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Python for Finance

You're reading from   Python for Finance Apply powerful finance models and quantitative analysis with Python

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Product type Paperback
Published in Jun 2017
Publisher
ISBN-13 9781787125698
Length 586 pages
Edition 2nd Edition
Languages
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Author (1):
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Yuxing Yan Yuxing Yan
Author Profile Icon Yuxing Yan
Yuxing Yan
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Table of Contents (17) Chapters Close

Preface 1. Python Basics FREE CHAPTER 2. Introduction to Python Modules 3. Time Value of Money 4. Sources of Data 5. Bond and Stock Valuation 6. Capital Asset Pricing Model 7. Multifactor Models and Performance Measures 8. Time-Series Analysis 9. Portfolio Theory 10. Options and Futures 11. Value at Risk 12. Monte Carlo Simulation 13. Credit Risk Analysis 14. Exotic Options 15. Volatility, Implied Volatility, ARCH, and GARCH 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 European options with known dividends:

European options with known dividends

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...

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