High-dimensional multivariate realized volatility estimation

Tim Bollerslev, Nour Meddahi and Serge Nyawa

Journal of Econometrics, 2019, vol. 212, issue 1, 116-136

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)
JEL-codes: C13 C32 C58 (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:212:y:2019:i:1:p:116-136

DOI: 10.1016/j.jeconom.2019.04.023

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