Credit risk models should reflect the observation that the relevant value of collateral is generally not the average value of the asset over all possible states of nature. In most cases, the relevant value of collateral for the lender is its secondary market value in bad states of nature, where...

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...

This paper attempts to find an aggregate leading indicator to predict the spreads observed for high-yield (HY) bond indices. Using a vector error correction (VEC) specification for quarterly data, we establish a long-term equilibrium relationship between the HY market spreads and its...

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...

Theories of systemic risk suggest that financial intermediaries' balance-sheet constraints amplify fundamental shocks. We provide supportive evidence for such theories by decomposing the U.S. dollar risk premium into components associated with macroeconomic fundamentals and a component...

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...