RT Journal Article SR Electronic T1 Forecasting Swap Spreads: A Bayesian Approach JF The Journal of Fixed Income FD Institutional Investor Journals SP 40 OP 53 DO 10.3905/jfi.2016.26.2.040 VO 26 IS 2 A1 Daniel Klein A1 Elena Nikitina A1 Jean-Christophe Curtillet YR 2016 UL https://pm-research.com/content/26/2/40.abstract AB In this article, we analyze the determinants of swap spreads applying Bayesian model search (BMS). BMS is a flexible methodology that explicitly accounts for model risk and has become increasingly popular in the financial forecasting literature. We apply BMS to explore the data-rich environment of the candidate explanatory variables to predict the dynamics of the Euro area swap spreads using monthly data from 2000 to 2015 within a linear regression framework. Our empirical results suggest that the BMS approach shows consistently superior forecast performance compared to a model selection approach based on the Schwarz information criterion and a naïve benchmark model.TOPICS: Derivatives applications, big data/machine learning, developed