A Dynamic Factor Model for Level and Volatility

TL;DR

This paper introduces a dynamic factor model that jointly captures level and volatility factors, enabling endogenous risk modeling and improving forecast accuracy, especially in tail distributions.

Contribution

It develops a novel joint evolution framework for level and volatility factors within a large-information setting, enhancing risk modeling and forecast precision.

Findings

Model improves density forecast accuracy in tails

Global inflation analysis shows heterogeneity in risk factors

Volatility factors significantly influence medium-term forecasts

Abstract

This paper develops a dynamic factor model in which common level and volatility factors evolve jointly, allowing conditional means and variances to interact endogenously within a large-information setting. The joint evolution of these factors provides a tractable framework for modeling risk, as fluctuations in volatility affect both the dispersion and the location of outcomes, generating state-dependent and asymmetric tail risks in predictive distributions. Volatility is captured by latent common factors that drive co-movement in second moments across a large panel, while heavy-tailed idiosyncratic shocks absorb transitory outliers and isolate persistent uncertainty dynamics. The framework embeds these interactions directly within a factor structure, allowing risk to arise endogenously from the joint dynamics of the system rather than being imposed through reduced-form approaches. Empirically, the model delivers systematic improvements in density forecast accuracy, particularly in the tails of the predictive distribution and at medium horizons. An application to international inflation highlights a dominant global level component in advanced economies and stronger regional and volatility contributions in emerging and developing economies, pointing to substantial heterogeneity in the role of uncertainty across countries.