We develop new multi-factor dynamic copula models with time-varying factor loadings and observation-driven dynamics. The new models are highly flexible, scalable to high dimensions, and ensure positivity of covariance and correlation matrices. A closed-form likelihood expression allows for...

We investigate the effect of estimation error on backtests of (multi-period) expected shortfall (ES) forecasts. These backtests are based on first order conditions of a recently introduced family of jointly consistent loss functions for Value-at-Risk (VaR) and ES. We provide explicit expressions...

To investigate how economies, financial markets or institutions can deal with stress, we nowadays often analyze the effects of shocks conditional on a recession or a bear market. MSVAR models are ideally suited for such analyses because they combine gradual movement with sudden switches. In this...

Housing markets typically exhibit a strong positive correlation between the rate of price increase and the number of houses sold. We document this correlation on high-quality Dutch data for the period 1985-2007, and estimate a VEC-model that allows us to study the mechanism giving rise to the...

This paper describes a forecasting exercise of close-to-open returns on major global stock indices, based on price patterns from foreign markets that have become available overnight. As the close-to-open gap is a scalar response variable to a functional variable, it is natural to focus on...

The Basel II Accord requires that banks and other Authorized Deposit-taking Institutions (ADIs) communicate their daily risk forecasts to the appropriate monetary authorities at the beginning of each trading day, using one or more risk models to measure Value-at-Risk (VaR). The risk estimates of...

We model the impact of financial conditions on asset market volatility and correlation. We propose extensions of (factor-)GARCH models for volatility and DCC models for correlation that allow for including indexes that measure financial conditions. In our empirical application we consider daily...

Accurate prediction of risk measures such as Value at Risk (VaR) and Expected Shortfall (ES) requires precise estimation of the tail of the predictive distribution. Two novel concepts are introduced that offer a specific focus on this part of the predictive density: the censored posterior, a...

The purpose of the paper is to discuss ten things potential users should know about the limits of the Dynamic Conditional Correlation (DCC) representation for estimating and forecasting time-varying conditional correlations. The reasons given for caution about the use of DCC include the...

We analyze earnings forecasts retrieved from the I/B/E/S database concerning 596 firms for the sample 1995 to 2011, with a specific focus on whether these earnings forecasts can be predicted from available data. Our main result is that earnings forecasts can be predicted quite accurately using...