Evaluating Credit VIX (CDS IV) Prediction Methods with Incremental Batch Learning
Computational Finance
2024-08-29 v1 Machine Learning
Risk Management
Abstract
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
Cite
@article{arxiv.2408.15404,
title = {Evaluating Credit VIX (CDS IV) Prediction Methods with Incremental Batch Learning},
author = {Robert Taylor},
journal= {arXiv preprint arXiv:2408.15404},
year = {2024}
}