A set-valued Markov chain approach to credit default

We propose a novel credit default model that takes into account the impact of macroeconomic factors and intergroup contagion on the defaults of obligors. We use a set-valued Markov chain to model the default process, which includes all defaulted obligors in the group. We obtain analytic characterizations for the default process and derive pricing formulas in explicit forms for synthetic collateralized debt obligations (CDOs). Furthermore, we use market data to calibrate the model and conduct numerical studies on the tranche spreads of CDOs. We find evidence to support that systematic default risk coupled with default contagion could have the leading component of the total default risk.     

On break-even correlation: the way to price structured credit derivatives by replication

We consider the pricing of European-style structured credit pay-off under the Gaussian Copula Model (GCM). When no sudden jump-to-default events occur, the perfect replication of these pay-offs under the GCM is obtained if and only if the underlying single-name credit spreads follow a particular family of dynamics and if the pricing parameters are given by so-called ‘break-even’ correlations. We exhibit a class of Merton-style models that are consistent with this result. We calculate break-even correlations explicitly to price nth-to-default baskets under the GCM. Finally, we illustrate the usefulness of this concept as a relative-value tool.     

On long-term credit risk assessment and rating: towards a new set of models

Institutional investors are supposed to assess credit risk by using a combination of quantitative information such as option models and qualitative assessments. Although option models can be easily constructed, they are not so suitable for the assessment of long-term credit risk that is required by institutional investors. This is mainly because the probability of bankruptcy varies so widely depending on the timing of assessment. We propose a new set of assessment models for long-term credit risk which does not necessarily use stock prices and may incorporate business cycles. The new grand model consists of the two pillars: a long-term cash flow prediction model and a credit risk spread assessment model. The calculated values derived from these models are effectively usable for reasonable calculation of risk spreads. It is quite interesting to see that our investigation indicates that rating bias may exist in the credit risk assessment of the market.     

Spatial statistical modelling of insurance risk: a spatial epidemiological approach to car insurance

Spatial models, such as the Besag, York and Mollie (BYM) model, have long been used in epidemiology and disease mapping. A common research question in these subjects is modelling the number of disease events per region; here the BYM models provides a holistic framework for both covariates and dependencies between regions. We use these tools to assess the relative insurance risk associated with the policyholders geographical location. A Bayesian modelling approach is presented and an elastic net is used to reduce the large number of possible geographic covariates. The final inference is performed using Integrated Nested Laplace Approximation. The model is applied to car insurance data from If P&C Insurance together with spatially referenced covariate data of high resolution, provided by Insightone. The entire analysis is performed using freely available R-packages. Including spatial dependence when modelling the number of claims significantly improves on the result obtained using ordinary generalised linear models. However, the support for adding a spatial component to the model for claims cost is weaker.     

The new political importance of the old hurricane risk: a contextual approach to understanding contemporary struggles with hurricane risk and insurance

In the United States, policy-makers struggle to resolve conflict between public demands for affordable insurance costs covering hurricanes and market demands for risk-based insurance pricing. Given the socially constructed nature of risk, a risk-based pricing approach prioritizes insurer values and business practices over all societal value goals expressly limiting democratic inclusion in decision-making about risk. As a step towards the more democratically inclusive approach of risk governance, this article uses the state of Florida as a case study to provide a narrative of the social and political context for the evolution of the idea of U.S. hurricane risk. I argue that today’s hurricane risk is a product of long-standing shared efforts to build prosperity. However, it is no longer a simple risk for society to overcome on its way to economic well-being. Contemporary hurricane risk is systemic and serves as a nexus for political battles over American values.     

Contagion risk in the interbank market: a probabilistic approach to cope with incomplete structural information

One lesson of the financial crisis erupting in 2008 has been that domino effects constitute a serious threat to the stability of the financial sector, i.e. the failure of one node in the interbank network might entail the danger of contagion to large parts of the entire system. How important this effect is, depends on the exact topology of the network on which the supervisory authorities have typically very incomplete knowledge. In order to explore the extent of contagion effects and to analyse the effectiveness of macroprudential measures to contain such effects, a reconstruction of the quantitative features of the empirical network would be needed. We propose a probabilistic approach to such a reconstruction: we propose to combine some important known quantities (like the size of the banks) with a realistic stochastic representation of the remaining structural elements. Our approach allows us to evaluate relevant measures for the contagion risk after default of one unit (i.e. the number of expected subsequent defaults, or their probabilities). For some quantities we are able to derive closed form solutions, others can be obtained via computational mean-field approximations.     

The decision game: a serious game approach to understanding environmental risk management decisions

Abstract

Here we describe the implementation of an experimental research tool called the Decision Game that we used to collect data on household flood risk management decisions. Participants using this tool play an interactive game that involves making household decisions about place of residence and a variety of household expenditures, including spending on flood insurance and private flood risk mitigation. Participants also answer survey questions before and after playing the game; the pre-game survey collected demographic information, and the post-game survey collected information about participant experiences with flooding and flood mitigation. Online and face-to-face participants showed similar engagement with the experiment, and most participants appeared to have made deliberate and considered decisions about risk mitigation. Online study participants had similar responses to those who participated in person, although face-to-face participants seemed slightly more likely to mitigate against risk. Overall, participants in this research were younger, more educated and more likely to rent a home than the average Canadian. Serious games may be useful for augmenting existing data gathering strategies used in understanding environmental decision making, particularly for rare catastrophic events for which stated preference surveys may be less informative. Serious games allow for sharing a mixture of information with study participants, including maps, video clips, text and even immersive 3D experiences, and can be administered online to increase participation levels. Future research will consider longer duration online experiments and more immersive interaction frameworks.