D. W. Andrews, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation, Econometrica, vol.59, issue.3, 1991.
DOI : 10.2307/2938229

D. W. Andrews, Estimation When a Parameter is on a Boundary, Econometrica, vol.67, issue.6, 1999.
DOI : 10.1111/1468-0262.00082
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.222.2801

D. W. Andrews, Testing When a Parameter is on the Boundary of the Maintained Hypothesis, Econometrica, vol.69, issue.3, p.683734, 2001.
DOI : 10.1111/1468-0262.00210

P. Billingsley, The Lindeberg-Levy theorem for martingales, Proceedings of the American Mathematical Society, vol.12, p.788792, 1961.
DOI : 10.2307/2034876

P. Billingsley, Probability and measure, 1995.

T. Bollerslev, Generalized autoregressive conditional heteroskedasticity, Journal of Econometrics, vol.31, issue.3, 1986.
DOI : 10.1016/0304-4076(86)90063-1
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.161.7380

T. Bollerslev, R. F. Engle, and J. M. Wooldridge, A Capital Asset Pricing Model with Time-Varying Covariances, Journal of Political Economy, vol.96, issue.1, 1988.
DOI : 10.1086/261527

T. Bollerslev, Modelling the coherence in short-run nominal exchange rates : a multivariate generalized ARCH model. The review of economics and statistics 495, 1990.

T. Bollerslev and J. M. Wooldridge, Quasi-maximum likelihood estimation and inference in dynamic models with time-varying covariances, Econometric reviews 11, 1992.
DOI : 10.1017/S0266466600004898

T. Bollerslev, Glossary to ARCH (GARCH)Volatility and Time Series Econometrics : Essays in Honor of, 2008.

M. B. Bouissou, J. Laont, and Q. H. Vuong, Tests of Noncausality under Markov Assumptions for Qualitative Panel Data, Econometrica, vol.54, issue.2, p.395414, 1986.
DOI : 10.2307/1913158

P. Bougerol and N. Picard, Stationarity of Garch processes and of some nonnegative time series, Journal of Econometrics, vol.52, issue.1-2, 1992.
DOI : 10.1016/0304-4076(92)90067-2

C. M. Hafner and A. Preminger, On asymptotic theory for multivariate GARCH models, Journal of Multivariate Analysis, vol.100, issue.9, 2009.
DOI : 10.1016/j.jmva.2009.03.011
URL : http://doi.org/10.1016/j.jmva.2009.03.011

P. Hall and Q. Yao, Inference in Arch and Garch Models with Heavy-Tailed Errors, Econometrica, vol.71, issue.1, p.285317, 2003.
DOI : 10.1111/1468-0262.00396
URL : http://eprints.lse.ac.uk/5875/1/Inference_in_ARCH_and_GARCH_models_with_heavy%2Dtailed_errors(LSERO).pdf

T. Hamadeh and J. Zakoïan, Asymptotic properties of LS and QML estimators for a class of nonlinear GARCH processes, Journal of Statistical Planning and Inference, vol.141, issue.1, p.488507, 2011.
DOI : 10.1016/j.jspi.2010.06.026

H. Han and D. Kristensen, Asymptotic Theory for the QMLE in GARCH-X Models With Stationary and Nonstationary Covariates, Journal of Business & Economic Statistics, vol.15, issue.3, p.416429, 2014.
DOI : 10.1007/BF00532676

N. Herrndorf, A functional central limit theorem for weakly dependent sequences of random variables. The Annals of Probability, 1984.

T. C. Koopmans and O. , The identication of structural characteristics, The Annals of Mathematical Statistics, vol.21, p.165181, 1950.

