We study equity premium out-of-sample predictability by extracting the information contained in a high number of macroeconomic predictors via large dimensional factor models. We compare the well known factor model with a static representation of the common components with a more general model...

Recently, several copula-based approaches have been proposed for modeling stationary multivariate time series. All of them are based on vine copulas, and they differ in the choice of the regular vine structure. In this article, we consider a copula autoregressive (COPAR) approach to model the...

Standard factor models focus on returns and leave prices undetermined. Thisapproach ignores information contained in the time-series of asset prices, relevantfor long-term investors and for detecting potential mispricing. To address this issue,we propose a novel (co-)integrated methodology to...

Quarterly GDP figures usually are published with a delay of some weeks. A common way to generate GDP series of higher frequency, i.e. to nowcast GDP, is to use available indicators to calculate a single index by means of a common factor derived from a dynamic factor model (DFM). This paper deals...

Most macroeconomic indicators failed to capture the sharp economic fluctuations during the Corona crisis in a timely manner. Instead, alternative high-frequency data have been used, aiming to monitor the economic situation. However, these data are often only loosely related to the business cycle...

To improve the dynamic assessment of risks of speculative assets, we apply a Markov switching MGARCH approach to portfolio forecasting. More specifically, we take advantage of the flexible Markov switching copula multivariate GARCH (MS-C-MGARCH) model of Fülle and Herwartz (2021). As an...