Credit Risk and Asset Correlation Modelling for the Swedish Market: A Comparative Analysis

Abstract: In order to ensure solvency, financial institutions must evaluate their credit risk exposure and determine how much economic capital is required to hold as a cushion. This thesis compares three factor models, namely Asymptotic Single Risk Factor (“ASRF”), Inter-sector and Intra-sector factor models and evaluates how their different characteristics affect the economic capital outcomes. The thesis also investigates how these outcomes are affected when assuming asset dependency through a Student's-$t$ copula. Focus will also be put on how different types and levels of asset correlation affect the models' credit risk results. We use a fictitious loan portfolio consisting of 138 Swedish firms with equity data from between 2007 and 2019 in order to calculate asset correlations and economic capital. Our main findings are that the asset correlations severely impact the outcomes of the credit risk models and that practitioners must calibrate and stress test their models regularly with respect to how correlations vary between different firms. The thesis also finds that using copulas for credit portfolios provides more conservative risk outcomes but makes the models less sensitive to correlation level input.