💻 Code & 📦 Data

This page contains some (hopefully) useful Python code and data used in our research. An acknowledgement and citation of doi.org/10.17605/OSF.IO/Z2486 in your paper would be much appreciated.

💻 Code

Option-Implied Moments (Python)

• Download qmoms Python package from GitHub.

• The qmoms package contains functions to compute option-implied moments and characteristics from implied volatility surface data.

📁 Computed Moments & Characteristics

qmoms uses out-the-money (OTM) implied volatilities interpolated as a function of moneyness (Strike/Underlying or Strike/Forward) within the available range. Outside the available range, boundary values are extended. OTM Definition: moneyness ≥ 1 for calls and < 1 for puts.

• Model-Free Implied Variance (log contract)
  • MFIV_BKM: Based on Bakshi, Kapadia, and Madan (RFS, 2003)
    
  • MFIV_BJN: Based on Britten-Jones and Neuberger (JF, 2002)
• Simple Model-Free Implied Variance
  • SMFIV: Based on Martin (QJE, 2017)
• Corridor VIX
  • CVIX: Based on Andersen, Bondarenko, and Gonzalez-Perez (RFS, 2015)
• Tail Loss Measure
  • TLM: Based on Vilkov and Xiao (2012) and Hamidieh (Journal of Risk, 2017)
• Down-Side Semivariances
  • Based on Feunou, Jahan-Parvar, and Okou (J. Fin. Econometrics, 2017)
    > Up semivariances can be computed as total variance minus down semivariance.
• Tail Jump Measure
  • RIX: Based on Gao, Gao and Song (RFS, 2018)
• Tail Steepness Measures
  • Slopeup (right tail) and Slopedn (left tail) measure tail steepness
    > Larger values = more expensive tail relative to ATM vol.
  Used in:
  • Carbon Tail Risk (Ilhan, Sautner, Vilkov, RFS 2021)
    
  • Pricing Climate Change Exposure (v.Lent, Sautner, Zhang, Vilkov, MS 2023)
    
  • Kelly, Pastor, Veronesi (JF, 2016)
    > Note: we define -Slope for Slopeup to improve interpretability.

📦 Data

Data hosted in OSF Data Repository

• Download Data on Climate Value and Values Discovery in Earnings Calls
  From Climate Value and Values Discovery in Earning Calls (2024)

• Download Firm-level Climate Change Exposure
  From Firm-level Climate Change Exposure (2023)

• Download Implied and Realized Correlations
  From Option-Implied Correlations and the Price of Correlation Risk (2005/2012)

• Download Generalized Lower Bounds
  From Generalized Bounds on the Conditional Expected Excess Return (2024)

• Download Option-Implied Market Betas
  From Measuring Equity Risk with Option-implied Correlations

• Download Model-Free Implied Skewness (MFIS)
  From Risk-Neutral Skewness: Return Predictability and Its Sources (2012)

• Download SPX 0DTE Data
  From ODTE Trading Rules (2024)

Direct Downloads

• From Carbon Tail Risk (2021 RFS)
  📊 Excel Data (2020-05-20)

• From Measuring Equity Risk with Option-implied Correlations (2012 RFS)
  📁 Zipped mat Data (1996–2009)

  Contents

  Contains 5 MATLAB .mat files:

  • market_betas_1996_2009.mat:
    Structure betas with 6 different beta methodologies, aligned to a common timeline in time vector dt. Also includes permno ID vector. In the paper we used:

    • implied: impl_daily_251d_mfiv, impl_monthly_60m_mfiv
      
    • historical: hist_daily_251d, hist_monthly_60m

  • id_dt.mat:
    Time vector dt, and vector of IDs (PERMNO from CRSP). The first PERMNO = 999999 is the market itself (S&P 500).

  • weights.mat:
    Synthetic weights w of stocks in the S&P 500. The first column is NaN because it refers to the market itself.

  • dailyret.mat:
    Daily returns (ret and retx for ex-div returns) for the S&P 500 and its components.

  • mnthly_ret.mat:
    Monthly returns retm for the S&P 500 and its components. Includes time vector dtm.