Assessing Default Probabilities from Structural Credit Risk Models
In this paper, we study the empirical performance of structural credit risk models by examining the default probabilities calculated from these models with di erent time horizons. The parameters of the models are estimated from rms bond and equity prices. The models studied include Merton (1974), Merton model with stochastic interest rate, Longsta and Schwartz (1995), Leland and Toft (1996) and Collin-Dufresne and Goldstein (2001). The sample rms chosen are those that have only one bond outstanding when bond prices are observed. We rst nd that when the Maximum Likelihood estimation, introduced in Duan (1994), is used to estimate the Merton model from bond prices the estimated volatility is unreasonable high and the estimation process does not converge for most of the rms in our sample. It shows that the Merton (1974) is not able to generate high yields to match the empirical observations. On the other hand, when equity prices are used as input we nd nd that the default probabilities predicted for investment-grade rms by Merton (1974) are all close to zero. When stochastic interest rates are assumed in Merton model the model performance is improved. Longsta and Schwartz (1995) with constant interest rate as well as the Leland and Toft (1996) model provide quite reasonable predictions on real default probabilities when compared with those reported by Moodys and S&P. However, Collin-Dufresnce and Goldstein (2001) predicts unreasonably high default probabilities for longer time horizons.
Wulin Suo Wei Wang
Queens School of Business Queens University Kingston, Ontario, K7L 3N6
国际会议
西安
英文
2006-07-17（万方平台首次上网日期，不代表论文的发表时间）