Storti, 2015. "Model Uncertainty and Forecast Combination in High‐Dimensional Multivariate Volatility Prediction," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(2), pages 83-91, March.
    103. 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.
    104. Annastiina Silvennoinen & Timo Ter�svirta, 2015. "Modeling Conditional Correlations of Asset Returns: A Smooth Transition Approach," Econometric Reviews, Taylor & Francis Journals, vol. 34(1-2), pages 174-197, February.
    105. Ramchand, Latha & Susmel, Raul, 1998. "Volatility and cross correlation across major stock markets," Journal of Empirical Finance, Elsevier, vol. 5(4), pages 397-416, October.
    106. Maria Kasch & Massimiliano Caporin, 2013. "Volatility Threshold Dynamic Conditional Correlations: An International Analysis," Journal of Financial Econometrics, Oxford University Press, vol. 11(4), pages 706-742, September.
    107. Audrino, Francesco, 2006. "The impact of general non-parametric volatility functions in multivariate GARCH models," Computational Statistics & Data Analysis, Elsevier, vol. 50(11), pages 3032-3052, July.
    108. André A. P. Santos & Francisco J. Nogales & Esther Ruiz, 2013. "Comparing Univariate and Multivariate Models to Forecast Portfolio Value-at-Risk," Journal of Financial Econometrics, Oxford University Press, vol. 11(2), pages 400-441, March.
    109. Christian Hafner & Helmut Herwartz, 2008. "Analytical quasi maximum likelihood inference in multivariate volatility models," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 67(2), pages 219-239, March.
    110. Christian Francq & Lajos Horváth & Jean-Michel Zakoïan, 2016. "Variance Targeting Estimation of Multivariate GARCH Models," Journal of Financial Econometrics, Oxford University Press, vol. 14(2), pages 353-382.
    111. Souček, Michael & Todorova, Neda, 2013. "Realized volatility transmission between crude oil and equity futures markets: A multivariate HAR approach," Energy Economics, Elsevier, vol. 40(C), pages 586-597.
    112. Bollerslev, Tim, 1990. "Modelling the Coherence in Short-run Nominal Exchange Rates: A Multivariate Generalized ARCH Model," The Review of Economics and Statistics, MIT Press, vol. 72(3), pages 498-505, August.
    113. Tomoaki Nakatani & Timo Terasvirta, 2009. "Testing for volatility interactions in the Constant Conditional Correlation GARCH model," Econometrics Journal, Royal Economic Society, vol. 12(1), pages 147-163, March.
    114. 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.
    115. Creal, Drew D. & Tsay, Ruey S., 2015. "High dimensional dynamic stochastic copula models," Journal of Econometrics, Elsevier, vol. 189(2), pages 335-345.
    116. Bjorn Hansson & Peter Hordahl, 1998. "Testing the conditional CAPM using multivariate GARCH-M," Applied Financial Economics, Taylor & Francis Journals, vol. 8(4), pages 377-388.
    117. Kristensen, Dennis & Linton, Oliver, 2004. "03.5.2. Consistent Standard Errors for Target Variance Approach to GARCH Estimation—Solution," Econometric Theory, Cambridge University Press, vol. 20(5), pages 990-993, October.
    118. Engle, Robert & Colacito, Riccardo, 2006. "Testing and Valuing Dynamic Correlations for Asset Allocation," Journal of Business & Economic Statistics, American Statistical Association, vol. 24, pages 238-253, April.
    119. Baillie, Richard T & Myers, Robert J, 1991. "Bivariate GARCH Estimation of the Optimal Commodity Futures Hedge," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 6(2), pages 109-124, April-Jun.
    120. Orazio P. Attanasio, 1991. "Risk, Time-Varying Second Moments and Market Efficiency," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(3), pages 479-494.
    121. Cavit Pakel & Neil Shephard & Kevin Sheppard & Robert F. Engle, 2021. "Fitting Vast Dimensional Time-Varying Covariance Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(3), pages 652-668, July.
    122. Chrétien, Stéphane & Ortega, Juan-Pablo, 2014. "Multivariate GARCH estimation via a Bregman-proximal trust-region method," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 210-236.
    123. 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.
    124. McAleer, Michael & Chan, Felix & Hoti, Suhejla & Lieberman, Offer, 2008. "Generalized Autoregressive Conditional Correlation," Econometric Theory, Cambridge University Press, vol. 24(6), pages 1554-1583, December.
    125. repec:dau:papers:123456789/13359 is not listed on IDEAS
    126. Okimoto, Tatsuyoshi, 2008. "New Evidence of Asymmetric Dependence Structures in International Equity Markets," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 43(3), pages 787-815, September.
    127. Massimiliano Caporin, 2007. "Variance (Non) Causality in Multivariate GARCH," Econometric Reviews, Taylor & Francis Journals, vol. 26(1), pages 1-24.
    128. So, Mike K.P. & Yeung, Cherry Y.T., 2014. "Vine-copula GARCH model with dynamic conditional dependence," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 655-671.
    129. C. Gourieroux, 2007. "Positivity Conditions for a Bivariate Autoregressive Volatility Specification," Journal of Financial Econometrics, Oxford University Press, vol. 5(4), pages 624-636, Fall.
    130. Mihaela ŞErban & Anthony Brockwell & John Lehoczky & Sanjay Srivastava, 2007. "Modelling the Dynamic Dependence Structure in Multivariate Financial Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 28(5), pages 763-782, September.
