mlsanityData to 14 Oct 2026

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

arXiv 2408.15404 ↗First posted 27 Aug 2024q-fin.CP cs.LG q-fin.RMcited by 0

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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

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.

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References · 7

  1. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,166
  2. 2019Transformer-XL: Attentive Language Models Beyond a Fixed-Length Contextcited by 1,850
  3. 2022TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysiscited by 1,058
  4. 2019Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecastingcited by 761
  5. 2022Transformers in Time Series: A Surveycited by 497
  6. 2023Comparing Deep Learning Models for the Task of Volatility Prediction Using Multivariate Datacited by 1
  7. 2023Combining Deep Learning and GARCH Models for Financial Volatility and Risk Forecastingcited by 1

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