Version 1
: Received: 20 May 2017 / Approved: 22 May 2017 / Online: 22 May 2017 (08:21:59 CEST)
How to cite:
Brummelhuis, R.; Luo, Z. CDS Rate Construction Methods by Machine Learning Techniques. Preprints2017, 2017050162. https://doi.org/10.20944/preprints201705.0162.v1
Brummelhuis, R.; Luo, Z. CDS Rate Construction Methods by Machine Learning Techniques. Preprints 2017, 2017050162. https://doi.org/10.20944/preprints201705.0162.v1
Brummelhuis, R.; Luo, Z. CDS Rate Construction Methods by Machine Learning Techniques. Preprints2017, 2017050162. https://doi.org/10.20944/preprints201705.0162.v1
APA Style
Brummelhuis, R., & Luo, Z. (2017). CDS Rate Construction Methods by Machine Learning Techniques. Preprints. https://doi.org/10.20944/preprints201705.0162.v1
Chicago/Turabian Style
Brummelhuis, R. and Zhongmin Luo. 2017 "CDS Rate Construction Methods by Machine Learning Techniques" Preprints. https://doi.org/10.20944/preprints201705.0162.v1
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.
Computer Science and Mathematics, Information Systems
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
