CDS Rate Construction Methods by Machine Learning Techniques
This provides a method for financial institutions to estimate counterparty-specific default risks where liquid CDS quotes are unavailable, though it is incremental as it applies existing ML techniques to a new financial domain.
The paper tackled the problem of constructing proxy CDS rates for illiquid counterparties to estimate default risks, using machine learning classifiers on financial data, and found that some classifiers achieved highly satisfactory accuracy rates after testing 156 classifiers from 8 families.
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.