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Python for Finance

Python for Finance

By : Yuxing Yan
3.9 (22)
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Python for Finance

Python for Finance

3.9 (22)
By: Yuxing Yan

Overview of this book

A hands-on guide with easy-to-follow examples to help you learn about option theory, quantitative finance, financial modeling, and time series using Python. Python for Finance is perfect for graduate students, practitioners, and application developers who wish to learn how to utilize Python to handle their financial needs. Basic knowledge of Python will be helpful but knowledge of programming is necessary.
Table of Contents (14 chapters)
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13
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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