We discuss the development of dynamic factor models for multivariate financial time series,
and the incorporation of stochastic volatility components for latent factor processes …

We investigate the effects of dynamic heteroskedasticity on statistical factor analysis. We
show that identification problems are alleviated when variation in factor variances is …

Abstract The Stock–Watson coincident index and its subsequent extensions assume a static
linear one‐factor model for the component indicators. This restrictive assumption is …

A new multivariate time series model with time varying conditional variances and
covariances is presented and analysed. A complete analysis of the proposed model is …

GARCH models are commonly used as latent processes in econometrics, financial
economics, and macroeconomics. Yet no exact likelihood analysis of these models has …

The factor GARCH model of Engle (1987) and the latent factor ARCH model of Diebold and
Nerlove (1989) have become rather popular multivariate volatility parametrizations due to …

We make two complementary contributions to efficiently estimate dynamic factor models: a
frequency domain EM algorithm and a swift iterated indirect inference procedure for ARMA …

The origin of factor analysis can be traced back to Spearman's (1904) seminal paper on
general intelligence. At the time, psychologists were trying to define intelligence by a single …

In light of current high and volatile energy and food prices, we examine the daily dynamics of
the conditional variance-covariance matrix and price discovery in European vegetable oils …

We derive the likelihood function and score of factor models with dynamic
heteroskedasticity, and the Kuhn-Tucker conditions defining the inequality restricted …