A Large Confirmatory Dynamic Factor Model for Stock Market Returns in Different Time Zones

TL;DR

This paper introduces a confirmatory dynamic factor model for stock returns observed across multiple time zones, incorporating global and continental factors, with estimators proven to be consistent and applied to international market data.

Contribution

It develops two estimators for a large dynamic factor model with global and regional factors, demonstrating their consistency and asymptotic properties.

Findings

Markets are more integrated during high VIX periods.

The model effectively captures cross-time zone stock return dynamics.

Estimators perform well in large sample settings.

Abstract

We propose a confirmatory dynamic factor model for a large number of stocks whose returns are observed daily across multiple time zones. The model has a global factor and a continental factor that both drive the individual stock return series. We propose two estimators of the model: a quasi-maximum likelihood estimator (QML-just-identified), and an improved estimator based on an Expectation Maximization (EM) algorithm (QML-all-res). Our estimators are consistent and asymptotically normal under the large approximate factor model setting. In particular, the asymptotic distributions of QML-all-res are the same as those of the infeasible OLS estimators that treat factors as known and utilize all the restrictions on the parameters of the model. We apply the model to MSCI equity indices of 42 developed and emerging markets, and find that most markets are more integrated when the CBOE Volatility Index (VIX) is high.