Estimating Banks' Probability of Default

In this thesis, after presenting different metrics and approaches that investors and analysts use to assess the financial stability of a bank, such as banking ratios and credit ratings, and the regulations of credit risk to which banks are subjected, I focus on the model of default forecasting built by Merton in 1974 and subsequently developed by KMV. I define few modifications that make the model more accessible, and use it to show how the KMV-Merton model can give a simplified yet straight-forward assessment of a bank’s health. I perform an analysis on the ten largest European banking institutions during the first four waves of the COVID-19 crisis, computing the distances to default and default probabilities. I then compute the averages of the distances to default of the sample and compare it to the average of the two biggest American banks and to two types of Z-score and six different financial metrics. I conclude that all indicators follow the same trend but the distance to default is available at a higher frequency. I include the VBA code used to compute the simplified KMV-Merton probability of default in the Appendix A.