PT - JOURNAL ARTICLE AU - Daniel Klein AU - Elena Nikitina AU - Jean-Christophe Curtillet TI - Forecasting Swap Spreads: <em>A Bayesian Approach</em> AID - 10.3905/jfi.2016.26.2.040 DP - 2016 Sep 30 TA - The Journal of Fixed Income PG - 40--53 VI - 26 IP - 2 4099 - https://pm-research.com/content/26/2/40.short 4100 - https://pm-research.com/content/26/2/40.full 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