Python for Finance

Chapter 15
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 evolution 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 included to solve the GJR_GARCH (1,1,1) process.
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