Equity option implied volatility skew as a substitute credit risk signal: Evidence from negative rating announcements in India, 2024–2025

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Type of the article: Research Article

Abstract
India’s equity derivative market ranks among the most liquid globally, yet its corporate credit derivative segment remains structurally underdeveloped, leaving the pricing of credit risk largely dependent on periodic assessments by rating agencies. This structural incompleteness raises a fundamental question about where credit-sensitive information is incorporated when formal credit derivative instruments are absent. This study examines whether informed traders utilize equity option implied volatility skew as a substitute channel for pricing credit risk prior to formal rating agency announcements in India. Using a high-frequency cross-sectional event study, we analyze the 25-delta put-call implied volatility (IV) skew across 42 negative credit rating actions – comprising outright downgrades, outlook revisions, and watch-negative placements – for Nifty 500 constituents listed in the NSE Futures and Options segment from January 2024 to November 2025. Cumulative abnormal returns (CARs) are computed using the standard market model with a 250-trading-day estimation window, and a cross-sectional OLS regression with heteroscedasticity-robust (HC3) standard errors is employed to test predictive relationships. The pre-event IV skew widened significantly in the three to five trading days preceding each public announcement, with a mean pre-event skew of 4.20%, markedly above the historical baseline of approximately 2.1%. Cross-sectional regression confirms that the pre-event skew is a robust negative predictor of post-announcement CARs (β = −0.315, t = −3.84, p < 0.001; Adj. R² = 0.389), with each percentage-point increase in pre-event skew corresponding to a 0.315% deeper post-announcement stock decline.

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    • Table 1. Descriptive statistics
    • Table 2. Cross-Sectional Regression of Post-Announcement CAR [0, +2] on Pre-Event IV Skew
    • Table A1. Sample of credit events used in the study (N = 42)
    • Conceptualization
      Abhishek Chandra Shukla, Ritesh Khatwani, Pradip Kumar Mitra, Vijay Agrawal, Janki Mistry
    • Data curation
      Abhishek Chandra Shukla, Ritesh Khatwani, Pradip Kumar Mitra, Vijay Agrawal, Janki Mistry
    • Formal Analysis
      Abhishek Chandra Shukla, Ritesh Khatwani, Pradip Kumar Mitra, Vijay Agrawal, Janki Mistry
    • Investigation
      Abhishek Chandra Shukla, Ritesh Khatwani, Pradip Kumar Mitra, Vijay Agrawal, Janki Mistry
    • Methodology
      Abhishek Chandra Shukla, Ritesh Khatwani, Pradip Kumar Mitra, Vijay Agrawal, Janki Mistry
    • Project administration
      Abhishek Chandra Shukla, Ritesh Khatwani, Pradip Kumar Mitra
    • Software
      Abhishek Chandra Shukla, Ritesh Khatwani, Vijay Agrawal, Janki Mistry
    • Supervision
      Abhishek Chandra Shukla, Ritesh Khatwani, Pradip Kumar Mitra, Vijay Agrawal, Janki Mistry
    • Writing – original draft
      Abhishek Chandra Shukla, Ritesh Khatwani, Pradip Kumar Mitra, Vijay Agrawal, Janki Mistry
    • Writing – review & editing
      Abhishek Chandra Shukla, Ritesh Khatwani, Pradip Kumar Mitra, Vijay Agrawal, Janki Mistry
    • Resources
      Ritesh Khatwani
    • Validation
      Ritesh Khatwani, Pradip Kumar Mitra
    • Visualization
      Ritesh Khatwani