Abstracts

Contingent convertibles (CoCos) are a new loss-absorbing bond class which receive high interest from financial regulators. When developing models to price these Cocos, it is important to see which factors drive the CoCo's value. Possible factors are the price of the stock on which the Coco is based, the interest rate and the credit default swap spread, but other factors can also be included. A way to look at this sensitivity is by fitting a regression model with multiple factors as covariates based on historical data. We perform a full regression analysis to see which factors are relevant when determining the CoCo price and should therefore be included in our pricing model. Hereby, it is also necessary to study the interaction between included factors. When developing models for different CoCo types, we can determine which types are sensitive to changes in certain factors (such as the volatility) which leads to a differentiation between CoCo types based on these market parameters. To compensate for possible outliers in the data, we also focus on robust regression techniques. The use of these techniques also allows us to identify deviating trading periods.