Beyond rocket science: A factor model for convertible bond returns

Abstract

Due to the lack of high-quality data and pricing complexity, convertible bonds are difficult to be captured by the factor model widely used in empirical asset pricing. We consider a zoo of convertible bond predictors in the Chinese markets and use instrumented principal components analysis (IPCA) to capture the cross-sectional returns of convertible bonds. Compared with the observable factor models in corporate bond and equity markets, the latent factor model can better describe the common variation in realized returns, and exhibit smaller pricing errors both in-sample and out-of-sample.