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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.
The recent financial crisis has highlighted the limits of the “originate to distribute” model of banking, but its nexus with the macroeconomy and monetary policy remains unexplored. I build a DSGE model with banks (along the lines of Holmström and Tirole [28] and Parlour and Plantin [39] and examine its properties with and without active secondary markets for credit risk transfer. The possibility of transferring credit reduces the impact of liquidity shocks on bank balance sheets, but also reduces the bank incentive to monitor. As a result, secondary markets allow to release bank capital and exacerbate the effect of productivity and other macroeconomic shocks on output and inflation. By offering a possibility of capital recycling and by reducing bank monitoring, secondary credit markets in general equilibrium allow banks to take on more risk. Keywords: Credit Risk Transfer , Dual Moral Hazard , Monetary Policy , Liquidity , Welfare JEL Classification: E3, E5, G3 First Draft: December 2009, This Draft: September 2010
Multiple lenders and corporate distress: evidence on debt restructuring : [Version Juni 2006]
(2006)
In the recent theoretical literature on lending risk, the coordination problem in multi-creditor relationships have been analyzed extensively. We address this topic empirically, relying on a unique panel data set that includes detailed credit-file information on distressed lending relationships in Germany. In particular, it includes information on creditor pools, a legal institution aiming at coordinating lender interests in borrower distress. We report three major findings. First, the existence of creditor pools increases the probability of workout success. Second, the results are consistent with coordination costs being positively related to pool size. Third, major determinants of pool formation are found to be the number of banks, the distribution of lending shares, and the severity of the distress shock.
Some have argued that recent increases in credit risk transfer are desirable because they improve the diversification of risk. Others have suggested that they may be undesirable if they increase the risk of financial crises. Using a model with banking and insurance sectors, we show that credit risk transfer can be beneficial when banks face uniform demand for liquidity. However, when they face idiosyncratic liquidity risk and hedge this risk in an interbank market, credit risk transfer can be detrimental to welfare. It can lead to contagion between the two sectors and increase the risk of crises. Klassifikation: G21, G22
Im Mittelpunkt dieses Beitrag stehen Verweildauermodelle und deren Verwendung als Analyseinstrumente für die Bewertung und Berechnung von Kreditausfallrisiken. Verschiedene Möglichkeiten zur Berechnung der Dauer des Nichtausfalls eines Kredites werden dabei vorgestellt. Die hier vorgestellten Verfahren werden auf einen aus Kreditakten von sechs deutschen Universalbanken zusammengestellten Datensatz angewendet. Beispiele und Interpretationshilfen zu den jeweils vorgestellten Methoden erleichtern den Zugang zu diesen Modellen. Es werden zahlreiche Hinweise auf weiterführende Literatur gegeben.
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.