Modeling and forecasting dynamic (or time-varying) covariance matrices has many important applications in finance, such as Markowitz portfolio selection. A popular tool to this end are multivariate GARCH models. Historically, such models did not perform well in large dimensions due to the...

Multivariate GARCH models do not perform well in large dimensions due to the so-called curse of dimensionality. The recent DCC-NL model of Engle et al. (2019) is able to overcome this curse via nonlinear shrinkage estimation of the unconditional correlation matrix. In this paper, we show how...

Macro-prudential authorities need to assess medium-term downside risks to the real economy, caused by severe financial shocks. Before activating policy measures, they also need to consider their short-term negative impact. This gives rise to a risk management problem, an inter-temporal trade-off...

We propose and implement a procedure to optimally hedge climate change risk. First, we construct climate risk indices through textual analysis of newspapers. Second, we present a new approach to compute factor mimicking portfolios to build climate risk hedge portfolios. The new mimicking...

This paper investigates the significance of dynamic conditional beta in predicting the cross-sectional variation in expected stock returns. The results indicate that the time-varying conditional beta is alive and well in the cross-section of daily stock returns. Portfolio-level analyses and...

We discuss several multivariate extensions of the Multiplicative Error Model to take into account dynamic interdependence and contemporaneously correlated innovations (vector MEM or vMEM). We suggest copula functions to link Gamma marginals of the innovations, in a specification where past...