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Python for Finance

You're reading from  Python for Finance

Product type Book
Published in Apr 2014
Publisher
ISBN-13 9781783284375
Pages 408 pages
Edition 1st Edition
Languages
Author (1):
Yuxing Yan Yuxing Yan
Profile icon Yuxing Yan

Table of Contents (20) Chapters

Python for Finance
Credits
About the Author
Acknowledgments
About the Reviewers
www.PacktPub.com
Preface
Introduction and Installation of Python Using Python as an Ordinary Calculator Using Python as a Financial Calculator 13 Lines of Python to Price a Call Option Introduction to Modules Introduction to NumPy and SciPy Visual Finance via Matplotlib Statistical Analysis of Time Series The Black-Scholes-Merton Option Model Python Loops and Implied Volatility Monte Carlo Simulation and Options Volatility Measures and GARCH Index

Summary


In this chapter, we discussed several types of distributions: normal, standard normal, lognormal, and Poisson. Since the assumption that stocks follow a lognormal distribution and returns follow a normal distribution is the cornerstone for option theory, the Monte Carlo simulation is used to price European options. Under certain scenarios, Asian options might be more effective in terms of hedging. Exotic options are more complex than the vanilla options since the former have no closed-form solution, while the latter could be priced by the Black-Scholes-Merton option model. One way to price these exotic options is to use the Monte Carlo simulation. The Python programs to price an Asian option and lookback options are discussed in detail.

In the next chapter, we will discuss various volatility measures, such as our conventional standard deviation, Lower Partial Standard Deviation (LPSD). Using the standard deviation of returns as a risk measure is based on a critical assumption that...

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