• Skip to main content
  • Skip to footer

OptionMetrics

search
  • About Us
    • Who We Serve
    • Why OptionMetrics
    • Leadership
  • Data Products
    • Equities
      • United States
      • Europe
      • Asia
      • Canada
      • ETFs
    • Futures
    • Signed Volume
    • Implied Beta
    • Dividend
      • Implied Dividend
      • Dividend Forecasting
  • Research
  • Blog
  • News & Events
  • Careers
  • Contact

F. Audrino, D. Colangelo: Semi-parametric forecasts of the implied volatility surface using regression trees

October 1, 2010

We present a new semi-parametric model for the prediction of implied volatility surfaces that can be estimated using machine learning algorithms. Given a reasonable starting model, a boosting algorithm based on regression trees sequentially minimizes generalized residuals computed as differences between observed and estimated implied volatilities. To overcome the poor predictive power of existing models, we include a grid in the region of interest and implement a cross-validation strategy to find an optimal stopping value for the boosting procedure.

Download

Share this post:
  • Facebook
  • Pinterest
  • Twitter
  • Linkedin
OptionMetrics Logo
  • About Us
  • Who We Serve
  • Why OptionMetrics
  • Leadership
  • Data Products
  • Equities
  • Futures
  • Signed Volume
  • Implied Beta
  • Dividend Forecasting
  • Research
  • Blog
  • News & Events
  • Careers
  • Contact Us
  • Support Request
Stay Connected

dashicons-linkedin dashicons-twitter dashicons-facebook-alt

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply

© 2025 OptionMetrics, LLC. All Rights Reserved. | Privacy Policy | Terms of Use | Accessibility | Site Map