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Python for Finance

You're reading from  Python for Finance

Product type Book
Published in Apr 2014
Publisher
ISBN-13 9781783284375
Pages 408 pages
Edition 1st Edition
Languages
Author (1):
Yuxing Yan Yuxing Yan
Profile icon Yuxing Yan

Table of Contents (20) Chapters

Python for Finance
Credits
About the Author
Acknowledgments
About the Reviewers
www.PacktPub.com
Preface
Introduction and Installation of Python Using Python as an Ordinary Calculator Using Python as a Financial Calculator 13 Lines of Python to Price a Call Option Introduction to Modules Introduction to NumPy and SciPy Visual Finance via Matplotlib Statistical Analysis of Time Series The Black-Scholes-Merton Option Model Python Loops and Implied Volatility Monte Carlo Simulation and Options Volatility Measures and GARCH Index

Converting the interest rates


Assume that bank A offers 5 percent compounding monthly, while bank B offers 5.1 percent compounding quarterly. Which bank should we borrow from in order to enjoy a lower interest rate? These examples are associated with conversion between different interest rates. First, let's look at the following formula used to estimate effective annual rate (EAR) for a given Annual Percentage Rate (APR).

Here, m is the compounding frequency within one year. For example, if the annual rate is 5 percent compounding semiannually, its equivalent effective annual rate will be 5.0625 percent. From the two banks' offers, we would choose the offer of bank A since the cost of borrowing (effective annual rate) is cheaper, as shown in the following code:

>>>(1+0.05/2)**2-1
0.05062499999999992
>>>(1+0.051/4)**4-1
0.051983692114066615

For a mortgage estimate, if the annual rate is 5 percent, compounding monthly, the effective monthly rate will be 0.41667 (0.05/12)....

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