场外衍生品的中央结算:双边结算与多边结算

IF 1.3 Q2 STATISTICS & PROBABILITY
R. Cont, Thomas Kokholm
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引用次数: 139

摘要

摘要本文研究了场外交易(OTC)的中央清算对交易对手风险敞口的影响,其中场外交易涉及多个具有异构特征的资产类别。引入中央交易对手(CCP)对预期交易商间风险敞口的影响取决于交易商之间的多边净额和资产类别之间的双边净额之间的权衡。我们发现,就资产类别的“风险”以及跨资产类别风险敞口的相关性而言,这种权衡对资产类别异质性的假设很敏感。特别是,虽然假设独立、同质风险敞口的分析表明,只有当参与者的数量高得不切实际时,中央清算才有效,但如果实际考虑到资产类别之间的风险和相关性差异,则得出相反的结论。我们认为,经验上合理的模型参数规范得出的结论是,中央清算确实减少了交易商之间的风险敞口:CCP中多边净额的收益超过了双边净额协议中跨资产类别净额的损失。当利率衍生品存在CCP时,为信用衍生品添加CCP会减少总体风险敞口。这些发现对于模型的统计假设以及用于量化暴露的风险度量的选择是稳健的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Central clearing of OTC derivatives: Bilateral vs multilateral netting
Abstract We study the impact of central clearing of over-the-counter (OTC) transactions on counterparty exposures in a market with OTC transactions across several asset classes with heterogeneous characteristics. The impact of introducing a central counterparty (CCP) on expected interdealer exposure is determined by the tradeoff between multilateral netting across dealers on one hand and bilateral netting across asset classes on the other hand. We find this tradeoff to be sensitive to assumptions on heterogeneity of asset classes in terms of `riskyness' of the asset class as well as correlation of exposures across asset classes. In particular, while an analysis assuming independent, homogeneous exposures suggests that central clearing is efficient only if one has an unrealistically high number of participants, the opposite conclusion is reached if differences in riskyness and correlation across asset classes are realistically taken into account. We argue that empirically plausible specifications of model parameters lead to the conclusion that central clearing does reduce interdealer exposures: the gain from multilateral netting in a CCP overweighs the loss of netting across asset classes in bilateral netting agreements. When a CCP exists for interest rate derivatives, adding a CCP for credit derivatives is shown to decrease overall exposures. These findings are shown to be robust to the statistical assumptions of the model as well as the choice of risk measure used to quantify exposures.
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来源期刊
Statistics & Risk Modeling
Statistics & Risk Modeling STATISTICS & PROBABILITY-
CiteScore
1.80
自引率
6.70%
发文量
6
期刊介绍: Statistics & Risk Modeling (STRM) aims at covering modern methods of statistics and probabilistic modeling, and their applications to risk management in finance, insurance and related areas. The journal also welcomes articles related to nonparametric statistical methods and stochastic processes. Papers on innovative applications of statistical modeling and inference in risk management are also encouraged. Topics Statistical analysis for models in finance and insurance Credit-, market- and operational risk models Models for systemic risk Risk management Nonparametric statistical inference Statistical analysis of stochastic processes Stochastics in finance and insurance Decision making under uncertainty.
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