S. Laurent, C. Lecourt, and F. C. Palm, Testing for jumps in conditionally Gaussian ARMA???GARCH models, a robust approach, Computational Statistics & Data Analysis, vol.100, 2014.
DOI : 10.1016/j.csda.2014.05.015
URL : https://hal.archives-ouvertes.fr/hal-01447861

J. Mezrich and R. F. Engle, GARCH for groups, Risk, vol.9, p.3640, 1996.

D. B. Nelson, Conditional Heteroskedasticity in Asset Returns: A New Approach, Econometrica, vol.59, issue.2, 1991.
DOI : 10.2307/2938260

W. K. Newey and K. D. West, A simple, positive semi-denite, heteroskedasticity and autocorrelationconsistent covariance matrix, Econometrica, vol.55, p.703708, 1987.
DOI : 10.3386/t0055

T. Nijman and E. Sentana, Marginalization and contemporaneous aggregation in multivariate GARCH processes, Journal of Econometrics, vol.71, issue.1-2, p.7187, 1996.
DOI : 10.1016/0304-4076(94)01695-X

J. Pan, H. Wang, and H. Tong, Estimation and tests for power-transformed and threshold GARCH models, Journal of Econometrics, vol.142, issue.1, 2008.
DOI : 10.1016/j.jeconom.2007.06.004

R. S. Pedersen and A. Rahbek, Multivariate variance targeting in the BEKK- GARCH model, The Econometrics Journal, vol.17, p.2455, 2014.

R. S. Pedersen, Inference and Testing on the Boundary in Extended Constant Conditional Correlation GARCH Models Available at SSRN : http, 2015.

D. W. Andrews, Estimation When a Parameter is on a Boundary, Econometrica, vol.67, issue.6, 1999.
DOI : 10.1111/1468-0262.00082
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.222.2801

D. W. Andrews, Testing When a Parameter is on the Boundary of the Maintained Hypothesis, Econometrica, vol.69, issue.3, p.683734, 2001.
DOI : 10.1111/1468-0262.00210

I. Berkes, L. Horváth, and P. Kokoszka, GARCH processes: structure and estimation, Bernoulli, vol.9, issue.2, 2003.
DOI : 10.3150/bj/1068128975

P. Billingsley, The Lindeberg-Levy theorem for martingales, Proceedings of the American Mathematical Society, vol.12, p.788792, 1961.
DOI : 10.2307/2034876

P. Billingsley, Probability and measure, 1995.

T. Bollerslev, Glossary to ARCH (GARCH) Volatility and Time Series Econometrics : Essays in Honor of, 2008.

P. Bougerol and N. Picard, Stationarity of Garch processes and of some nonnegative time series, Journal of Econometrics, vol.52, issue.1-2, 1992.
DOI : 10.1016/0304-4076(92)90067-2

P. Bougerol and N. Picard, Strict Stationarity of Generalized Autoregressive Processes, The Annals of Probability, vol.20, issue.4, p.17141729, 1992.
DOI : 10.1214/aop/1176989526

A. Brandt, The stochastic equation Y n+1 = A n Y n + B n with stationary coecients, p.221254, 1986.

Z. Ding, C. Granger, and R. F. Engle, A long memory property of stock market returns and a new model, Journal of Empirical Finance, vol.1, p.83106, 1993.

R. F. Engle and A. J. Patton, What good is a volatility model, 2001.
DOI : 10.1088/1469-7688/1/2/305
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.196.8255

J. C. Escanciano, QUASI-MAXIMUM LIKELIHOOD ESTIMATION OF SEMI-STRONG GARCH MODELS, Econometric Theory, vol.45, issue.02, 2009.
DOI : 10.2307/2171862

T. J. Fisher and C. M. Gallagher, New Weighted Portmanteau Statistics for Time Series Goodness of Fit Testing, Journal of the American Statistical Association, vol.47, issue.1, p.777787, 2012.
DOI : 10.1080/01621459.2012.688465

C. Francq and J. Zakoïan, Maximum likelihood estimation of pure GARCH and ARMA-GARCH processes, Bernoulli, vol.10, issue.4, p.605637, 2004.
DOI : 10.3150/bj/1093265632

C. Francq and J. Zakoïan, Quasi-Maximum Likelihood Estimation in GARCH Processes when some coecients are equal to zero, Stochastic Processes and their Applications 117, 2007.
DOI : 10.1016/j.spa.2007.01.001
URL : http://doi.org/10.1016/j.spa.2007.01.001

C. Francq and J. Zakoian, Bartlett's formula for a general class of nonlinear processes, Journal of Time Series Analysis, vol.91, issue.4, p.449465, 2009.
DOI : 10.1111/j.1467-9892.2009.00623.x

C. Francq and J. Zakoian, Testing the nullity of GARCH coecients : correction of the standard tests and relative eciency comparisons, Journal of the American Statistical Association, vol.104, p.313324, 2009.

