Multivariate High-Frequency-Based Factor Model
32 Pages Posted: 11 Mar 2020
Date Written: February 18, 2020
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: Suggested Citation