High-dimensional Realized Covariance Estimation: a Parametric Approach

32 Pages Posted: 18 Mar 2020 Last revised: 7 Sep 2022

See all articles by Giuseppe Buccheri

Giuseppe Buccheri

University of Verona - Department of Economics

Gael Mboussa Anga

Scuola Normale Superiore

Date Written: November 10, 2021

Abstract

We introduce a parametric dynamic factor specification for high-frequency financial data that simplifies considerably the estimation of the realized covariance matrix in high dimensions.
The estimation method is tested in an empirical setting that emphasizes the effect of the curse of dimensionality. Compared to standard parametric approaches, our factor specification is computationally less demanding and provides statistically indistinguishable performances in standard risk management applications. The method is also assessed on Monte-Carlo simulations under several forms of misspecification.

Keywords: Realized covariance; Risk management; High-dimensions; Epps effect;

JEL Classification: C58, D53, D81

Suggested Citation

Buccheri, Giuseppe and Mboussa Anga, Gael, High-dimensional Realized Covariance Estimation: a Parametric Approach (November 10, 2021). Available at SSRN: https://ssrn.com/abstract=3541883 or http://dx.doi.org/10.2139/ssrn.3541883

Giuseppe Buccheri (Contact Author)

University of Verona - Department of Economics ( email )

Via Cantarane, 24
37129 Verona
Italy
045 8028525 (Phone)

Gael Mboussa Anga

Scuola Normale Superiore ( email )

Piazza dei Cavalieri, 7
Pisa, 56126
Italy

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