Multivariate High-Frequency-Based Factor Model

32 Pages Posted: 11 Mar 2020

See all articles by Simon Bodilsen

Simon Bodilsen

Aarhus University - Department of Economics and Business Economics

Date Written: February 18, 2020

Abstract

We propose 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 an approximate factor model. A novel feature of the model, is the use of the hierarchical clustering algorithm to determine the structure of the idiosyncratic covariance matrices. To construct a conditional covariance model, we suggest to model the components of the covariance structure separately using autoregressive time series regressions. 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: High-frequency data, multivariate volatility, hierarchical clustering, factor models, portfolio selection

JEL Classification: C55, C58, G11

Suggested Citation

Bodilsen, Simon, Multivariate High-Frequency-Based Factor Model (February 18, 2020). 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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