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Python for Finance. - Second Edition

You're reading from  Python for Finance. - Second Edition

Product type Book
Published in Jun 2017
Publisher
ISBN-13 9781787125698
Pages 586 pages
Edition 2nd Edition
Languages

Table of Contents (23) Chapters

Python for Finance Second Edition
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
Python Basics Introduction to Python Modules Time Value of Money Sources of Data Bond and Stock Valuation Capital Asset Pricing Model Multifactor Models and Performance Measures Time-Series Analysis Portfolio Theory Options and Futures Value at Risk Monte Carlo Simulation Credit Risk Analysis Exotic Options Volatility, Implied Volatility, ARCH, and GARCH Index

Simulation of stock price movements


We mentioned in the previous sections that in finance, returns are assumed to follow a normal distribution, whereas prices follow a lognormal distribution. The stock price at time t+1 is a function of the stock price at t, mean, standard deviation, and the time interval, as shown in the following formula:

In this formula, St + 1 is the stock price at t+1, ˆ μ is the expected stock return, t _ is the time interval (T t n_= ), T is the time (in years), n is the number of steps, ε is the distribution term with a zero mean, and σ is the volatility of the underlying stock. With a simple manipulation, equation (4) can lead to the following equation that we will use in our programs:

In a risk-neutral work, no investors require compensation for bearing risk. In other words, in such a world, the expected return on any security (investment) is the risk-free rate. Thus, in a risk-neutral world, the previous equation becomes the following equation:

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