Dynamic principal component CAW models for high-dimensional realized covariance matrices

References

• Aielli, G.P. and Caporin, M., Dynamic principal components: A new class of multivariate GARCH models. Working Paper, 2015.
   Google Scholar
• Alexander, C.O., Orthogonal GARCH. In Mastering Risk, edited by C.O. Alexander, Vol. 2, pp. 21–38, 2001 (Financial Times-Prentice Hall: London).
   Google Scholar
• Alexander, C.O. and Chibumba, A.M, Multivariate Orthogonal Factor GARCH, 1997 (Mimeo: University of Sussex, UK).
   Google Scholar
• Andersen, T.G., Bollerslev, T., Diebold, F.X. and Labys, P., Modeling and forecasting realised volatility. Econometrica, 2003, 71, 579–625. doi: 10.1111/1468-0262.00418
   Web of Science ®Google Scholar
• Asai, M. and McAleer, M., Forecasting co-volatilites via factor models with asymmetry and long memory in realized covariance. J. Econom., 2015, 189(2), 251–262. doi: 10.1016/j.jeconom.2015.03.020
   Web of Science ®Google Scholar
• Asai, M., McAleer, M. and Yu, J., Multivariate stochastic volatility: A review. Econom. Rev., 2006, 25, 145–175. doi: 10.1080/07474930600713564
   Web of Science ®Google Scholar
• Barndorff-Nielsen, O.E., Hansen, P.R., Lunde, A. and Shephard, N., Multivariate realised kernels: Consistent positive semi-definite estimators of the covariation of equity prices with noise and non-synchronous trading. J. Econom., 2011, 162, 149–169. doi: 10.1016/j.jeconom.2010.07.009
   Web of Science ®Google Scholar
• Barndorff-Nielsen, O.E. and Shephard, N., Econometric analysis of realised covariation: High frequency covariance, regression and correlation in financial economics. Econometrica, 2004, 72, 885–925. doi: 10.1111/j.1468-0262.2004.00515.x
   Web of Science ®Google Scholar
• Bauer, G.H. and Vorkink, K., Forecasting multivariate realized stock market volatility. J. Econom., 2011, 160, 93–101. doi: 10.1016/j.jeconom.2010.03.021
   Web of Science ®Google Scholar
• Bauwens, L., Braione, M. and Storti, G., Forecasting comparison of long term component dynamic models for realized covariance matrices. Ann. Econ. Stat., 2014, 123/124, 103–134.
   Google Scholar
• Bauwens, L., Braione, M. and Storti, G., A dynamic component model for forecasting high-dimensional realized covariance matrices. Economet. Stat., 2016, 1, 40–61. doi: 10.1016/j.ecosta.2016.09.003
   Web of Science ®Google Scholar
• Bauwens, L., Laurent, S. and Rombouts, J.V.K., Multivariate GARCH models: A survey. J. App. Econom., 2006, 21, 79–109. doi: 10.1002/jae.842
   Web of Science ®Google Scholar
• Bauwens, L., Storti, G. and Violante, F., Dynamic conditional correlation models for realized covariance matrices. CORE Working paper, 2012.
   Google Scholar
• Bollerslev, T. and Wooldridge, J.M., Quasi maximum likelihood estimation and inference in dynamic models with time-varying covariances. Econom. Rev., 1992, 11, 143–172. doi: 10.1080/07474939208800229
   Google Scholar
• Chiriac, R. and Voev, V., Modelling and forecasting multivariate realized volatility. J. Appl. Econom., 2011, 26, 922–947. doi: 10.1002/jae.1152
   Web of Science ®Google Scholar
• Corsi, F., A simple approximative long-memory model of realized volatility. J. Financ. Econom., 2009, 7(2), 174–196. doi: 10.1093/jjfinec/nbp001
   Web of Science ®Google Scholar
• Engle, R.F., Dynamic conditional correlation: A simple class of multivariate GARCH models. J. Bus. Econ. Stat., 2002, 20, 339–350. doi: 10.1198/073500102288618487
   Web of Science ®Google Scholar
• Engle, R.F, Anticipating Correlations, 2009 (Princeton University Press: Princeton, NJ).
