High-dimensional multivariate realized volatility estimation

Tim Bollerslev, Nour Meddahi and Serge Luther Nyawa Womo
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Tim Bollerslev: Duke University [Durham]
Nour Meddahi: TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement
Serge Luther Nyawa Womo: TBS - Toulouse Business School

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Abstract: We provide a new factor-based estimator of the realized covolatility matrix, applicable in situations when the number of assets is large and the high-frequency data are contaminated with microstructure noises. Our estimator relies on the assumption of a factor structure for the noise component, separate from the latent systematic risk factors that characterize the cross-sectional variation in the frictionless returns. The new estimator provides theoretically more efficient and finite-sample more accurate estimates of large-scale integrated covolatility and correlation matrices than other recently developed realized estimation procedures. These theoretical and simulation-based findings are further corroborated by an empirical application related to portfolio allocation and risk minimization involving several hundred individual stocks.

Keywords: Realized covolatility matrix; High-dimensional estimation; High-frequency data; Microstructure noise; Robust measures (search for similar items in EconPapers)
Date: 2019-09
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Published in Journal of Econometrics, 2019, 212 (1), pp.116-136. ⟨10.1016/j.jeconom.2019.04.023⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04947294

DOI: 10.1016/j.jeconom.2019.04.023

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