Martin Hibbeln

Improvements in Loss Given Default Forecasts for Bank Loans

An accurate forecast of the parameter loss given default (LGD) of loans plays a crucial role for risk-based decision making by banks. We theoretically analyze problems arising when forecasting LGDs of bank loans that lead to inconsistent estimates and a low predictive power. We present several improvements for LGD estimates, considering length-biased sampling, different loan characteristics depending on the type of default end, and different information sets according to the default status. We empirically demonstrate the capability of our proposals based on a data set of 69,985 defaulted bank loans. Our results are not only important for banks, but also for regulators, because neglecting these issues leads to a significant underestimation of capital requirements.

The Impact of the Financial Crisis and Natural Catastrophes on CAT Bonds

CAT bonds are important instruments for the insurance of catastrophe risk. Due to a low degree of deal standardization, there is uncertainty about the determination of the CAT bond premium. In addition, it is not apparent how CAT bonds react after the financial crisis or a natural catastrophe. We empirically verify which factors determine the CAT bond premium and what effects arise if a catastrophe occurs. On a broad data set using secondary market premiums we find strong evidence that the recent financial crisis has a significant impact on CAT bond premiums. Furthermore, we find that after hurricane Katrina an increased risk perception for hurricanes can be observed.

How Is Your User Feeling? Inferring Emotion Through Human-Computer Interaction Devices

City University of Hong Kong [7002626, 7004123]; Research Grants Council of the Hong Kong Special Administrative Region [CityU149512]

Measuring concentration risk for regulatory purposes

The measurement of concentration risk in credit portfolios is necessary for the determination of regulatory capital under Pillar 2 of Basel II as well as for managing portfolios and allocating economic capital. Existing multi-factor models that deal with concentration risk are often inconsistent with the Pillar 1 capital requirements. Therefore, we adjust these models to achieve Basel II-compliant results. Within a simulation study we test the impact of sector concentrations on several portfolios and contrast the accuracy of the different models. In this context, we also compare Value at Risk and Expected Shortfall regarding their suitability to assess concentration risk.

The Impact of the Financial Crisis and Natural Catastrophes on CAT Bonds: The Impact of the Financial Crisis and Natural Catastrophes on CAT Bonds

This article employs secondary market data to examine how natural catastrophes or financial crises affect CAT bond premiums. We find evidence that both the financial crisis and Hurricane Katrina significantly affected CAT bond premiums. The premium increase resulting from natural catastrophes can primarily be attributed to an increased coefficient of expected loss calculated by catastrophe modeling companies. Furthermore, our results indicate a positive relationship between corporate spreads and CAT bond premiums. Thus, CAT bonds should not be regarded as “zero�?beta�? securities. Moreover, our results indicate that deal complexity, ratings, and the reinsurance cycle are significant drivers of CAT bond premiums.

Informational Synergies in Consumer Credit

Lenders can consider multiple sources of private information to assess consumer credit risk but little is known about the informational synergies between these sources. Using more than 1.7 million observations from checking accounts and credit card accounts of the same individuals during 2007-2014, we find that activity from both accounts contains information beyond credit scores and other controls. Checking accounts display warning indications earlier and more accurately than credit card accounts. Type I default prediction errors decrease by 33% when lenders consider information from both sources. The evidence suggests sizeable informational synergies that lenders can exploit to manage credit relationships.

Reply to 'Comment on 'Markowitz Versus Michaud: Portfolio Optimization Strategies Reconsidered''

In response to the comment by Michaud/Michaud/Esch (2015) on our article “Markowitz versus Michaud: Portfolio Optimization Strategies Reconsidered”, we present clarifications.

Do Investors Consider Asymmetric Information in Pricing Securitizations

Two factors have proven to be strongly relevant for the subprime mortgage crisis. The first is the lack of screening incentives of originators, which had not been anticipated by investors. The second is that investors relied too much on credit ratings. We examine whether investors have learned from these shortcomings. On the basis of securitizations from 2010 and 2011, we find that investors require a significantly higher risk premium when there is a high degree of asymmetric information. The credit spreads of information sensitive tranches are significantly higher if originators do not retain a part of the securitization or if they choose vertical slice retention instead of retaining the equity tranche. Moreover, the relevance of credit ratings in comparison to other credit factors has significantly decreased. Apparently, investors mainly consider ratings to discriminate between information sensitive and information insensitive tranches, beyond that they rely on their own risk analysis. This suggests that investors have learned their lesson from the subprime mortgage crisis.We examine whether investors of securitizations anticipate screening and monitoring incentives of originators. Theoretical literature suggests that equity retention leads to a maximization of screening efforts; thus, equity retention should result in a reduction of credit spreads if investors anticipate these incentives. Implementing an instrumental variable approach, we infer the causal effect of retention on spreads. Using a unique data set of securitizations, we find for information sensitive tranches, for which screening and monitoring incentives have the greatest impact, that investors require an additional risk premium of 120 basis points if originators do not retain a material share of securitizations. Furthermore, deals with vertical slice retention have a significantly higher risk premium compared to deals with equity retention.

Model-Based Measurement of Name Concentration Risk in Credit Portfolios

The focus of this chapter is on sector concentrations. This type of concentration risk can occur if there is more than one systematic risk factor that influences credit defaults. The main research questions that are analyzed in this chapter are: How can existing approaches for measuring sector concentration risk be modified and adjusted to be consistent with the Basel framework? Is the risk measure Value at Risk problematic when dealing with sector concentration risk? Which methods are capable of measuring concentration risk and how good do they perform in comparison? What are the advantages and disadvantages of these methods? In order to deal with these questions, it is initially determined how a multi-factor model can be parameterized to obtain a capital requirement which is consistent with Basel II. Furthermore, the models of Pykhtin (Risk 17(3):85–90, 2004), Cespedes et al. (J Credit Risk, 2(3):57–85, 2006), and Dullmann (Measuring business sector concentration by an infection model. Discussion Paper, Series 2: Banking and Financial Studies, Deutsche Bundesbank, (3), 2006), which have been developed to approximate the risk in the presence of sector concentrations, are presented and modified. Then, the accuracy of these models concerning their ability to measure sector concentration risk is compared.

Credit Risk Measurement in the Context of Basel II

In Chap. 2, the fundamentals of credit risk measurement and the quantitative framework of Basel II are presented. At first, the need of banking regulation in general, the development of banking supervision, as well as the concept of Basel II is presented briefly. Furthermore, the risk measures VaR and ES are introduced, which are the most common characteristic numbers for measuring risk in credit portfolios. In this context, the emphasis is put on the (non-)coherency and estimation issues. Then, the asset value model of Merton (J Fin 29(2):449–470, 1974), the one-factor model of Vasicek (Probability of Loss on Loan Portfolio. KMV Corporation, San Francisco), and the ASRF model of Gordy (J Fin Intermed 12(3):199–232, 2003) are presented. These models build the fundament of the IRB Approach of Basel II, which is explained subsequently.