   Google Scholar
• Engle, R.F. and Kroner, K.F., Multivariate simultaneous generalized ARCH. Econ. Theory., 1995, 11, 122–150. doi: 10.1017/S0266466600009063
   Web of Science ®Google Scholar
• Engle, R.F., Ledoit, O. and Wolf, M., Large dynamic covariance matrices. J. Bus. Econ. Stat., 2019, 37(2), 363–375. doi: 10.1080/07350015.2017.1345683
   Web of Science ®Google Scholar
• Geweke, J. and Meese, R., Estimating regression models of finite but unknown order. Int. Econ. Rev. (Philadelphia), 1981, 22, 55–70. doi: 10.2307/2526135
   Web of Science ®Google Scholar
• Golosnoy, V., Gribisch, B. and Liesenfeld, R., The conditional autoregressive Wishart model for multivariate stock market volatility. J. Econom., 2012, 167, 211–223. doi: 10.1016/j.jeconom.2011.11.004
   Web of Science ®Google Scholar
• Gourieroux, C., Jasiak, J. and Sufana, R., The Wishart autoregressive process of multivariate stochastic volatility. J. Econom., 2009, 150, 167–181. doi: 10.1016/j.jeconom.2008.12.016
   Web of Science ®Google Scholar
• Hafner, C.M. and Reznikova, O., On the estimation of dynamic conditional correlation models. Comput. Stat. Data. An., 2012, 56, 3533–3545. doi: 10.1016/j.csda.2010.09.022
   Web of Science ®Google Scholar
• Hansen, P.R., Lunde, A. and Nason, J.M., The model confidence set. Econometrica, 2011, 79, 453–497. doi: 10.3982/ECTA5771
   Web of Science ®Google Scholar
• Jin, X., Maheu, J.M. and Yang, Q., Bayesian parametric and semiparametric factor models for large realized covariance matrices. J. Appl. Econom., 2019, 34(5), 641–660. doi: 10.1002/jae.2685
   Web of Science ®Google Scholar
• Kelley, C.T., Iterative Methods for Optimization, 1999 (Society for Industrial and Applied Mathematics: Philadelphia, PA).
   Google Scholar
• Lütkepohl, H., Handbook of Matrices, 1996 (Wiley: Chichester).
   Google Scholar
• Lütkepohl, H, New Introduction to Multiple Time Series Analysis, 2005 (Springer: Berlin).
   Google Scholar
• Lunde, A., Shephard, N. and Sheppard, K., Econometric analysis of vast covariance matrices using composite realized kernels and their application to portfolio choice. J. Bus. Econ. Stat., 2016, 34(4), 504–518. doi: 10.1080/07350015.2015.1064432
   Web of Science ®Google Scholar
• Morgan, J.P, Risk Metrics. Technical Document, Fourth ed., 1996 (J.P. Morgan/Reuters: New York).
   Google Scholar
• Noureldin, D., Shephard, N. and Sheppard, K., Multivariate high-Frequency-Based volatility (HEAVY) models. J. Appl. Econom., 2012, 27, 907–933. doi: 10.1002/jae.1260
   Web of Science ®Google Scholar
• Noureldin, D., Shephard, N. and Sheppard, K., Multivariate rotated ARCH models. J. Econom., 2014, 179, 16–30. doi: 10.1016/j.jeconom.2013.10.003
   Web of Science ®Google Scholar
• Pakel, C., Shephard, N., Sheppard, K. and Engle, R.F., Fitting vast dimensional time-varying covariance models. SSRN Working Paper, 2018.
   Google Scholar
• Patton, A.J., Volatility forecast comparison using imperfect volatility proxies. J. Econom., 2011, 16(1), 246–256. doi: 10.1016/j.jeconom.2010.03.034
   Google Scholar
• Rao, C.R, Linear Statistical Inference and Its Applications, 1965 (John Wiley & Sons: Hoboken, NJ).
   Google Scholar
• Shen, K., Yao, J. and Li, W.K., Forecasting high-dimensional realized volatility matrices using a factor model. Quant. Finance, Online June, 2018. doi: 10.1080/14697688.2018.1473632
   Google Scholar
• Sheppard, K. and Xu, W., Factor high-frequency based volatility (HEAVY) models. J. Financ. Econom., 2019, 17(1), 33–65. doi: 10.1093/jjfinec/nby028
   Web of Science ®Google Scholar
• Tao, M., Wang, Y., Yao, Q. and Zou, J., Large volatility matrix inference via combining low-frequency and high-frequency approaches. J. Amer. Statist. Assoc., 2011, 106(495), 1025–1040. doi: 10.1198/jasa.2011.tm10276
   Web of Science ®Google Scholar
• Wang, Y. and Zou, J., Vast volatility matrix estimation for high-frequency financial data. Ann. Statist., 2010, 38(2), 943–978. doi: 10.1214/09-AOS730
   Web of Science ®Google Scholar
• Wooldridge, J.M., Estimation and inference for dependent processes. In Handbook of Econometrics, edited by R.F. Engle and D.L. McFadden, Vol. 4, pp. 2639–2738, 1994 (Elsevier B.V.: Amsterdam).
   Google Scholar