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The European low-carbon transition began in the last few decades and is accelerating to achieve net-zero emissions by 2050. This paper examines how climate-related transition indicators of a large European corporate firm relate to its CDS-implied credit risk across various time horizons. Findings show that firms with higher GHG emissions have higher CDS spreads at all tenors, including the 30-year horizon, particularly after the 2015 Paris Agreement, and in prominent industries such as Electricity, Gas, and Mining. Results suggest that the European CDS market is currently pricing, to some extent, albeit small, the exposure to transition risk for a firm across different time horizons. However, it fails to account for a company’s efforts to manage transition risks and its exposure to the EU Emissions Trading Scheme. CDS market participants seem to find challenging to risk-differentiate ETS-participating firms from other firms.
Low risk anomalies?
(2016)
This paper shows theoretically and empirically that beta- and volatility-based low risk anomalies are driven by return skewness. The empirical patterns concisely match the predictions of our model which generates skewness of stock returns via default risk. With increasing downside risk, the standard capital asset pricing model increasingly overestimates required equity returns relative to firms' true (skew-adjusted) market risk. Empirically, the profitability of betting against beta/volatility increases with firms' downside risk. Our results suggest that the returns to betting against beta/volatility do not necessarily pose asset pricing puzzles but rather that such strategies collect premia that compensate for skew risk.
This paper analyzes the risk properties of typical asset-backed securities (ABS), like CDOs or MBS, relying on a model with both macroeconomic and idiosyncratic components. The examined properties include expected loss, loss given default, and macro factor dependencies. Using a two-dimensional loss decomposition as a new metric, the risk properties of individual ABS tranches can directly be compared to those of corporate bonds, within and across rating classes. By applying Monte Carlo Simulation, we find that the risk properties of ABS differ significantly and systematically from those of straight bonds with the same rating. In particular, loss given default, the sensitivities to macroeconomic risk, and model risk differ greatly between instruments. Our findings have implications for understanding the credit crisis and for policy making. On an economic level, our analysis suggests a new explanation for the observed rating inflation in structured finance markets during the pre-crisis period 2004-2007. On a policy level, our findings call for a termination of the 'one-size-fits-all' approach to the rating methodology for fixed income instruments, requiring an own rating methodology for structured finance instruments. JEL Classification: G21, G28
This paper analyzes the risk properties of typical asset-backed securities (ABS), like CDOs or MBS, relying on a model with both macroeconomic and idiosyncratic components. The examined properties include expected loss, loss given default, and macro factor dependencies. Using a two-dimensional loss decomposition as a new metric, the risk properties of individual ABS tranches can directly be compared to those of corporate bonds, within and across rating classes. By applying Monte Carlo Simulation, we find that the risk properties of ABS differ significantly and systematically from those of straight bonds with the same rating. In particular, loss given default, the sensitivities to macroeconomic risk, and model risk differ greatly between instruments. Our findings have implications for understanding the credit crisis and for policy making. On an economic level, our analysis suggests a new explanation for the observed rating inflation in structured finance markets during the pre-crisis period 2004-2007. On a policy level, our findings call for a termination of the 'one-size-fits-all' approach to the rating methodology for fixed income instruments, requiring an own rating methodology for structured finance instruments. JEL Classification: G21, G28 Keywords: credit risk, risk transfer, systematic risk
Risk transfer with CDOs
(2008)
Modern bank management comprises both classical lending business and transfer of asset risk to capital markets through securitization. Sound knowledge of the risks involved in securitization transactions is a prerequisite for solid risk management. This paper aims to resolve a part of the opaqueness surrounding credit-risk allocation to tranches that represent claims of different seniority on a reference portfolio. In particular, this paper analyzes the allocation of credit risk to different tranches of a CDO transaction when the underlying asset returns are driven by a common macro factor and an idiosyncratic component. Junior and senior tranches are found to be nearly orthogonal, motivating a search for the where about of systematic risk in CDO transactions. We propose a metric for capturing the allocation of systematic risk to tranches. First, in contrast to a widely-held claim, we show that (extreme) tail risk in standard CDO transactions is held by all tranches. While junior tranches take on all types of systematic risk, senior tranches take on almost no non-tail risk. This is in stark contrast to an untranched bond portfolio of the same rating quality, which on average suffers substantial losses for all realizations of the macro factor. Second, given tranching, a shock to the risk of the underlying asset portfolio (e.g. a rise in asset correlation or in mean portfolio loss) has the strongest impact, in relative terms, on the exposure of senior tranche CDO-investors. Our findings can be used to explain major stylized facts observed in credit markets.
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.
Dieser Beitrag stellt verschiedene ökonometrische Methoden zur Bewertung und Berechnung von Kreditausfallrisiken vor und wendet diese auf einen Datensatz sechs deutscher Universalbanken an. Im Mittelpunkt stehen dabei Logit- und Probitmodelle, mit deren Hilfe die Ausfallwahrscheinlichkeit eines Kredites geschätzt werden kann. Dabei werden auch moderne Verfahren zur Analyse von Paneldaten besprochen. Beispiele undInterpretationshilfen zu den jeweils vorgestellten Methoden erleichtern den Zugang zu diesen Modellen. Es werden zahlreiche Hinweise auf weiterführende Literatur gegeben.
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.
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.
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.