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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.
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
This paper makes an attempt to present the economics of credit securitization in a non-technical way, starting from the description and the analysis of a typical securitization transaction. The paper sketches a theoretical explanation for why tranching, or nonproportional risk sharing, which is at the heart of securitization transactions, may allow commercial banks to maximize their shareholder value. However, the analysis makes also clear that the conditions under which credit securitization enhances welfare, are fairly restrictive, and require not only an active role of the banking supervisiory authorities, but also a price tag on the implicit insurance currently provided by the lender of last resort. Klassifikation: D82, G21, D74. February 16, 2005.
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.
This paper analyzes banks' choice between lending to firms individually and sharing lending with other banks, when firms and banks are subject to moral hazard and monitoring is essential. Multiple-bank lending is optimal whenever the benefit of greater diversification in terms of higher monitoring dominates the costs of free-riding and duplication of efforts. The model predicts a greater use of multiple-bank lending when banks are small relative to investment projects, firms are less profitable, and poor financial integration, regulation and inefficient judicial systems increase monitoring costs. These results are consistent with empirical observations concerning small business lending and loan syndication. JEL Klassifikation: D82; G21; G32.
This paper analyzes the empirical relationship between credit default swap, bond and stock markets during the period 2000-2002. Focusing on the intertemporal comovement, we examine weekly and daily lead-lag relationships in a vector autoregressive model and the adjustment between markets caused by cointegration. First, we find that stock returns lead CDS and bond spread changes. Second, CDS spread changes Granger cause bond spread changes for a higher number of firms than vice versa. Third, the CDS market is significantly more sensitive to the stock market than the bond market and the magnitude of this sensitivity increases when credit quality becomes worse. Finally, the CDS market plays a more important role for price discovery than the corporate bond market. JEL Klassifikation: G10, G14, C32.
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.
Ambivalence in the regulatory definition of capital adequacy for credit risk has recently stirred the financial services industry to collateral loan obligations (CLOs) as an important balance sheet management tool. CLOs represent a specialised form of Asset-Backed Securitisation (ABS), with investors acquiring a structured claim on the interest proceeds generated from a portfolio of bank loans in the form of tranches with different seniority. By way of modelling Merton-type risk-neutral asset returns of contingent claims on a multi-asset portfolio of corporate loans in a CLO transaction, we analyse the optimal design of loan securitisation from the perspective of credit risk in potential collateral default. We propose a pricing model that draws on a careful simulation of expected loan loss based on parametric bootstrapping through extreme value theory (EVT). The analysis illustrates the dichotomous effect of loss cascading, as the most junior tranche of CLO transactions exhibits a distinctly different default tolerance compared to the remaining tranches. By solving the puzzling question of properly pricing the risk premium for expected credit loss, we explain the rationale of first loss retention as credit risk cover on the basis of our simulation results for pricing purposes under the impact of asymmetric information. Klassifikation: C15, C22, D82, F34, G13, G18, G20
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.
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.
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.
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
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.
This paper uses a unique data set from credit files of six leading German banks to provide some empirical insights into their rating systems used to classify corporate borrowers. On the basis of the New Basle Capital Accord, which allows banks to use their internal rating systems to compute their minimum capital requirements, the relations between potential risk factors, rating decisions and the default probabilities are analysed to answer the question whether German banks are ready for the internal ratings-based approach. The results suggests that the answer is not affirmative at this stage. We find internal rating systems not comparable over banks and furthermore we reveal differences between credit rating determining and default probability determining factors respectively. Klassifikation: G21, G33, G38
In recent years new methods and models have been developed to quantify credit risk on a portfolio basis. CreditMetrics (tm), CreditRisk+, CreditPortfolio (tm) are among the best known and many others are similar to them. At first glance they are quite different in their approaches and methodologies. A comparison of these models especially with regard to their applicability on typical middle market loan portfolios is in the focus of this study. The analysis shows that differences in the results of an application of the models on a certain loan portfolio is mainly due to different approaches in approximating default correlations. That is especially true for typically non-rated medium-sized counterparties. On the other hand distributional assumptions or different solution techniques in the models are more or less compatible.
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.
This paper discusses the role of the credit rating agencies during the recent financial crises. In particular, it examines whether the agencies can add to the dynamics of emerging market crises. Academics and investors often argue that sovereign credit ratings are responsible for pronounced boom-bust cycles in emerging-markets lending. Using a vector autoregressive system this paper examines how US dollar bond yield spreads and the short-term international liquidity position react to an unexpected sovereign credit rating change. Contrary to common belief and previous studies, the empirical results suggest that an abrupt downgrade does not necessarily intensify a financial crisis.
During the last years the lending business has come under considerable competitive pressure and bank managers often express concern regarding its profitability vis-a-vis other activities. This paper tries to empirically identify factors that are able to explain the financial performance of bank lending activities. The analysis is based on the CFS-data-set that has been collected in 1997 from 200 medium-sized firms. Two regressions are performed: The first is directed towards relationships between the interest rate premiums and various determining factors, the second aims at detecting relationships between those factors and the occurrence of several types of problems during the course of a credit engagement. Furthermore, the results of both regressions are used to test theoretical hypotheses regarding the impact of certain parameters on credit terms and distress probabilities. The findings are somewhat “puzzling“: First, the rating is not as significant as expected. Second, credit contracts seem to be priced lower for situations with greater risks. Finally, the results do not fully support any of three hypotheses that are often advanced to describe the role of collateral and covenants in credit contracts.