Refine
Document Type
- Working Paper (5)
- Conference Proceeding (2)
- Report (1)
Language
- English (8) (remove)
Has Fulltext
- yes (8)
Is part of the Bibliography
- no (8)
Keywords
- Kreditrisiko (8) (remove)
Institute
- Wirtschaftswissenschaften (8) (remove)
Market risks account for an integral part of life insurers' risk profiles. This paper explores the market risk sensitivities of insurers in two large life insurance markets, namely the U.S. and Europe. Based on panel regression models and daily market data from 2012 to 2018, we analyze the reaction of insurers' stock returns to changes in interest rates and CDS spreads of sovereign counterparties. We find that the influence of interest rate movements on stock returns is more than 50% larger for U.S. than for European life insurers. Falling interest rates reduce stock returns in particular for less solvent firms, insurers with a high share of life insurance reserves and unit-linked insurers. Moreover, life insurers' sensitivity to interest rate changes is seven times larger than their sensitivity towards CDS spreads. Only European insurers significantly suffer from rising CDS spreads, whereas U.S. insurers are immunized against increasing sovereign default probabilities.
Rating agencies state that they take a rating action only when it is unlikely to be reversed shortly afterwards. Based on a formal representation of the rating process, I show that such a policy provides a good explanation for the puzzling empirical evidence: Rating changes occur relatively seldom, exhibit serial dependence, and lag changes in the issuers’ default risk. In terms of informational losses, avoiding rating reversals can be more harmful than monitoring credit quality only twice per year.
Evaluating the quality of credit portfolio risk models is an important question for both banks and regulators. Lopez and Saidenberg (2000) suggest cross-sectional resampling techniques in order to make efficient use of available data and to produce measures of forecast accuracy. We first show that their proposal disregards crosssectional dependence in simulated subportfolios, which renders standard statistical inference invalid. We proceed by suggesting another evaluation methodology which draws on the concept of likelihood ratio tests. Specifically, we compare the predictive quality of alternative models by comparing the probabilities that observed data have been generated by these models. The distribution of the test statistic can be derived through Monte Carlo simulation. To exploit differences in cross-sectional predictions of alternative models, the test can be based on a linear combination of subportfolio statistics. In the construction of the test, the weight of a subportfolio depends on the difference in the loss distributions which alternative models predict for this particular portfolio. This makes efficient use of the data, and reduces computational burden. Monte Carlo simulations suggest that the power of the tests is satisfactory.
JEL classification: G2; G28; C52
Evaluating the quality of credit portfolio risk models is an important issue for both banks and regulators. Lopez and Saidenberg (2000) suggest cross-sectional resampling techniques in order to make efficient use of available data. We show that their proposal disregards cross-sectional dependence in resampled portfolios, which renders standard statistical inference invalid. We proceed by suggesting the Berkowitz (1999) procedure, which relies on standard likelihood ratio tests performed on transformed default data. We simulate the power of this approach in various settings including one in which the test is extended to incorporate cross-sectional information. To compare the predictive ability of alternative models, we propose to use either Bonferroni bounds or the likelihood-ratio of the two models. Monte Carlo simulations show that a default history of ten years can be sufficient to resolve uncertainties currently present in credit risk modeling.
Multiple lenders and corporate distress: evidence on debt restructuring : [Version Juli 2002]
(2002)
In the recent theoretical literature on lending risk, the common pool problem in multi-bank relationships has been analyzed extensively. In this paper we address this topic empirically, relying on a unique panel data set that includes detailed credit-fie information on distressed lending relationships in Germany. In particular, it includes information on bank pools, a legal institution aimed at coordinating lender interests in borrower distress. We find that the existence of small bank pools increases the probability of workout success and that coordination costs are positively related to pool size. We identify major determinants of pool formation, in particular the distribution of lending shares among banks, the number of banks, and the severity of the distress shock to the borrower.
Under a new Basel capital accord, bank regulators might use quantitative measures when evaluating the eligibility of internal credit rating systems for the internal ratings based approach. Based on data from Deutsche Bundesbank and using a simulation approach, we find that it is possible to identify strongly inferior rating systems out-of time based on statistics that measure either the quality of ranking borrowers from good to bad, or the quality of individual default probability forecasts. Banks do not significantly improve system quality if they use credit scores instead of ratings, or logistic regression default probability estimates instead of historical data. Banks that are not able to discriminate between high- and low-risk borrowers increase their average capital requirements due to the concavity of the capital requirements function.
This paper makes an attempt to present the economics of credit securitisation in a non-technical way, starting from the description and the analysis of a typical securitisation transaction. The paper sketches a theoretical explanation for why tranching, or nonproportional risk sharing, which is at the heart of securitisation transactions, may allow commercial banks to maximize their shareholder value. However, the analysis makes also clear that the conditions under which credit securitisation enhances welfare, are fairly restrictive, and require not only an active role of the banking supervisory authorities, but also a price tag on the implicit insurance currently provided by the lender of last resort.
We derive the effects of credit risk transfer (CRT) markets on real sector productivity and on the volume of financial intermediation in a model where banks choose their optimal degree of CRT and monitoring. We find that CRT increases productivity in the up-market real sector but decreases it in the low-end segment. If optimal, CRT unambiguously fosters financial deepening, i.e., it reduces credit-rationing in the economy. These effects rely upon the ability of banks to commit to the optimal CRT at the funding stage. The optimal degree of CRT depends on the combination of moral hazard, general riskiness, and the cost of monitoring in non-monotonic ways.