Jan 15, 2014 · 7:30 PM
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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. We present an example using NAG’s Python bindings and the pandas library to calculate the implied volatility of options prices. Additionally we will fit varying degrees of polynomials to the curves, examine the volatility surface, and look at the limitations of numerical computing in Python.
Brian Spector is a Technical Consultant at Numerical Algorithms Group in Lisle, Illinois, USA. He works closely with NAG’s customers very often with those from financial services providing technical support and/or delivering services. Brian has worked on a number of projects including different methods of portfolio optimization, implied volatility, and a Gaussian mixture model algorithm. Recently, he has been developing the Python bindings for the NAG Library.
Brian has a Master’s degree in Math from Carnegie Mellon University with concentration in Applied Analysis and is currently working on his Master’s in Financial Math from the University of Chicago.