This paper examines the relation between variations in perceived inflation uncertainty and bond premia. Using the subjective probability distributions available in the Survey of Professional Forecasters we construct a quarterly time series of average individual uncertainty about inflation...

The evolution of the yields of different maturities is related and can be described by a reduced number of commom latent factors. Multifactor interest rate models of the finance literature, common factor models of the time series literature and others use this property. Each model has advantages...

This research investigates the macro factors for forecasting (1) bond risk premia and (2) term structure of government bond yields by using Bayesian Model Averaging (BMA) based on empirical prior. Different from the traditional variable selection approach which advocates finding an...

Statistical learning models have profoundly changed the rules of trading on the stock exchange. Quantitative analysts try to utilise them predict potential profits and risks in a better manner. However, the available studies are mostly focused on testing the increasingly complex machine learning...

A 'lost decade' for the Eurozone is looming on the horizon. Under these circumstances, stable indicators for future economic activity are especially valuable to decision makers. This paper examines the predictive power of the yield spread, one of the most reliable indicators for gross domestic...

An OLS and probit framework is used to examine the predictive power of yield spreads with respect to GDP growth and recessions in the Eurozone from the 1990s to the recent past. Credit default swap (CDS) data on sovereign bonds, which provide a direct measure of default risk, are employed as...

We assess financial theory-based and machine learning-implied measurements of stock risk premia by comparing the quality of their return forecasts. In the low signal-to-noise environment of a one month horizon, we find that it is preferable to rely on a theory-based approach instead of engaging...

We use machine learning methods to predict stock return volatility. Our out-of-sample prediction of realised volatility for a large cross-section of US stocks over the sample period from 1992 to 2016 is on average 44.1% against the actual realised volatility of 43.8% with an R2 being as high as...