Computationally efficient inference procedures for vast dimensional realized covariance models
Luc Bauwens () and
Giuseppe Storti ()
No 2012028, CORE Discussion Papers from Université catholique de Louvain, Center for Operations Research and Econometrics (CORE)
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.
Keywords: realized covariance; CAW model; BEKK model; composite likelihood; covariance targeting; Wishart distribution (search for similar items in EconPapers)
JEL-codes: C32 C58 (search for similar items in EconPapers)
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Working Paper: Computationally efficient inference procedures for vast dimensional realized covariance models (2013)
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Persistent link: https://EconPapers.repec.org/RePEc:cor:louvco:2012028
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