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Mod-Poisson approximation schemes: Applications to credit risk

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  • Pierre-Loic M'eliot
  • Ashkan Nikeghbali
  • Gabriele Visentin

Abstract

We introduce a new numerical approximation method for functionals of factor credit portfolio models based on the theory of mod-$\phi$ convergence and mod-$\phi$ approximation schemes. The method can be understood as providing correction terms to the classic Poisson approximation, where higher order corrections lead to asymptotically better approximations as the number of obligors increases. We test the model empirically on two tasks: the estimation of risk measures ($\mathrm{VaR}$ and $\mathrm{ES}$) and the computation of CDO tranche prices. We compare it to other commonly used methods -- such as the recursive method, the large deviations approximation, the Chen--Stein method and the Monte Carlo simulation technique (with and without importance sampling) -- and we show that it leads to more accurate estimates while requiring less computational time.

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  • Pierre-Loic M'eliot & Ashkan Nikeghbali & Gabriele Visentin, 2022. "Mod-Poisson approximation schemes: Applications to credit risk," Papers 2211.04436, arXiv.org.
  • Handle: RePEc:arx:papers:2211.04436
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    References listed on IDEAS

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    4. Paul Glasserman & Jingyi Li, 2005. "Importance Sampling for Portfolio Credit Risk," Management Science, INFORMS, vol. 51(11), pages 1643-1656, November.
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