High-dimensional penalized arch processes

References

• Aielli, G. P. (2013). Dynamic conditional correlation: on properties and estimation. Journal of Business & Economic Statistics 31:282–299.
   Web of Science ®Google Scholar
• Alexander, C. (2001). Orthogonal GARCH. In: Alexander, C., ed., Mastering Risk, London: Financial Times-Prentice Hall, pp. 21–28.
   Google Scholar
• Asai, M., McAleer, M., Yu, J. (2006). Multivariate stochastic volatility: a review. Econometric Reviews 25(2–3):145–175. doi:10.1080/07474930600713564
   Web of Science ®Google Scholar
• Bauwens, L., Laurent, S., Rombouts, J. V. K. (2006). Multivariate GARCH models: a survey. Journal of Applied Econometrics 21(1):79–109. doi:10.1002/jae.842
   Web of Science ®Google Scholar
• Billio, M., Caporin, M., Gobbo, M. (2006). Flexible dynamic conditional correlation multivariate GARCH models for asset allocation. Applied Financial Economics Letters 2(2):123–130. doi:10.1080/17446540500428843
   Google Scholar
• Bollerslev, T., Engle, R. F., Wooldridge, J. M. (1988). A capital asset pricing model with time-varying covariances. Journal of Political Economy 96(1):116–131.
   Web of Science ®Google Scholar
• Boussama, F. (2006). Ergodicité des chaînes de Markov à valeurs dans une variété algébrique: application aux modèles GARCH multivariés. Comptes Rendus de l’Académie des Sciences Paris 343:275–278.
   Google Scholar
• Boussama, F., Fuchs, F., Stelzer, R. (2011). Stationarity and geometric ergodicity of BEKK multivariate GARCH models. Stochastic Processes and Their Applications 121(10):2331–2360. doi:10.1016/j.spa.2011.06.001
   Web of Science ®Google Scholar
• Caner, M., Han, X. (2014). Selecting the correct number of factors in approximate factor models: the large panel case with group bridge estimators. Journal of Business & Economic Statistics 32(3):359–374.
   Web of Science ®Google Scholar
• Caner, M., Kock, A. B. (2018). Asymptotically honest confidence regions for high dimensional parameters by the desparsified conservative Lasso. Journal of Econometrics 203(1):143–168. doi:10.1016/j.jeconom.2017.11.005
   Web of Science ®Google Scholar
• Darolles, S., Francq, C., Laurent, S. (2018). Asymptotic of Cholesky-GARCH models and time-varying conditional beta. Journal of Econometrics 204(2):223–247. doi:10.1016/j.jeconom.2018.02.003
   Web of Science ®Google Scholar
• Diebold, F. X., Mariano, R. S. (1995). Comparing predictive accuracy. Journal of Business & Economic Statistics 13:253–263.
   Web of Science ®Google Scholar
• Engle, R. (2002). Dynamic conditional correlation. Journal of Business & Economic Statistics 20:339–350.
   Web of Science ®Google Scholar
• Engle, R., Colacito, R. (2006). Testing and valuing dynamic correlations for asset allocation. Journal of Business & Economic Statistics 24:238–253.
   Web of Science ®Google Scholar
• Engle, R. F., Kroner, K. F. (1995). Multivariate simultaneous generalized ARCH. Econometric Theory 11(1):122–150. doi:10.1017/S0266466600009063
   Web of Science ®Google Scholar
• Fan, J., Fan, Y., Lv, J. (2008). Large dimensional covariance matrix estimation using a factor model. Journal of Econometrics 147(1):186–197. doi:10.1016/j.jeconom.2008.09.017
   Web of Science ®Google Scholar
• Fan, J., Li, R. (2001). Variable selection via nonconcave penalized likelihood and its oracle properties. Journal of the American Statistical Association 96(456):1348–1360. doi:10.1198/016214501753382273
   Web of Science ®Google Scholar
• Francq, C., Zakoïan, J.-M. (2010). GARCH Models Structure, Statistical Inference and Financial Applications, Chichester, West Sussex: John Wiley and Sons.
   Google Scholar
• Gouriéroux, C. (1997). ARCH Models and Financial Applications, Berlin, Heidelberg: Springer.
   Google Scholar
• Hastie, T., Tibshirani, R., Wainwright, M. (2015). Statistical Learning with Sparsity: The Lasso and Generalizations. Monographs on Statistics and Applied Probability 143, London: Chapman and Hall.
   Google Scholar
• Higham, N. J., Tisseur, F. (2003). Bounds for eigenvalues of matrix polynomials. Linear Algebra and Its Applications 358(1–3):5–22. doi:10.1016/S0024-3795(01)00316-0
   Google Scholar
• Pakel, C., Shephard, N., Sheppard, K., Engle, R. F. (2018). Fitting vast dimensional time-varying covariance models. Mimeo.
   Google Scholar
• Poignard, B. (2020). Asymptotic theory of the adaptive Sparse Group Lasso. Annals of the Institute of Statistical Mathematics 72(1):297–328. doi:10.1007/s10463-018-0692-7
   Web of Science ®Google Scholar
• Racine, J. (2000). Consistent cross-validatory model-selection for dependent data: hv-block cross-validation. Journal of Econometrics 99(1):39–61. doi:10.1016/S0304-4076(00)00030-0
   Web of Science ®Google Scholar
• Simon, N., Friedman, J., Hastie, T., Tibshirani, R. (2013). A Sparse Group Lasso. Journal of Computational and Graphical Statistics 22(2):231–245. doi:10.1080/10618600.2012.681250
   Web of Science ®Google Scholar
• Tibshirani, R., Suo, X. (2016). An ordered Lasso and Sparse time-lagged regression. Technometrics 4:415–423.
   Google Scholar