Bayesian Estimation of Corporate Default Spreads

ArXiv ID: 2503.02991 “View on arXiv”

Authors: Unknown

Abstract

Risk-averse investors often wish to exclude stocks from their portfolios that bear high credit risk, which is a measure of a firm’s likelihood of bankruptcy. This risk is commonly estimated by constructing signals from quarterly accounting items, such as debt and income volatility. While such information may provide a rich description of a firm’s credit risk, the low-frequency with which the data is released implies that investors may be operating with outdated information. In this paper we circumvent this problem by developing a high-frequency credit risk proxy via corporate default spreads which are estimated from daily bond price data. We accomplish this by adapting classic yield curve estimation methods to a corporate bond setting, leveraging advances in Bayesian estimation to ensure higher model stability when working with small sample data which also allows us to directly model the uncertainty of our predictions.

Keywords: Credit Risk, Bayesian Estimation, Yield Curve Estimation, Default Spreads, High-Frequency Data, Fixed Income (Corporate Bonds)

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 6.0/10
• Quadrant: Holy Grail
• Why: The paper presents advanced mathematical modeling with Bayesian estimation and yield curve theory, but focuses more on methodology and theoretical frameworks than on comprehensive backtesting with real-world data.

  flowchart TD
    A["Research Goal: Develop high-frequency credit risk proxy"] --> B["Methodology: Bayesian yield curve estimation"]
    B --> C["Input: Daily corporate bond price data"]
    C --> D["Compute: Corporate default spreads"]
    D --> E["Estimate: Bayesian parameters with uncertainty quantification"]
    E --> F["Outcome: High-frequency default spread estimates"]
    F --> G["Benefit: Real-time credit risk assessment<br>compared to low-frequency accounting data"]