Evaluating Credit VIX (CDS IV) Prediction Methods with Incremental Batch Learning
<|reference_start|>Evaluating Credit VIX (CDS IV) Prediction Methods with Incremental Batch Learning: 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<|reference_end|>
@article{taylor2024evaluating,
    title={Evaluating Credit VIX (CDS IV) Prediction Methods with Incremental Batch
  Learning},
    author={Robert Taylor},
    journal={arXiv preprint arXiv:2408.15404},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.15404},
    primaryClass={q-fin.CP cs.LG q-fin.RM}
}