Correlation, Where are You? Where are We? Update on Correlation Modeling

Christian Kamtchueng
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Abstract

During the first decades following Black and Scholes, the quantitative finance have been focus mainly on the modeling of the volatility. Indeed, the expansion of derivatives product brought some liquidity regarding this parameter. The implied volatility is the reflect of market convention for the vanilla premium but are also an extension of the industry focus. If the volatility modelling is difficult, the correlation modelling stage at another level. In fact, many market participant portfolios were attached to correlation risk - market participants such as insurance companies and pension funds - therefore, banks innovated solutions which imply transfer of the risk. A huge range of products such as Dispersion, Correlation Swap, Basket Option, Best Of and Worst Of have been introduced and sold with more or less popularity. Banks were willing to take the risk but they needed more elaborate model in order to capture the correlation risk. If the business pressure was to trade and deal with the residual risk with conventional correlation framework (deterministic term structure), after the default of Lehman Brother, the Trading Desk had to transfer the pressure and challenge quants in order to have the ability to hedge their correlation exposure. In this paper, we enumerate industry innovations on correlation modeling and discuss some improvements and market understanding of it.
相关性,你在哪里?我们在哪里?相关建模的最新进展
在布莱克和斯科尔斯之后的最初几十年里,量化金融主要集中在波动性的建模上。事实上,衍生产品的扩张为这一参数带来了一定的流动性。隐含波动率反映了市场对香草溢价的惯例,但也是行业焦点的延伸。如果波动性建模是困难的,相关性建模阶段在另一个层面。事实上,许多市场参与者的投资组合都与相关风险有关——保险公司和养老基金等市场参与者——因此,银行创新了意味着风险转移的解决方案。大量的产品,如分散,相关互换,篮子期权,最好的和最差的已经推出和销售或多或少受欢迎。银行愿意承担风险,但他们需要更复杂的模型来捕捉相关风险。如果业务压力是使用传统的相关框架(确定性期限结构)进行交易和处理剩余风险,那么在雷曼兄弟违约后,交易部门必须转移压力和挑战量化分析师,以便有能力对冲其相关敞口。本文列举了相关建模的行业创新,并讨论了相关建模的改进和市场理解。
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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