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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. What is the definition of volatility?

2. How can you measure risk (volatility)?/

3. What are the issues related to the widely used definition of risk (standard deviation)?

4. How can you test whether stock returns follow a normal distribution? For given sets of stocks, test whether they follow a normal distribution.

5. What is the lower partial standard deviation? What are its applications?

6. Choose five stocks, such as DELL, IBM, Microsoft, Citi Group, and Walmart, and compare their standard deviation with LPSD based on the last three-years' daily data.

7. Is a stock's volatility constant over the years?

8. Use the Breusch-Pagan (1979) test to confirm or reject the hypothesis that daily returns for IBM is homogeneous.

9. How can you test whether a stock's volatility is constant?

10. What does "fat tail" mean ? Why should we care about fat tail?

11. How can you download the option data?

12. What is an ARCH (1) process?

13. What is a GARCH (1,1) process?

14. Apply...

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