仿射点过程与投资组合信用风险

Eymen Errais, K. Giesecke, L. Goldberg
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引用次数: 350

摘要

本文分析了一类由仿射跳扩散驱动的多变量关联事件定时点过程模型。仿射点过程的组成部分是自激励和交叉激励的,便于描述复杂的事件依赖结构。ode描述了仿射点过程的变换和整数值仿射点过程的概率分布。仿射点过程的矩取封闭形式。这保证了应用程序中高度的计算可追溯性。我们在组合信用风险的背景下说明了这一点,其中公司违约的相关性是主要问题。我们考虑了暴露于相关违约风险的证券的估值,并通过市场校准实验证明了我们的结果的意义。我们证明了一个简单的模型变体可以捕获2008年9月期间指数和分级市场价格隐含的默认聚类,这个月见证了显著的波动。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Affine Point Processes and Portfolio Credit Risk
This paper analyzes a family of multivariate point process models of correlated event timing whose arrival intensity is driven by an affine jump diffusion. The components of an affine point process are self- and cross-exciting and facilitate the description of complex event dependence structures. ODEs characterize the transform of an affine point process and the probability distribution of an integer-valued affine point process. The moments of an affine point process take a closed form. This guarantees a high degree of computational tractability in applications. We illustrate this in the context of portfolio credit risk, where the correlation of corporate defaults is the main issue. We consider the valuation of securities exposed to correlated default risk and demonstrate the significance of our results through market calibration experiments. We show that a simple model variant can capture the default clustering implied by index and tranche market prices during September 2008, a month that witnessed significant volatility.
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