TY - JOUR T1 - CDS Proxy Construction via Machine Learning Techniques—<em>Part I: Methodology and Results</em> JF - The Journal of Financial Data Science SP - 111 LP - 127 DO - 10.3905/jfds.2019.1.2.111 VL - 1 IS - 2 AU - Raymond Brummelhuis AU - Zhongmin Luo Y1 - 2019/04/30 UR - https://pm-research.com/content/1/2/111.abstract N2 - To price and risk-manage over-the-counter derivatives, financial institutions have to estimate counterparty default risks based on liquidly quoted credit default swap (CDS) rates. For the vast majority of counterparties, liquid CDS quotes are not available and proxy CDS rates need to be constructed. Existing methods ignore counterparty-specific default risks and can lead to arbitrage. The authors propose to construct CDS proxy rates by machine learning techniques to associate liquidly quoted CDS proxy names to illiquid ones on the basis of observable financial feature variables. A benchmarking exercise shows that the proposed method leads to significantly smaller estimation errors than do existing methods. The authors tested 126 classifiers coming from the eight most popular algorithms, rank-ordered performance, and investigated performance variations among and within the classifiers. In Part I, the authors present the methodology, review the different machine learning techniques, and report on cross-classifier performance. In Part II, they focus on parametrization and intra-classifier performance, investigate correlation effects, and perform a benchmarking exercise. This is a first systematic study of CDS proxy construction by machine learning techniques and a first classifier comparison study entirely based on financial market data. The techniques should be of interest for financial institutions seeking proxies for CDS rates or other financial variables.TOPICS: Credit default swaps, big data/machine learning, performance measurement ER -