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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

Black-Scholes-Merton option model on non-dividend paying stocks

The Black-Scholes-Merton option model is a closed-form solution to price a European option on a stock which does not pay any dividends before its maturity date. If we use Black-Scholes-Merton option model on non-dividend paying stocks or the price today, X for the exercise price, r for the continuously compounded risk-free rate, T for the maturity in years, Black-Scholes-Merton option model on non-dividend paying stocks for the volatility of the stock, the closed-form formulae for a European call (c) and put (p) are:

Black-Scholes-Merton option model on non-dividend paying stocks

Here, N() is the cumulative standard normal distribution. The following Python codes represent the preceding equations to evaluate a European call:

from scipy import log,exp,sqrt,stats
def bs_call(S,X,T,r,sigma):
    d1=(log(S/X)+(r+sigma*sigma/2.)*T)/(sigma*sqrt(T))
    d2 = d1-sigma*sqrt(T)
return S*stats.norm.cdf(d1)-X*exp(-r*T)*stats.norm.cdf(d2)

In the preceding program, the stats.norm.cdf() is the cumulative normal distribution, that is, N() in the Black-Scholes-Merton option model. The current stock price is $40, the strike price...

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