Pricing First-to-Default Credit Protection with a Gaussian Copula

Summary

The document raises a pricing question about a first-to-default contract on two names with a specified default correlation, using the Li model and a Gaussian copula. The author has tried generating Gaussian samples and correlating them with a linear transformation, but remains unsure how to calculate the fee-leg rate.

No answer or pricing formula is provided, so the note does not resolve the fee-leg calculation or give evidence for a particular approach. It identifies the modeling setup and the practical point of uncertainty, but leaves important details open, including the names’ default probabilities, contract terms, and how simulated defaults translate into fee payments. Readers should treat it as an unanswered modeling question rather than a complete valuation method.

Key ideas

• The question concerns first-to-default pricing for a two-name basket under the Li model and a Gaussian copula.
• The assumed default correlation between the names is specified.
• Correlated Gaussian sampling is proposed, but the fee-leg rate remains unresolved.
• The document provides no answer, valuation result, or supporting evidence.

Tags

# First to Default


# First to Default












Hi I would like a clarification on how to price a first to default over a basket of two names with correlation rho=0.2 via Li Model using Gaussian Copula. Up to now I've tried to extract a gaussian sample and correlate it via a linear transformation. But I still have issues on the rate to use on the fee leg when pricing.

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