We study the term structure of variance (total risk), systematic and idiosyncratic risk. Consistent with the expectations hypothesis, we find that, for the entire market, the slope of the term structure of variance is mainly informative about the path of future variance. Thus, there is little...

Predictability is time and frequency dependent. We propose a new forecasting method - forecast combination in the frequency domain - that takes this fact into account. With this method we forecast the equity premium and real GDP growth rate. Combining forecasts in the frequency domain produces...

Enhanced machine learning methods provide an encouraging alternative to forecast asset prices by extending or generalizing the possible model specifications compared to conventional linear regression methods. Even if enhanced methods of machine learning in the literature often lead to better...

Motivated by recent US evidence, we evaluate the predictive power of changes in the weight of large firms in the aggregate stock market ("Goliath vs David" (GVD)) for Swiss stock market returns and bond market returns. Previous research suggests that the asset return dynamics in the US and...

This paper aims to forecast the Market Risk premium (MRP) in the US stock market by applying machine learning techniques, namely the Multilayer Perceptron Network (MLP), the Elman Network (EN) and the Higher Order Neural Network (HONN). Furthermore, Univariate ARMA and Exponential Smoothing...

We examine the informational efficiency of market ambiguity in predicting market excess returns and the equity premium internationally. Empirical results show a strong predictive ability of option-implied, and sentiment-based, ambiguity for U.S. stock market returns for up to three years. We...

In this paper we investigate 3 important properties of global currencies: misalignments measured by the deviations from equilibrium (real effective) exchange rates, crash sensitivity captured by the copula tail dependence to the global market, and moment risk premia using a model-free method --...

We show that the out-of-sample forecast of the equity risk premium can be significantly improved by taking into account the frequency-domain relationship between the equity risk premium and several potential predictors. We consider fifteen predictors from the existing literature, for the...