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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

Introducing futures

Before discussing the basic concepts and formulas related to futures, let's review the concept of continuously compounded interest rates. In Chapter 3, Time Value of Money, we learned that the following formula could be applied to estimate the future value of a given present value:

Introducing futures

Here, FV is the future value, PV is the present value, R is the effective period rate and n is the number of periods. For example, assume that the Annual Percentage Rate (APR) is 8%, compounded semiannually. If we deposit $100 today, what is its future value in two years? The following code shows the result:

import scipy as ps
pv=100
APR=0.08
rate=APR/2.0
n=2
nper=n*2
fv=ps.fv(rate,nper,0,pv)
print(fv)

The output is shown here:

-116.985856

The future value is $116.99. In the preceding program, the effective semiannual rate is 4% since the APR is 8% compounded semiannually. In options theory, risk-free rates and dividend yields are defined as continuously compounded. It is easy to derive the...

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