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The Term Structure of Credit Default Swap Spreads and the Cross Section of Options Returns

Journal: Journal of Futures Markets

Date: 2025

Author: Hao Zhang, Yukun Shi, Dun Han, Pei Liu, Yaofei Xu

Abstract:
This paper, using the natural logarithmic form credit default swap (log CDS) slope, examines the variation in cross‐sectional 1‐month ATM delta‐hedged straddle returns. Our analysis reveals that the log CDS slope significantly and positively predicts these returns, even when accounting for several key volatility mispricing factors. Further investigation shows that this pre dictive relationship exhibits a strong time‐varying pattern, closely linked to market conditions. In contrast, the relationship between notable volatility mispricing factors and straddle returns remains relatively stable over time. Constructing a long‐short quintile portfolio on straddle options confirms that trading performance improves when the past 12‐month market return is at a historically lower level, market volatility is at a historically higher level, and the VIX is elevated. Log CDS slope, as a proxy for excess jump risk premium, significantly predicts delta‐hedged option returns during periods of high volatility.

Link: Google Scholar

Background and Context

Credit Default Swaps and Options

Credit Default Swap (CDS) spreads reflect the credit risk and financial health of companies, while options markets price volatility risk.

Log CDS Slope Innovation

The study introduces the log CDS slope (logarithmic difference between 5-year and 1-year CDS spreads) as a novel predictor of option returns.

Comprehensive Dataset

Analysis covers 713 US companies over 20 years (2002-2021) with 63,661 firm-month observations using portfolio sorting and regression methods.

CDS Slope Construction Captures Credit Risk Trends

5-Year CDS Long-term − 1-Year CDS Short-term Log CDS Slope Captures credit trend
  • CDS slope represents difference between long-term and short-term default risk perceptions
  • Higher slope indicates worsening credit outlook as longer-term risk exceeds short-term
  • Logarithmic transformation enables better cross-sectional comparison across firms with different risk levels

Predictive Power Varies Dramatically Across Market Conditions

Normal Markets Weak Predictive Power Volatile Markets Strong Predictive Power Conditions determine relationship strength
  • CDS slope predictive ability depends critically on prevailing market volatility and stress levels
  • During calm periods, traditional volatility factors dominate option return prediction mechanisms
  • High volatility periods activate jump risk premium, making CDS information more valuable

Strong Predictive Power Before 2012, Weakens Significantly After

  • 2002-2012 period shows clear monotonic relationship between CDS slope and option returns
  • Post-2012 period exhibits dramatically reduced predictive power with inconsistent return patterns
  • Full sample results represent weighted average, masking important time-variation in relationship strength

Highest VIX Periods Generate Strongest CDS Slope Performance

  • Long-short strategy returns increase substantially when VIX reaches historically elevated levels
  • Low VIX periods actually generate negative returns, indicating strategy timing dependence
  • Peak performance occurs during maximum market stress when jump risk premiums dominate

CDS Slope Provides Unique Information Beyond Traditional Volatility Measures

  • Fama-MacBeth regression shows CDS slope maintains significant predictive power alongside traditional factors
  • Traditional IV-HV spread generates highest risk premium but cannot fully explain CDS effects
  • CDS slope captures jump risk premium component not captured by volatility mispricing measures

Contribution and Implications

  • First study to demonstrate systematic information spillover from CDS markets to individual option returns
  • Provides new conditional asset pricing framework showing market-dependent predictive power patterns
  • Offers practical trading strategy performing best during high-volatility periods when markets need hedging most
  • Theoretical contribution linking jump risk premiums to credit term structure information across markets

Data Sources

  • Portfolio returns chart constructed from Tables 4, 6, and 7 Panel D delta-hedged straddle returns
  • VIX conditional analysis chart uses Table 11 Panel D long-short strategy returns across VIX rankings
  • Risk factor premium chart displays Fama-MacBeth coefficients from Table 8 Panel B regression results