An empirical investigation of a structural credit risk model.
Koo, Wai Ming.
Lee, Teck Kiang.
Sim, Carolyn Boon Kheng.
Date of Issue2000
School of Mechanical and Aerospace Engineering
Default probabilities are important to the credit markets. Changes in default probabilities of a borrowing firm may predict the occurrence of financial distress or default in the firm. Knowing a firm's default likelihood is important to financial lenders as it allows them to estimate their resulting credit exposure to the firm. In this dissertation, we examine the likelihood of default of a group of local companies listed on the Singapore Exchange using the default prediction framework of the KMV Corporation of San Francisco. Although a variety of default risk models are available in the market, we have chosen the KMV approach for several reasons. First, it is relatively simple to implement. Second, by being based on stock market data rather than "historic" book value accounting data, it is forward-looking. Third, it has strong theoretical underpinnings, having its basis on the modern theory of corporate finance and options. Based on our study, there appears to be significant leading information about credit events in the expected default frequencies (EDFs) generated using the KMV framework.
DRNTU::Engineering::Mechanical engineering::Mechanics and dynamics
Nanyang Technological University