The liquidity premium in CDS transaction prices: Do frictions matter? CFR Working Paper,

Research QuestionThis paper analyses the effect of a sustained period of low interest rates on the outlook for the German banking sector. Low interest rates provide a particular challenge for German banks, which are highly dependent on interest income and exhibit relatively high cost-income ratios.It is thus an open question whether German banks will manage to earn their cost of capital in this environment. ContributionWe analyse the interest earnings from loans and the interest expenses for deposits, i.e. the core business interest margin of a bank. We consider different future interest rate scenarios and analyse the extent to which they cause a further narrowing of the core business interest margin. Finally, we test whether a special feature of German accounting standards could serve as a buffer in sustaining profitability for some time. ResultsOur results indicate that a sustained period of low interest rates will increase the pressure on the core business interest margin earned by German banks. Even if interest rates stayed constant at current levels, the core business interest margin of German banks would be reduced by 16% over the next four years. Moreover, this projected decline in the core business interest margin will result in only 20% of German banks earning a cost of capital of 8% by the end of this decade. However, by applying a special feature of German accounting standards and using hidden and open reserves, German banks may alleviate this decline to a certain extent. Nichttechnische Zusammenfassung Fragestellung AbstractIn 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.

Using a unique data set on German banks' loans to the German real economy, we investigate banks' credit risk. This data set includes the volume of loans per bank and industry as well as the corresponding write-downs. Our empirical study for the period 2003-2011 yields the following results: (i) Beyond the nationwide credit loss rate, industry composition, and regional factors, the loans' maturity structure is found to drive the bank-wide loss rates in the credit portfolio. (ii) The nationwide loss rate has the most impact, followed by the maturity structure and the industry composition. (iii) For nationwide banks, these common factors explain about 26% of the time variation in the loss rate of credit portfolios; for regional banks, this percentage is less than eight percent. KeywordsCredit risk, systematic risk, maturity, stress tests JEL Classification G21 Non-technical summaryHow much default events depend on systematic factors which have an impact on entire borrower groups plays a key role in the default risk of credit portfolios. The stronger the influence of such factors is, the less useful is a diversification across a large number of borrowers and the stronger are the fluctuations in portfolio losses over time. As a bank has to use capital to absorb loss fluctuations, it is crucial for both risk managers and regulators to identify systematic factors and be aware of their relative influence.A direct probability estimate of common defaults is inappropriate, as it is in the nature of the credit business that defaults -and let alone common defaults -rarely occur.First, asset value models are used in practice, which recognize the loans as a derivative of the (non-observable) firm value of borrowers. The systematic factors of such models are generally not observable. Second, "intensity-based" models are employed. Their systematic factors can be interpreted as an average default rate in a given sector (a branch of industry, e.g.) at a given time. In both types of model, the borrowers have to be assigned to suitable groups, preferably so that the link between the defaults is as large as possible within the group and as small as possible between the groups. Allocation by industrial sector is usual, but neither exhaustive nor obligatory. In principle, other classification criteria can be just as meaningful. This is the point of departure for our study. We use a Bundesbank dataset, which covers all German on-balance-sheet credit business with the real economy from 2003 to 2011. It contains credit volumes and write-downs for every bank, broken down into borrower categories and maturity bands. In addition, many credit exposures can be assigned to a region. Our empirical model is essentially an intensity-based approach, as we calculate systematic factors as averages of individual write-down rates.We show that up to 26 percent of the temporal variation of bank-specific write-down rates can be explained by four systematic components. Besides the nationwide loss rate, the portfolio composition with respect to indu...

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This study provides a rigorous empirical comparison of structural and reduced-form credit risk frameworks. As major difference we focus on the discriminative modeling of default time. In contrast to previous literature, we calibrate both approaches to bond and equity prices. By using same input data, applying comparable estimation techniques, and assessing the out-of-sample prediction quality on same time series of CDS prices we are able to judge whether empirically the model structure itself makes an important difference. Interestingly, the models' prediction power is quite close on average. Still, the reduced-form approach outperforms the structural for investment-grade names and longer maturities. JEL Classification: G13Keywords: Credit risk, structural models, reduced-form models, default intensity, stationary leverage, credit default swaps. Non-technical SummaryIn the financial industry, applying the Black/Scholes option pricing framework for pricing purposes has been widely accepted as the benchmark model for equity and FX derivatives.However, no single agreed pricing model has emerged that could serve as a benchmark for instruments that are exposed to credit risk. The literature differentiates between structural models that are based on modeling of the evolution of the balance sheet of the issuer, and reduced-form models that specify credit risk exogenously by a default intensity process.Until now, there has been no common agreement in academia and in the financial industry on which model framework better captures credit risk. In previous studies, even when testing the same model, the use of different datasets has contributed to quite different results. This study overcomes this issue by applying the same dataset to structural and reduced-form approaches. Leverage has been used as a key credit risk factor that could be explanatory in both frameworks. By using the same input data, applying comparable estimation techniques and assessing the out-of-sample prediction quality on the same time series of CDS prices, we are able to judge whether empirically the model structure itself makes an important difference. The models' predictive power is quite close on average, indicating that for pricing purposes the modeling type does not matter compared to the input data used. Still, the reduced-form approach outperforms the structural approach for investment-grade names and longer maturities. In contrast, the structural approach performs better for shorter maturities and sub-investment grade names. The study concludes that both frameworks provide CDS price prediction results equally well if a basis of comparison can be provided. These results have implications on choosing appropriate risk measurement techniques in financial markets.