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

ArXiv ID: 2408.15404 “View on arXiv”

Authors: Unknown

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

Keywords: SVM, Gradient Boosting, Attention-GRU Hybrid, Implied Volatility, Credit Default Swaps (CDS), Credit Derivatives

Complexity vs Empirical Score

• Math Complexity: 7.2/10
• Empirical Rigor: 6.5/10
• Quadrant: Holy Grail
• Why: The paper employs advanced architectures like Attention-GRU hybrids and includes mathematical formalisms (e.g., volatility index formula, Merton model references), indicating moderate-to-high complexity, while its experimental design with comparative ML models and feature engineering demonstrates substantial empirical backing.

  flowchart TD
    A["Research Goal:<br>Predict CDS Implied Volatility<br>using SOTA ML methods"] --> B["Data Preparation<br>Merton Determinants Feature Matrix<br>iTraxx Europe Main 1-Month Volatility Index"]
    B --> C{"Model Selection"}
    C --> D["Support Vector Machine SVM"]
    C --> E["Gradient Boosting"]
    C --> F["Attention-GRU Hybrid"]
    D & E & F --> G["Experimental Process:<br>Incremental Batch Learning<br>Validation"]
    G --> H["Key Findings"]
    H --> I["Strengths identified for<br>Financial Risk Prediction"]
    H --> J["Performance Comparison:<br>SOTA vs Classical ML"]