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Mastering Python for Finance

You're reading from   Mastering Python for Finance Understand, design, and implement state-of-the-art mathematical and statistical applications used in finance with Python

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Product type Paperback
Published in Apr 2015
Publisher Packt
ISBN-13 9781784394516
Length 340 pages
Edition 1st Edition
Languages
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Toc

Table of Contents (17) Chapters Close

Mastering Python for Finance
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
1. Python for Financial Applications FREE CHAPTER 2. The Importance of Linearity in Finance 3. Nonlinearity in Finance 4. Numerical Procedures 5. Interest Rates and Derivatives 6. Interactive Financial Analytics with Python and VSTOXX 7. Big Data with Python 8. Algorithmic Trading 9. Backtesting 10. Excel with Python Index

Putting it all together – implied volatility modeling


In the options pricing methods we learned so far, a number of parameters are assumed to be constant: interest rates, strike prices, dividends, and volatility. Here, the parameter of interest is volatility. In quantitative research, the volatility ratio is used to forecast price trends.

To derive implied volatilities, we need to refer to Chapter 3, Nonlinearity in Finance where we discussed root-finding methods of nonlinear functions. We will use the bisection method of numerical procedures in our next example to create an implied volatility curve.

Implied volatilities of AAPL American put option

Let's consider the option data of the stock Apple (AAPL) gathered at the end of day on October 3, 2014, given in the following table. The option expires on December 20, 2014. The prices listed are the mid-points of the bid and ask prices:

Strike price

Call price

Put price

75

30

0.16

80

24.55

0.32

85

20.1

0.6

90

15.37

1.22

92.5

10...

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