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Distance to default as a measure of default risk

Companies are facing increasing uncertain times, key investment decisions and challenging financial needs. Now more than ever it is important to understand both the financial decisions of the business corporations as well as how they connect. In this paper I would like to show the basics to understand complex pricing of assets such as derivatives. Since central, commercial and investment banks use lots of models to price securities and forecast financial markets I wanted to research on the basic theorems and ideas needed to do so. The subject that I chose to study is the combination of the representative companies included in the EURO STOXX 50 index, but excluding the financial institutions due to high leverage ratios that may lead to wrong interpretations. In order to estimate the credit (default) risk on every company’s assets, I used two different approaches and then I compared and chose the one that was more precise with the results obtained. Moody’s KMV approach to measure distance to default is the most commonly used, but in the paper I show that it is not always the best option for estimating this measure. In order to provide evidence about the functionality of the distance to default, I compare the estimations gathered from the empirical research with credit default swaps (CDS) that are linked to the sample companies of the project. Two different sets of linear regressions were done. The first set of regressions is the cross-sectional one, while the second set is done with a sequence of time-series data points. By the comparison of these regressions, it’s expected to conclude whether the traditional and most widespread method is the best possible way to perform such estimations or if there are better ways to proceed and therefore, that may give us even better results.