CDS Rate Construction Methods by Machine Learning Techniques

Raymond Brummelhuis and Zhongmin Luo ()

MPRA Paper from University Library of Munich, Germany

Abstract: Regulators require financial institutions to estimate counterparty default risks from liquid CDS quotes for the valuation and risk management of OTC derivatives. However, the vast majority of counterparties do not have liquid CDS quotes and need proxy CDS rates. Existing methods cannot account for counterparty-specific default risks; we propose to construct proxy CDS rates by associating to illiquid counterparty liquid CDS Proxy based on Machine Learning Techniques. After testing 156 classifiers from 8 most popular classifier families, we found that some classifiers achieve highly satisfactory accuracy rates. Furthermore, we have rank-ordered the performances and investigated performance variations amongst and within the 8 classifier families. This paper is, to the best of our knowledge, the first systematic study of CDS Proxy construction by Machine Learning techniques, and the first systematic classifier comparison study based entirely on financial market data. Its findings both confirm and contrast existing classifier performance literature. Given the typically highly correlated nature of financial data, we investigated the impact of correlation on classifier performance. The techniques used in this paper should be of interest for financial institutions seeking a CDS Proxy method, and can serve for proxy construction for other financial variables. Some directions for future research are indicated.

Keywords: Machine Learning; Counterparty Credit Risk; CDS Proxy Construction; Classification. (search for similar items in EconPapers)
JEL-codes: B23 C1 C38 C4 C45 C58 C6 (search for similar items in EconPapers)
Date: 2017-05-12
New Economics Papers: this item is included in nep-cmp and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Related works:
Working Paper: CDS Rate Construction Methods by Machine Learning Techniques (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:79194

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