# Evaluating Credit VIX (CDS IV) Prediction Methods with Incremental Batch Learning

arXiv: https://arxiv.org/abs/2408.15404 · HTML: https://mlsanity.com/paper/2408.15404/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 60 references that are in the map.
- First posted on arXiv 27 Aug 2024.

## Builds on (most cited first, 7 of 7)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://mlsanity.com/paper/1901.02860/) (2019)
- [TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis](https://mlsanity.com/paper/2210.02186/) (2022)
- [Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting](https://mlsanity.com/paper/1912.09363/) (2019)
- [Transformers in Time Series: A Survey](https://mlsanity.com/paper/2202.07125/) (2022)
- [Comparing Deep Learning Models for the Task of Volatility Prediction Using Multivariate Data](https://mlsanity.com/paper/2306.12446/) (2023)
- [Combining Deep Learning and GARCH Models for Financial Volatility and Risk Forecasting](https://mlsanity.com/paper/2310.01063/) (2023)

## Abstract (arXiv)

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