Comparative Analysis of the Reduced form Model and the Structural Model in Credit Risk Modelling

Abstract

Credit risk models are statistical tools to infer the future default probabilities and loss distribution of values of a portfolio of debts. Credit risk modelling is prevalent in today’s financial decision-making process. It turns out that both models of modelling credit risk contribute to explaining the default risk of listed firms, however, reduce-form model outperformances the structural model. Structural models are used to calculate the probability of default for a firm based on the value of assets and liabilities. The basic idea is that a company (with limited liability) defaults if the value of its assets is less than the debt of the company. The causal driver of defaults in structural model will choose to work with variables that help us explain what causes defaults. Default risk is endogenous in the structural model, this is so because the factors that causes defaults within a path are predictable. The structural model is an economic model with focus on options pricing, call option and put option. It provides clarity about the nature of defaults and how the various economic features that are chosen to relate with each other when defaults occur. The reduced form model is mostly concerned with prediction of when does defaults occurs? Default risk is exogenous to the reduced form model, can be caused by random events and most often comes as a surprise. Statistical models are used to observe the variables and help maximise the reduced form model. The empirical result suggests that reduce-form model can better predict the firm’s default risk.