C. Francq and J. Zakoïan, GARCH Models : Structure, Statistical Inference and Financial Applications, 2010.
DOI : 10.1002/9780470670057

A. M. Fuertes, M. Izzeldin, and E. Kalotychou, On forecasting daily stock volatility : the role of intraday information and market conditions, International Journal of Forecasting, vol.25, p.259281, 2009.

L. R. Glosten, R. Jaganathan, and D. Runkle, On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks, The Journal of Finance, vol.25, issue.5, 1993.
DOI : 10.1111/j.1540-6261.1993.tb05128.x

S. Guo, S. Ling, and K. Zhu, Factor double autoregressive models with application to simultaneous causality testing, Journal of Statistical Planning and Inference, vol.148, issue.8294, p.80, 2014.
DOI : 10.1016/j.jspi.2013.12.007

D. Kristensen and A. Rahbek, ASYMPTOTICS OF THE QMLE FOR A CLASS OF ARCH(q) MODELS, Econometric Theory, vol.10, issue.05, p.946961, 2005.
DOI : 10.1016/S0167-7152(00)00138-3

S. Laurent, C. Lecourt, and F. C. Palm, Testing for jumps in conditionally Gaussian ARMA???GARCH models, a robust approach, Computational Statistics & Data Analysis, vol.100, p.383400, 2016.
DOI : 10.1016/j.csda.2014.05.015
URL : https://hal.archives-ouvertes.fr/hal-01447861

W. K. Li and T. K. Mak, ON THE SQUARED RESIDUAL AUTOCORRELATIONS IN NON-LINEAR TIME SERIES WITH CONDITIONAL HETEROSKEDASTICITY, Journal of Time Series Analysis, vol.5, issue.6, p.627636, 1994.
DOI : 10.1093/biomet/79.2.435

S. Ling, Self-weighted and local quasi-maximum likelihood estimators for ARMA-GARCH/IGARCH models, Journal of Econometrics, vol.140, issue.2, 2007.
DOI : 10.1016/j.jeconom.2006.07.016

D. B. Nelson, Conditional Heteroskedasticity in Asset Returns: A New Approach, Econometrica, vol.59, issue.2, 1991.
DOI : 10.2307/2938260

T. Nijman and E. Sentana, Marginalization and contemporaneous aggregation in multivariate GARCH processes, Journal of Econometrics, vol.71, issue.1-2, p.7187, 1996.
DOI : 10.1016/0304-4076(94)01695-X

J. Pan, H. Wang, and H. Tong, Estimation and tests for power-transformed and threshold GARCH models, Journal of Econometrics, vol.142, issue.1, 2008.
DOI : 10.1016/j.jeconom.2007.06.004

R. S. Pedersen, Inference and Testing on the Boundary in Extended Constant Conditional Correlation GARCH Models, Journal of Econometrics, vol.196, p.2336, 2017.

G. Sucarrat and A. Escribano, The Power Log-GARCH Model. Working document, Economic Series 10-13, 2010.

S. J. Taylor, Modelling Financial Time Series, 1986.
DOI : 10.1142/6578

O. Wintenberger, Continuous Invertibility and Stable QML Estimation of the EGARCH(1,1) Model, Scandinavian Journal of Statistics, vol.151, issue.2, 2013.
DOI : 10.1111/sjos.12038
URL : https://hal.archives-ouvertes.fr/hal-00751706

D. W. Andrews, Heteroskedasticity and autocorrelation consis-tent covariance matrix estimation, Econometrica, vol.59, 1991.
DOI : 10.2307/2938229

