Abstract:
Credit risk modelling and quantification is a very crucial issue in bank management and has become more popular among practitioners and academicians in recent years because of the changes and developments in banking and financial systems. CreditMetrics of J.P. Morgan, KMV Portfolio Manager, CreditRisk+ of Credit Suisse First Boston, and McKinsey’s CreditPortfolioView are widely used frameworks in practice. Thus, this thesis focuses on these models rather than statistical modelling most academic publications are based on. Nevertheless, we find that there are several links between the models used in practice, the statistical models, and the regulatory frameworks such as Basel II. Moreover, we explore the basics of the Basel II capital accord, the principles of four frameworks and the calibration methods available in the literature. As a result of this study, it seems possible to apply the credit risk frameworks used in practice to estimate the parameters of the Basel II framework. Also, we develop a regression method to calibrate the multifactor model of CreditMetrics and determine the necessary steps for this implementation. Although, due to incomplete information provided in the literature on the calibration of these models and due to lack of data, it may not be easy to implement the models used in practice, in this thesis we calibrate the multifactor model of CreditMetrics to accessible real data by our regression based calibration method. The small credit portfolio formed by real-world data taken from Bloomberg Data Services is also presented within this thesis. Next, we artificially generate a large multifactor model for a large credit portfolio taking our real-world portfolio as a reference in order to inspect the loss and value distributions of a realistically large credit portfolio. Finally, by analyzing the results of multi-year Monte Carlo simulations on different portfolios of different rating concentrations, we deduce the significance of the effects of transition risk and portfolio concentration on risk-return profile, and the strength of simulation in assessing the credit spread policy. Throughout this thesis R-Software environment has been used for all kind of computations.
