We propose a novel dynamic approach to forecast the weights of the global minimum variance portfolio (GMVP). The GMVP weights are the population coefficients of a linear regression of a benchmark return on a vector of return differences. This representation enables us to derive a consistent loss...

We propose a novel dynamic approach to forecast the weights of the global minimum variance portfolio (GMVP). The GMVP weights are the population coefficients of a linear regression of a benchmark return on a vector of return differences. This representation enables us to derive a consistent loss...

Social comparison nudges that employ descriptive norms were found to increase charitable giving. This paper finds that individuals who receive a descriptive norm donate significantly more when they have to guess the descriptive norm beforehand. We argue that guessing draws attention to the norm...

Social comparison nudges that employ descriptive norms were found to increase charitable giving. This paper finds that individuals who receive a descriptive norm donate significantly more when they have to guess the descriptive norm beforehand. We argue that guessing draws attention to the norm...

In this paper, we investigate the calibration of the smoothing parameter in an exponentially weighted moving average (EWMA) for realized covariance matrices. Although it is the crucial determinant in steering the dynamics of the EWMA, little attention is drawn on its calibration in many...

Die vorliegende Dissertation befasst sich mit probabilistischen Prognosen, die seit einigen Jahren ein aktives ökonometrisches Forschungsgebiet darstellen. Da solche Prognosen eine vollständige Verteilung für die interessierende Zufallsvariable angeben, beinhalten sie Information über...

The linear pool is the most popular method for combining density forecasts. We analyze its implications concerning forecast uncertainty, using a new framework that focuses on the means and variances of the individual and combined forecasts. Our results show that, if the variance predictions of...

Multivariate distributional forecasts have become widespread in recent years. To assess the quality of such forecasts, suitable evaluation methods are needed. In the univariate case, calibration tests based on the probability integral transform (PIT) are routinely used. However, multivariate...

This paper shows entropic tilting to be a flexible and powerful tool for combining medium-term forecasts from BVARs with short-term forecasts from other sources (nowcasts from either surveys or other models). Tilting systematically improves the accuracy of both point and density forecasts, and...

The linear pool is the most popular method for combining density forecasts. We analyze the linear pool's implications concerning forecast uncertainty in a new theoretical framework that focuses on the mean and variance of each density forecast to be combined. Our results show that, if the...