T. Bollerslev, R. F. Engle, and J. M. Wooldridge, A Capital Asset Pricing Model with Time-Varying Covariances, Journal of Political Economy, vol.96, issue.1, 1988.
DOI : 10.1086/261527

M. B. Bouissou, J. Laont, and Q. H. Vuong, Tests of Noncausality under Markov Assumptions for Qualitative Panel Data, Econometrica, vol.54, issue.2, p.414, 1986.
DOI : 10.2307/1913158

F. Boussama, F. Fuchs, and R. Stelzer, Stationarity and geometric ergodicity of BEKK multivariate GARCH models, Stochastic Processes and their Applications 121, 2011.
DOI : 10.1016/j.spa.2011.06.001
URL : http://arxiv.org/abs/1106.0165

C. Cakmakli and D. J. Van-dijk, Getting the Most Out of Macroeconomic Information for Predicting Stock Returns and Volatility, SSRN Electronic Journal, 2010.
DOI : 10.2139/ssrn.1713687

C. Christiansen, M. Schmeling, and A. Schrimpf, A comprehensive look at nancial volatility prediction by economic variables, Journal of Applied Econometrics, vol.27, p.956977, 2012.
DOI : 10.1002/jae.2298

F. Comte and O. Lieberman, Asymptotic theory for multivariate GARCH processes, Journal of Multivariate Analysis, vol.84, issue.1, p.6184, 2003.
DOI : 10.1016/S0047-259X(02)00009-X
URL : https://hal.archives-ouvertes.fr/hal-00170769

R. F. Engle and K. F. Kroner, Multivariate simultaneous generalized ARCH Econometric theory 11, 1995.
DOI : 10.1017/s0266466600009063
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.27.1240

R. Engle, Anticipating correlations : a new paradigm for risk management, 2009.
DOI : 10.1515/9781400830190

J. Florens and M. Mouchart, A Note on Noncausality, Econometrica, vol.50, issue.3, p.583591, 1982.
DOI : 10.2307/1912602

C. Francq and J. Zakoïan, Estimating linear representations of nonlinear processes, Journal of Statistical Planning and Inference, vol.68, issue.1, p.145165, 1998.
DOI : 10.1016/S0378-3758(97)00139-0

C. Francq and J. Zakoïan, Covariance matrix estimation for estimators of mixing weak ARMA models, Journal of Statistical Planning and Inference, vol.83, issue.2, p.369394, 2000.
DOI : 10.1016/S0378-3758(99)00109-3

C. Francq, R. Roy, and J. Zakoïan, Diagnostic Checking in ARMA Models With Uncorrelated Errors, Journal of the American Statistical Association, vol.100, issue.470, p.532544, 2005.
DOI : 10.1198/016214504000001510

C. Francq and J. Zakoïan, GARCH Models : Structure, Statistical Inference and Financial Applications, 2010.
DOI : 10.1002/9780470670057

C. Francq, L. Horvath, and J. Zakoïan, Merits and Drawbacks of Variance Targeting in GARCH Models, Journal of Financial Econometrics, vol.9, issue.4, p.619656, 2011.
DOI : 10.1093/jjfinec/nbr004

C. Francq, L. Horváth, and J. Zakoïan, Variance Targeting Estimation of Multivariate GARCH Models, Journal of Financial Econometrics, vol.14, issue.2, 2014.
DOI : 10.1093/jjfinec/nbu030

C. Francq and L. Q. Thieu, Qml inference for volatility models with covariates, 2015.

C. Francq and G. Sucarrat, Equation-by-Equation Estimation of a Multivariate Log- GARCH-X Model of Financial Returns, 2015.

N. Herrndorf, A functional central limit theorem for weakly dependent sequences of random variables. The Annals of Probability, 1984.

T. C. Koopmans and O. , The identication of structural characteristics, The Annals of Mathematical Statistics, vol.21, p.165181, 1950.

