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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

Bootstrapping with/without replacements


Assume that we have the historical data, such as price and return, for a stock. Obviously, we could estimate their mean, standard deviation, and other related statistics. What are their expected annual mean and risk next year? The simplest, maybe naïve way is to use the historical mean and standard deviation. A better way is to construct the distribution of annual return and risk. This means that we have to find a way to use historical data more effectively to predict the future. In such cases, we could apply the bootstrapping methodology. For example, for one stock, we have its last 20-year monthly returns, that is, 240 observations.

To estimate next year's 12 monthly returns, we need to construct a return distribution. First, we choose 12 returns randomly from the historical return set without replacements and estimate their mean and standard deviations. We repeat this procedure 5,000 times. The final output will be our return-standard distribution...

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