A dynamic component model for forecasting high-dimensional realized covariance matrices

Luc Bauwens, Manuela Braione and Giuseppe Storti

No 2016001, LIDAM Discussion Papers CORE from Université catholique de Louvain, Center for Operations Research and Econometrics (CORE)

Abstract: The Multiplicative MIDAS Realized DCC (MMReDCC) model of Bauwens et al. [5] decomposes the dynamics of the realized covariance matrix of returns into short-run transitory and long-run secular components where the latter reflects the effect of the continuously changing economic conditions. The model allows to obtain positive-definite forecasts of the realized covariance matrices but, due to the high number of parameters involved, estimation becomes unfeasible for large cross-sectional dimensions. Our contribution in this paper is twofold. First, in order to obtain a computationally feasible estimation procedure, we propose an algorithm that relies on the maximization of an iteratively re-computed moment-based profile likelihood function. We assess the finite sample properties of the proposed algorithm via a simulation study. Second, we propose a bootstrap procedure for generating multi-step ahead forecasts from the MMReDCC model. In an empirical application on realized covariance matrices for fifty equities, we find that the MMReDCC not only statistically outperforms the selected benchmarks in-sample, but also improves the out-of-sample ability to generate accurate multi-step ahead forecasts of the realized covariances.

Keywords: Realized covariance; dynamic component models; multi-step forecasting; MIDAS; targeting; model confidence set (search for similar items in EconPapers)
Date: 2016-02-01
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
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Citations: View citations in EconPapers (5)

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Related works:
Working Paper: A Dynamic Component Model for Forecasting High-Dimensional Realized Covariances Matrices (2020)
Journal Article: A dynamic component model for forecasting high-dimensional realized covariance matrices (2017)
Working Paper: A dynamic component model for forecasting high-dimensional realized covariance matrices (2017)
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