Estimating Volatility for Option Pricing

  1. Historical Approach: This assumes that past volatility is representative of future volatility.
  • For BSM, the annualized standard deviation of price returns is applied.
  • σannual = σperiodic * √periods per year
  • 250 trading days per year is a convention when the periodic standard deviation is daily; 12 is applied for monthly data; 52 is applied for weekly data.
  1. Implied Volatility Approach: This takes the current market price of the option and back solves for the implied value of volatility via the option pricing model.
  • The output of the implied volatility approach is an estimate for volatility that equates the BSM price of the option to the market price of the option.
  • Analysts and traders can use this approach to form opinions as to whether an option price is too high or too low based on their own expectations for volatility relative to the implied volatility priced into the option.
Membership
Learn the skills required to excel in data science and data analytics covering R, Python, machine learning, and AI.
I WANT TO JOIN
JOIN 30,000 DATA PROFESSIONALS

Free Guides - Getting Started with R and Python

Enter your name and email address below and we will email you the guides for R programming and Python.

Saylient AI Logo

Take the Next Step in Your Data Career

Join our membership for lifetime unlimited access to all our data analytics and data science learning content and resources.