TY - JOUR T1 - CDS Proxy Construction via Machine Learning Techniques—<em>Part II: Parametrization, Correlation, Benchmarking</em> JF - The Journal of Financial Data Science SP - 128 LP - 151 DO - 10.3905/jfds.2019.1.2.128 VL - 1 IS - 2 AU - Raymond Brummelhuis AU - Zhongmin Luo Y1 - 2019/04/30 UR - https://pm-research.com/content/1/2/128.abstract N2 - This is the second of two articles by the authors on the construction of credit default swap (CDS) proxy rates. In the first article, the authors proposed a machine learning (ML)-based proxy-rate construction technique that 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 a 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 parametrizations, 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.TOPICS: Credit default swaps, big data/machine learning, performance measurement ER -