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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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Table of Contents (14) Chapters Close

Preface 1. Introduction and Installation of Python 2. Using Python as an Ordinary Calculator FREE CHAPTER 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

Summary

In this chapter, we focused on several issues, especially on volatility measures and ARCH/GARCH. For the volatility measures, first we discussed the widely used standard deviation, which is based on the normality assumption. To show that such an assumption might not hold, we introduced several normality tests, such as the Shapiro-Wilk test and the Anderson-Darling test. To show a fat tail of many stocks' real distribution benchmarked on a normal distribution, we vividly used various graphs to illustrate it. To show that the volatility might not be constant, we presented the test to compare the variance over two periods. Then, we showed a Python program to conduct the Breusch-Pangan (1979) test for heteroskedasticity. ARCH and GARCH are used widely to describe the evolvements of volatility over time. For these models, we simulate their simple form such as ARCH (1) and GARCH (1,1) processes. In addition to their graphical presentations, the Python codes of Kevin Sheppard are...

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