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Python for Finance

You're reading from   Python for Finance If your interest is finance and trading, then using Python to build a financial calculator makes absolute sense. As does this book which is a hands-on guide covering everything from option theory to time series.

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Product type Paperback
Published in Apr 2014
Publisher
ISBN-13 9781783284375
Length 408 pages
Edition 1st Edition
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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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Toc

Table of Contents (14) Chapters Close

Preface 1. Introduction and Installation of Python FREE CHAPTER 2. Using Python as an Ordinary Calculator 3. Using Python as a Financial Calculator 4. 13 Lines of Python to Price a Call Option 5. Introduction to Modules 6. Introduction to NumPy and SciPy 7. Visual Finance via Matplotlib 8. Statistical Analysis of Time Series 9. The Black-Scholes-Merton Option Model 10. Python Loops and Implied Volatility 11. Monte Carlo Simulation and Options 12. Volatility Measures and GARCH Index

Exercises

1. How many types of loops are present in Python? What are the differences between them?

2. What are the advantages of using a for loop versus a while loop? What are the disadvantages?

3. Based on a for loop, write a Python program to estimate the implied volatility. For a given set of values S=35, X=36, rf=0.024, T=1, sigma=0.13, and c=2.24, what is the implied volatility?

4. Write a Python program based on the Black-Scholes-Merton option model put option model to estimate the implied volatility.

5. Should we get different volatilities based on the Black-Scholes-Merton option model's call and put?

6. For a stock with multiple calls, we could estimate its implied volatility based on its call or put. Based on the Black-Scholes-Merton option model, could we get different values?

7. When estimating a huge number of implied volatilities, such as 5,000 stocks, how can we make our process more efficient?

8. We could apply the binary search method to estimate an implied volatility based...

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