Book Image

Python for Finance - Second Edition

By : Yuxing Yan
5 (1)
Book Image

Python for Finance - Second Edition

5 (1)
By: Yuxing Yan

Overview of this book

This book uses Python as its computational tool. Since Python is free, any school or organization can download and use it. This book is organized according to various finance subjects. In other words, the first edition focuses more on Python, while the second edition is truly trying to apply Python to finance. The book starts by explaining topics exclusively related to Python. Then we deal with critical parts of Python, explaining concepts such as time value of money stock and bond evaluations, capital asset pricing model, multi-factor models, time series analysis, portfolio theory, options and futures. This book will help us to learn or review the basics of quantitative finance and apply Python to solve various problems, such as estimating IBM’s market risk, running a Fama-French 3-factor, 5-factor, or Fama-French-Carhart 4 factor model, estimating the VaR of a 5-stock portfolio, estimating the optimal portfolio, and constructing the efficient frontier for a 20-stock portfolio with real-world stock, and with Monte Carlo Simulation. Later, we will also learn how to replicate the famous Black-Scholes-Merton option model and how to price exotic options such as the average price call option.
Table of Contents (23 chapters)
Python for Finance Second Edition
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
Index

European options with known dividends


Assume that we have a known dividend d1 distributed at time T1, T1<T, where T is our maturity date. We can modify the original Black-Scholes-Merton option model by replacing S0 with S, where :

In the preceding example, if we have a known dividend of $1.5 delivered in one month, what is the price of the call?

>>>import p4f
>>>s0=40
>>>d1=1.5
>>>r=0.015
>>>T=6/12
>>>s=s0-exp(-r*T*d1)
>>>x=42
>>>sigma=0.2 
>>>round(p4f.bs_call(s,x,T,r,sigma),2)
1.18

The first line of the program imports the module called p4f which contains the call option model. The result shows that the price of the call is $1.18, which is lower than the previous value ($1.56). It is understandable since the price of the underlying stock would drop roughly by $1.5 in one month. Because of this, the chance that we could exercise our call option will be smaller, that is, less likely to go beyond $42. The preceding...