CDS Proxy Construction via Machine Learning Techniques Part II: Parametrization, Correlation, Benchmarking
Journal of Financial Data Science, Vol. No. 2, 2019
Posted: 6 Mar 2019 Last revised: 15 May 2019
Date Written: November 5, 2018
This article is the second of two articles by the authors on the construction of CDS proxy rates. In the first article, the authors proposed a machine learning (ML) -based proxy-rate construction technique which uses classification to construct so-called Proxy-Names whose liquidly quoted CDS rates can be used as CDS proxy rates. The authors then compared the performances of ML classifiers from the eight most popular classifier families as function of carefully selected sets of feature variables. In this second article, the authors take a closer look at the performances of the individual classifiers as a function of their different parametrisations, which they refer to as an Intra-classifier performance study. The authors also examine the effects of feature variable correlations on classifier performance, and perform a benchmarking exercise by comparing the ML-based CDS-proxy technique with two currently used proxy-rate construction methods: Curve Mapping and Cross-Sectional Regression.
Keywords: CDS Proxy Construction, Counterparty Credit Risk, Machine Learning, Classification
JEL Classification: C4, C45, C63
Suggested Citation: Suggested Citation