Leírás
We briefly present a multi-factor Gaussian copula portfolio model for corporate default risk. We model the asset value of each company with a stochastic process, where the simulated asset values drive the possible future defaults of the companies. The model assumes three types of systematic factors driving the asset value of each company. These factors represent the state of the global economy and the economic conditions of different geographical regions and industries. The corresponding factor loadings play a key role in the model, as they capture the correlation structure between the asset returns of different companies and therefore influence the joint probabilities of default. Higher correlation between the asset returns of different companies in a portfolio increases the likelihood that multiple companies will default simultaneously, thus increasing the likelihood of extreme losses in the portfolio. Hence, accurately measuring these correlations is essential for the identification of portfolio risk.
We describe a possible methodology for measuring the correlations between asset returns of different companies, which can be used for calibrating the corresponding factor loadings. The approach relies upon single-name CDS spread data. We will also briefly analyze the structure of correlations obtained using this methodology.