Credit Risk Modeling in a Semi-Markov Process Environment

Book excerpt: In recent times, credit risk analysis has grown to become one of the most important problems dealt with in the mathematical finance literature. Fundamentally, the problem deals with estimating the probability that an obligor defaults on their debt in a certain time. To obtain such a probability, several methods have been developed which are regulated by the Basel Accord. This establishes a legal framework for dealing with credit and market risks, and empowers banks to perform their own methodologies according to their interests under certain criteria. Credit risk analysis is founded on the rating system, which is an assessment of the capability of an obligor to make its payments in full and on time, in order to estimate risks and make the investor decisions easier. Credit risk models can be classified into several different categories. In structural form models (SFM), that are founded on the Black & Scholes theory for option pricing and the Merton model, it is assumed that default occurs if a firm's market value is lower than a threshold, most often its liabilities. The problem is that this is clearly is an unrealistic assumption. The factors models (FM) attempt to predict the random default time by assuming a hazard rate based on latent exogenous and endogenous variables. Reduced form models (RFM) mainly focus on the accuracy of the probability of default (PD), to such an extent that it is given more importance than an intuitive economical interpretation. Portfolio reduced form models (PRFM) belong to the RFM family, and were developed to overcome the SFM's difficulties. Most of these models are based on the assumption of having an underlying Markovian process, either in discrete or continuous time. For a discrete process, the main information is containted in a transition matrix, from which we obtain migration probabilities. However, according to previous analysis, it has been found that this approach contains embedding problems. The continuous time Markov process (CTMP) has its main information contained in a matrix Q of constant instantaneous transition rates between states. Both approaches assume that the future depends only on the present, though previous empirical analysis has proved that the probability of changing rating depends on the time a firm maintains the same rating. In order to face this difficulty we approach the PD with the continuous time semi-Markov process (CTSMP), which relaxes the exponential waiting time distribution assumption of the Markovian analogue. In this work we have relaxed the constant transition rate assumption and assumed that it depends on the residence time, thus we have derived CTSMP forward integral and differential equations respectively and the corresponding equations for the particular cases of exponential, gamma and power law waiting time distributions, we have also obtained a numerical solution of the migration probability by the Monte Carlo Method and compared the results with the Markovian models in discrete and continuous time respectively, and the discrete time semi-Markov process. We have focused on firms from U.S.A. and Canada classified as financial sector according to Global Industry Classification Standard and we have concluded that the gamma and Weibull distribution are the best adjustment models.