    131. Andrew J. Patton, 2006. "Modelling Asymmetric Exchange Rate Dependence," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 47(2), pages 527-556, May.
    132. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    133. Scherrer, Wolfgang & Ribarits, Eva, 2007. "On The Parametrization Of Multivariate Garch Models," Econometric Theory, Cambridge University Press, vol. 23(3), pages 464-484, June.
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    2. 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.
    3. Ariana Paola Cortés Ángel & Mustafa Hakan Eratalay, 2022. "Deep diving into the S&P Europe 350 index network and its reaction to COVID-19," Journal of Computational Social Science, Springer, vol. 5(2), pages 1343-1408, November.
    4. Georgios Gioldasis & Antonio Musolesi & Michel Simioni, 2020. "Model uncertainty, nonlinearities and out-of-sample comparison: evidence from international technology diffusion," Working Papers hal-02790523, HAL.
    5. Karanasos, Menelaos & Xu, Yongdeng & Yfanti, Stavroula, 2017. "Constrained QML Estimation for Multivariate Asymmetric MEM with Spillovers: The Practicality of Matrix Inequalities," Cardiff Economics Working Papers E2017/14, Cardiff University, Cardiff Business School, Economics Section.
    6. 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.
    7. Ana Alzate-Ortega & Natalia Garzón & Jesús Molina-Muñoz, 2024. "Volatility Spillovers in Emerging Markets: Oil Shocks, Energy, Stocks, and Gold," Energies, MDPI, vol. 17(2), pages 1-19, January.
    8. Cristina Amado & Annastiina Silvennoinen & Timo Ter¨asvirta, 2018. "Models with Multiplicative Decomposition of Conditional Variances and Correlations," NIPE Working Papers 07/2018, NIPE - Universidade do Minho.
    9. Shimada, Junji & Tsukuda, Yoshihiko & Miyakoshi, Tatsuyoshi, 2021. "Who is the center of local currency Asian government bond markets?," Japan and the World Economy, Elsevier, vol. 59(C).
    10. Bollerslev, Tim & Patton, Andrew J. & Quaedvlieg, Rogier, 2020. "Multivariate leverage effects and realized semicovariance GARCH models," Journal of Econometrics, Elsevier, vol. 217(2), pages 411-430.
    11. Vogler, Jan & Golosnoy, Vasyl, 2023. "Unrestricted maximum likelihood estimation of multivariate realized volatility models," European Journal of Operational Research, Elsevier, vol. 304(3), pages 1063-1074.
    12. Georgios Gioldasis & Antonio Musolesi & Michel Simioni, 2021. "Interactive R&D Spillovers: an estimation strategy based on forecasting-driven model selection," Working Papers hal-03224910, HAL.
    13. 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.
    14. Jingwei Pan, 0000. "Evaluating Correlation Forecasts Under Asymmetric Loss," Proceedings of Economics and Finance Conferences 11413234, International Institute of Social and Economic Sciences.
    15. Gioldasis, Georgios & Musolesi, Antonio & Simioni, Michel, 2023. "Interactive R&D spillovers: An estimation strategy based on forecasting-driven model selection," International Journal of Forecasting, Elsevier, vol. 39(1), pages 144-169.
    16. Ángeles Cebrián-Hernández & Enrique Jiménez-Rodríguez, 2021. "Modeling of the Bitcoin Volatility through Key Financial Environment Variables: An Application of Conditional Correlation MGARCH Models," Mathematics, MDPI, vol. 9(3), pages 1-16, January.
    17. 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.
    18. Georgios Gioldasis & Antonio Musolesi & Michel Simioni, 2021. "Interactive R&D Spillovers: An estimation strategy based on forecasting-driven model selection," SEEDS Working Papers 0621, SEEDS, Sustainability Environmental Economics and Dynamics Studies, revised Jun 2021.
    19. Moura, Guilherme V. & Santos, André A. P., 2019. "Comparing Forecasts of Extremely Large Conditional Covariance Matrices," DES - Working Papers. Statistics and Econometrics. WS 29291, Universidad Carlos III de Madrid. Departamento de Estadística.
    20. Văn, Lê & Bảo, Nguyễn Khắc Quốc, 2022. "The relationship between global stock and precious metals under Covid-19 and happiness perspectives," Resources Policy, Elsevier, vol. 77(C).
    21. Ariana Paola Cortés à ngel & Mustafa Hakan Eratalay, 2021. "Deedp Diving Into The S&P 350 Europe Index Network Ans Its Reaction To Covid-19," University of Tartu - Faculty of Economics and Business Administration Working Paper Series 134, Faculty of Economics and Business Administration, University of Tartu (Estonia).
    22. Fiszeder, Piotr & Fałdziński, Marcin, 2019. "Improving forecasts with the co-range dynamic conditional correlation model," Journal of Economic Dynamics and Control, Elsevier, vol. 108(C).
    23. Markus Vogl, 2022. "Quantitative modelling frontiers: a literature review on the evolution in financial and risk modelling after the financial crisis (2008–2019)," SN Business & Economics, Springer, vol. 2(12), pages 1-69, December.
    24. Georgios Gioldasis & Antonio Musolesi & Michel Simioni, 2020. "Model uncertainty, nonlinearities and out-of-sample comparison: evidence from international technology diffusion," SEEDS Working Papers 0120, SEEDS, Sustainability Environmental Economics and Dynamics Studies, revised Jan 2020.

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    Keywords

    BEKK;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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