Implied Volatility using Python's Pandas Library
Python has some nice packages such as numpy, scipy, and matplotlib for numerical computing and data visualization. Another package that deserves a mention that we have seen increasingly is Python's pandas library. Pandas has fast and efficient data analysis tools to store and process large amounts of data. Additionally, pandas has numpy and ctypes built into it which allow easy integration with NAG's nag4py package.
Below is an example using nag4py and the pandas library to calculate the implied volatility of options prices. All the code below can be downloaded to calculate your own implied volatility surface for data on the Chicago Board of Options Exchange website.
Background on Implied Volatility
The famous Black Scholes formula for pricing a Call/Put option on a stock is a function of 6 variables; Underlying Price, Interest Rate, Dividends, Strike Price, Time-to-Expiration, and Volatility. Note that for a given option contract we can observe the Underlying Price, Interest Rate, and Dividend Rate. In addition, the options contract specifies the Strike Price and Time-to-Expiration.
Thus the one variable we have to tweak is the volatility. We can then ask the question: For what volatility* does the Black Scholes equation price equal the market price.
This volatility* is then denoted as the implied volatility observed in the market. Since the Black Scholes equation is a continuous function of volatility on (0, 1) we can use a NAG root finder to locate such volatility*. In fact, we will use a couple NAG functions; nag_zero_cont_func_brent will find the root using Brent's Algorithm, nag_bsm_price will calculate the theoretical option price, nag_2d_cheb_fit_lines will perform a least squares Chebyshev fit to the volatility surface, and nag_2d_cheb_eval will evaluate the surface at intermediate points.
Running the Script
We have created an example script that will run on data from the Chicago Board of Options Exchange (CBOE) website. The CBOE provides options data in downloadable format here.
The program uses the pandas package to easily store and manipulate the data via DataFrames. In the script, you'll notice pandas takes care of the many data processing functions and then calls the NAG Library for more complex analysis. The program will automatically read in the options data, calculate implied volatility for the call and put options, and plot the volatility curves and surface. The above code can be run as follows (given that you have pandas, matplotlib, nag4py, and ctypes):
I ran the program on Apple Options data (SYM AAPL). Below is the output, generated in 10 seconds with roughly 500,000 calls to the NAG Library:
Implied Volatility for AAPL (APPLE INC),500.3691,+4.3291, Oct 15 2013 @ 15:25 ET,Bid,500.32,Ask,500.48,Size,3x1,Vol,10041069,
|Figure 1: Volatility Curves (Click to Enlarge)|
In the above picture we see some nice volatility skews/smiles that level out as the option expiration date increases. This occurs when investors demand higher premiums (and thus volatility) for deep in and out of the money option prices. As a result, implied volatility curves are oftentimes upward sloping, downward sloping, or U-shaped.
Below is the volatility surface (plotting Strike, Expiration, and Implied Volatility from Figure 1 on the same graph).
|Figure 2: Volatility Surface (Click to Enlarge)|
When performing the Chebyshev surface fit, two required inputs are k and l; the degrees of the polynomials in the x and y directions. I choose k = 3 and l = 3 for the above graph, but these can vary among stocks. It is best to first examine the volatility curves for various expiration dates (Figure 1) and then choose k and l appropriately.
So go ahead, download any options data the CBOE provides and plot your own volatility surface! Many thanks to John Morrissey and Chris Seymour for code suggestions.
Update: Download data during CBOE trading hours to ensure the graphs are not null.