Efficient Monte Carlo Counterparty Credit Risk Pricing and Measurement

Samim Ghamami, Bo Zhang
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引用次数: 13

Abstract

Counterparty credit risk (CCR), a key driver of the 2007-08 credit crisis, has become one of the main focuses of the major global and U.S. regulatory standards. Financial institutions invest large amounts of resources employing Monte Carlo simulation to measure and price their counterparty credit risk. We develop efficient Monte Carlo CCR estimation frameworks by focusing on the most widely used and regulatory-driven CCR measures: expected positive exposure (EPE), credit value adjustment (CVA), and effective expected positive exposure (EEPE). Our numerical examples illustrate that our proposed efficient Monte Carlo estimators outperform the existing crude estimators of these CCR measures substantially in terms of mean square error (MSE). We also demonstrate that the two widely used sampling methods, the so-called Path Dependent Simulation (PDS) and Direct Jump to Simulation date (DJS), are not equivalent in that they lead to Monte Carlo CCR estimators which are drastically different in terms of their MSE.
有效的蒙特卡洛交易对手信用风险定价与计量
交易对手信用风险(CCR)是2007-08年信贷危机的主要驱动因素之一,已成为全球和美国主要监管标准的主要关注点之一。金融机构投入大量资源,利用蒙特卡罗模拟来衡量和定价其交易对手的信用风险。我们通过关注最广泛使用和监管驱动的CCR措施:预期正敞口(EPE),信贷价值调整(CVA)和有效预期正敞口(EEPE),开发了有效的蒙特卡罗CCR估计框架。我们的数值示例表明,我们提出的有效蒙特卡罗估计器在均方误差(MSE)方面大大优于这些CCR度量的现有粗估计器。我们还证明了两种广泛使用的采样方法,即所谓的路径相关模拟(PDS)和直接跳转到模拟日期(DJS),它们并不等效,因为它们导致蒙特卡罗CCR估计量在MSE方面有很大不同。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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