Yalin Gündüz

Predicting credit default swap prices with financial and pure data-driven approaches

The increasing popularity of credit default swaps (CDSs) necessitates understanding their various features. In this study, we analyse the capability of CDSs in predicting CDS prices of other companies in the same risk class or CDS prices of further time horizons. In doing so, we employ the basic forms of structural (the Merton model) and reduced-form (constant intensity) models in a cross-sectional and a time series setup. By utilizing a credit default swap dataset exclusively for estimation and out-of-sample prediction, our study also serves as a comparison between the basic forms of credit risk models. Finally, it contrasts the results with the performance of a new supervised learning forecasting technique, the Support Vector Machines Regression. We show that although the Merton and the constant intensity models handle default timing and interest rates differently, the prediction performance in cross-sectional and time series analyses is, on average, similar. In one-, five-, and 10-step-ahead predictions of time series, the machine learning algorithm significantly outperforms financial models.

WILL GERMAN BANKS EARN THEIR COST OF CAPITAL?: GERMAN BANKS

In recent years, the German banking sector has overcome major challenges such as the global financial crisis and the European debt crisis. This paper analyses a recent development as a particular determinant of the future outlook for the German banking sector. Interest rates are at historically low levels and may remain at these levels for a considerable period of time. Such levels pose a specific challenge to banks which are heavily dependent on interest income, as is the case for most German banks. We consider different interest rate scenarios and analyse the extent to which they cause a further narrowing of the interest rate margin. Our results indicate that a projected decline in this margin will result in no more than 20% of German banks earning a cost of capital of 8% by the end of this decade. This decline is somewhat alleviated by the fact that German banks can apply a special feature of German accounting standards by using hidden and open reserves.

Practical Applications of Market Transparency and the Marking Precision of Bond Mutual Fund Managers

Recent research in The Journal of Portfolio Management investigates whether the introduction of TRACE—the US corporate bond pricing program run by the Financial Industry Regulatory Authority (FINRA) —has improved bond-valuation precision and price transparency. This report highlights the practical applications of the research findings. It is based on an in-depth interview with co-author John Merrick, Jr . of the College of William and Mary Raymond A. Mason School of Business .“ Our story here is that market transparency matters, in terms of improving valuations,” he says. Merrick and his co-authors, Gjergji Cici and Scott Gibson , (colleagues at the College of William and Mary), and Yalin Gündüz (of Deutsche Bundesbank ), present their findings in Market Transparency and the Marking Precision of Bond Mutual Fund Managers .