Credit derivatives: the past, the present and the future
The determinants of credit spread returns
What’s driving the default swap basis?
What is the value of modified restructuring?
The debt and equity linkage and the valuation of credit derivatives
Nth to default swaps and notes: all about default correlation
Portfolio credit risk models
Credit derivatives as an efficient way of transitioning to optimal portfolios
Overview of the CDO market
Synthetic securitisation and structured portfolio credit derivatives
Integrating credit derivatives and securitisation technology: the collateralised synthetic obligation
Considerations for dynamic and static, cash and synthetic collateralised debt obligations
CDOs of CDOs: art eating itself?
Valuation and risk analysis of synthetic collateralised debt obligations: a copula function approach
Extreme events and multi-name credit derivatives
Reduced-form models: curve construction and the pricing of credit swaps, options and hybrids
Modelling and hedging of default risk
ISDA’s role in the credit derivatives marketplace
Credit linked notes
Using guarantees and credit derivatives to reduce credit risk capital requirements under the New Basel Capital Accord
The dependence structure of asset returns lies at the heart of a class of models widely employed for the valuation of multi-name credit derivatives as we saw in the last chapter. In this chapter, we study the dependence structure of asset returns using copula functions. Employing a statistical methodology that relies on a minimal amount of distributional assumptions, we first investigate whether the popular tenet of Normal dependence between asset returns is supported on the basis of empirical observations. We also compare the dependence structures of asset and equity returns to provide some insight into the common practice of estimating the former using equity data. Our results show that the presence of joint extreme events in the data is incompatible with the assumption of Normal dependence, and support the use of equity returns as proxies for asset returns. Furthermore, we present evidence that the likelihood of joint extreme events does not diminish as we decrease the sampling frequency of our observations. Building on our empirical findings, we then describe how to capture the effects of joint extreme events by means of a simple and computationally efficient time-to-default