Evaluating Credit VIX (CDS IV) Prediction Methods with Incremental Batch Learning
This work addresses financial risk prediction for credit markets, but it is incremental as it compares existing methods on new data.
The paper compared SVM, Gradient Boosting, and an Attention-GRU Hybrid model to predict the Implied Volatility of credit default swaps (CDS) on European corporate debt, finding strengths in both SOTA and classical methods for this financial risk task.
This paper presents the experimental process and results of SVM, Gradient Boosting, and an Attention-GRU Hybrid model in predicting the Implied Volatility of rolled-over five-year spread contracts of credit default swaps (CDS) on European corporate debt during the quarter following mid-May '24, as represented by the iTraxx/Cboe Europe Main 1-Month Volatility Index (BP Volatility). The analysis employs a feature matrix inspired by Merton's determinants of default probability. Our comparative assessment aims to identify strengths in SOTA and classical machine learning methods for financial risk prediction