Valuing Convertible Bonds using Stochastic Dynamic Programming with Monte Carlo Based Regressions - An empirical study of the US convertible bond market

Abstract

Using Monte Carlo simulation combined with least squares regression to estimate continuation values and optimal exercise decisions in a stochastic dynamic programming framework, we estimate fair price for 40 convertible bonds in the US market. In contrast to most previous studies, we do not find evidence of systematic underpricing in the market. Our results show an average overpricing of 1.1 %, while deviations between observed and predicted prices seem related to coupon rate and credit rating. Furthermore, we find no evidence of a relation between price deviation and moneyness of the conversion option.