J. Mezrich and R. F. Engle, GARCH for groups, Risk, vol.9, p.3640, 1996.

W. K. Newey and K. D. West, A simple, positive semi-denite, heteroskedasticity and autocorrelationconsistent covariance matrix, Econometrica, vol.55, p.703708, 1987.
DOI : 10.3386/t0055

R. S. Pedersen and A. Rahbek, Multivariate variance targeting in the BEKK-GARCH model, The Econometrics Journal, vol.17, p.2455, 2014.

P. C. Phillips, Y. Sun, and S. Jin, Consistent HAC estimation and robust regression testing using sharp origin kernels with no truncation. Discussion paper, 2003.
DOI : 10.1016/j.jspi.2006.06.033
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.414.7075

A. Silvennoinen and T. Teräsvirta, Modeling Multivariate Autoregressive Conditional Heteroskedasticity with the Double Smooth Transition Conditional Correlation GARCH Model, Journal of Financial Econometrics, vol.7, issue.4, p.373411, 2009.
DOI : 10.1093/jjfinec/nbp013

T. Bollerslev, Modelling the coherence in short-run nominal exchange rates : a multivariate generalized ARCH model. The review of economics and statistics 495505, 1990.

T. Bollerslev and J. M. Wooldridge, Quasi-maximum likelihood estimation and inference in dynamic models with time-varying covariances, Econometric reviews 11, 1992.
DOI : 10.1017/S0266466600004898

R. F. Engle, V. K. Ng, and K. F. Kroner, Asset pricing with a factor-ARCH covariance structure : Empirical estimates for treasury bills, Journal of Econometrics, vol.45, p.213237, 1990.
DOI : 10.3386/t0065

R. F. Engle and K. Sheppar, Theoretical and empirical properties of dynamic conditional correlation multivariate GARCH, National Bureau of Economic Research, 2001.
DOI : 10.3386/w8554
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.196.3196

R. F. Engle, Dynamic Conditional Correlation, Journal of Business & Economic Statistics, vol.20, issue.3, pp.339-350, 2002.
DOI : 10.1198/073500102288618487

R. Engle, Anticipating correlations : a new paradigm for risk management, 2009.
DOI : 10.1515/9781400830190

C. Francq and J. Zakoïan, GARCH Models : Structure, Statistical Inference and Financial Applications, 2010.
DOI : 10.1002/9780470670057

C. Francq and L. Q. Thieu, Qml inference for volatility models with covariates, 2015.

C. Francq and G. Sucarrat, Equation-by-Equation Estimation of a Multivariate Log- GARCH-X Model of Financial Returns, Journal of the Royal Statistical Society : Series B, vol.78, issue.613635, 2016.

N. Herrndorf, A functional central limit theorem for weakly dependent sequences of random variables. The Annals of Probability, 1984.

R. S. Pedersen and A. Rahbek, Multivariate variance targeting in the BEKK-GARCH model, The Econometrics Journal, vol.17, p.2455, 2014.
DOI : 10.1111/ectj.12019

N. Shephard and K. Sheppard, Realizing the future : forecasting with high frequency based volatility (HEAVY) models, Journal of Applied Econometrics, vol.25, 2010.

. En and Z. Francq, ont developpé la méthode EbE et appliqué au modèle BEKK diagonal sans covariables. Cependant il n'a étudié que le comportement de l'estimateur EbE sous l'hypothèse que l' innovation est iid, 2016.

D. W. Andrews, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation, Econometrica, vol.59, issue.3, 1991.
DOI : 10.2307/2938229

D. W. Andrews, Estimation When a Parameter is on a Boundary, Econometrica, vol.67, issue.6, 1999.
DOI : 10.1111/1468-0262.00082
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.222.2801

D. W. Andrews, Testing When a Parameter is on the Boundary of the Maintained Hypothesis, Econometrica, vol.69, issue.3, p.683734, 2001.
DOI : 10.1111/1468-0262.00210

R. Baillie, Long memory processes and fractional integration in econometrics, Journal of Econometrics, vol.73, issue.1, p.559, 1996.
DOI : 10.1016/0304-4076(95)01732-1