Accurate estimation and optimal control of tail risk is important for building portfolios with desirable properties, especially when dealing with a large set of assets. In this work, we consider optimal asset allocations strategies based on the minimization of two asymmetric deviation measures,...

We employ a mixed-frequency quantile regression approach to model the time-varying conditional distribution of the US real GDP growth rate. We show that monthly information on the US financial cycle improves the predictive power of an otherwise quarterly-only model. We combine selected quantiles...

This study proposes a novel expectile regression complete subset averaging (ECSA) method to forecast the downside risk for asset returns. Given a high-dimensional set of covariates, we combine the forecasts from a complete subset of expectile regression models that use a fixed number of...

This paper presents a framework for quantifying uncertainty around point forecasts for GDP, inflation and house prices in Norway. The framework combines quantile regressions using a broad set of uncertainty indicators with a skewed t-distribution, allowing for time-variation and asymmetry in the...