Large-Dimensional Portfolio Selection with a High-Frequency-Based Dynamic Factor Model

46 Pages Posted: 11 Mar 2020 Last revised: 15 Nov 2022

See all articles by Simon Bodilsen

Simon Bodilsen

Aarhus University - Department of Economics and Business Economics

Date Written: November 11, 2022

Abstract

This paper proposes a new predictive model for large-dimensional realized covariance
matrices. Using high-frequency data, we construct daily realized covariance matrices
for the constituents of the S&P 500 Index and a set of observable factors. Using
a standard decomposition of the joint covariance matrix, we express the covariance
matrix of the individual assets similar to a dynamic factor model. To construct a
conditional covariance model, we suggest to model the components of the covariance
structure using a series of autoregressive time series regressions. A novel feature of
the model, is the use of the data-driven hierarchical clustering algorithm to determine
the structure of the idiosyncratic covariance matrix. In an out-of-sample portfolio
selection exercise, we find that the proposed model outperforms other commonly used
multivariate volatility models in extant literature.

Keywords: Big data, Hierarchical clustering, High-frequency data, Minimum variance portfolio, Multivariate volatility

JEL Classification: C55, C58, G11

Suggested Citation

Bodilsen, Simon, Large-Dimensional Portfolio Selection with a High-Frequency-Based Dynamic Factor Model (November 11, 2022). Available at SSRN: https://ssrn.com/abstract=3540455 or http://dx.doi.org/10.2139/ssrn.3540455

Simon Bodilsen (Contact Author)

Aarhus University - Department of Economics and Business Economics ( email )

Fuglesangs Allé 4
Aarhus V, 8210
Denmark

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