Computationally efficient inference procedures for vast dimensional realized covariance models
This paper illustrates some computationally efficient estimation procedures for the estimation of vast dimensional realized covariance models. In particular, we derive a Composite Maximum Likelihood (CML) estimator for the parameters of a Conditionally Autoregressive Wishart (CAW) model incorporating scalar system matrices and covariance targeting. The finite sample statistical properties of this estimator are investigated by means of a Monte Carlo simulation study in which the data generating process is assumed to be given by a scalar CAW model. The performance of the CML estimator is satisfactory in all the settings considered although a relevant finding of our study is that the efficiency of the CML estimator is critically dependent on the implementation settings chosen by modeller and, more specifically, on the dimension of the marginal log-likelihoods used to build the composite likelihood functions.
|Date of creation:||25 Jul 2012|
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- Robert Engle & Neil Shephard & Kevin Shepphard, 2008.
"Fitting vast dimensional time-varying covariance models,"
OFRC Working Papers Series
2008fe30, Oxford Financial Research Centre.
- Neil Shephard & Kevin Sheppard & Robert F. Engle, 2008. "Fitting vast dimensional time-varying covariance models," Economics Series Working Papers 403, University of Oxford, Department of Economics.
- Dovonon, Prosper & Goncalves, Silvia & Meddahi, Nour, 2010.
"Bootstrapping realized multivariate volatility measures,"
40123, University Library of Munich, Germany.
- Dovonon, Prosper & Gonçalves, Sílvia & Meddahi, Nour, 2013. "Bootstrapping realized multivariate volatility measures," Journal of Econometrics, Elsevier, vol. 172(1), pages 49-65.
- Joan Jasiak & R. Sufana & C. Gourieroux, 2005.
"The Wishart Autoregressive Process of Multivariate Stochastic Volatility,"
2005_2, York University, Department of Economics.
- Gourieroux, C. & Jasiak, J. & Sufana, R., 2009. "The Wishart Autoregressive process of multivariate stochastic volatility," Journal of Econometrics, Elsevier, vol. 150(2), pages 167-181, June.
- Golosnoy, Vasyl & Gribisch, Bastian & Liesenfeld, Roman, 2012.
"The conditional autoregressive Wishart model for multivariate stock market volatility,"
Journal of Econometrics,
Elsevier, vol. 167(1), pages 211-223.
- Golosnoy, Vasyl & Gribisch, Bastian & Liesenfeld, Roman, 2010. "The conditional autoregressive wishart model for multivariate stock market volatility," Economics Working Papers 2010,07, Christian-Albrechts-University of Kiel, Department of Economics.
- Diaa Noureldin & Neil Shephard & Kevin Sheppard, 2011.
"Multivariate High-Frequency-Based Volatility (HEAVY) Models,"
2011-W01, Economics Group, Nuffield College, University of Oxford.
- Diaa Noureldin & Neil Shephard & Kevin Sheppard, 2012. "Multivariate high‐frequency‐based volatility (HEAVY) models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(6), pages 907-933, 09.
- Diaa Noureldin & Neil Shephard & Kevin Sheppard, 2011. "Multivariate High-Frequency-Based Volatility (HEAVY) Models," Economics Series Working Papers 533, University of Oxford, Department of Economics.
- BAUWENS, Luc & STORTI, Giuseppe & VIOLANTE, Francesco, 2012. "Dynamic conditional correlation models for realized covariance matrices," CORE Discussion Papers 2012060, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- Bonato, Matteo & Caporin, Massimiliano & Ranaldo, Angelo, 2012.
"Forecasting Realized (Co)Variances with a Bloc Structure Wishart Autoregressive Model,"
Working Papers on Finance
1211, University of St. Gallen, School of Finance.
- Matteo Bonato & Massimiliano Caporin & Angelo Ranaldo, 2009. "Forecasting realized (co)variances with a block structure Wishart autoregressive model," Working Papers 2009-03, Swiss National Bank.
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