Python for Finance

Chapter 14
Summary
The options we've discussed in Chapter 10, Options and Futures are usually called
vanilla options that have a close-form solution, that is, the Black-Scholes-Merton
option model. In addition to those vanilla options, many exotic options exist. In
this chapter, we have discussed several types of exotic options, such as Bermudan
options, simple chooser options, shout and binary options, average price options,
Up-and-in options, up-and-out options, and down-and-in and down-and-out
options. For a European call, the option buyer could exercise their right at the
maturity date, while for an American option buyer, they could exercise their right
any time before or on the maturity date. A Bermudan option could be exercised a
few times before maturity.
In the next chapter, we will discuss various volatility measures, such as our
conventional standard deviation, Lower Partial Standard Deviation (LPSD).
Using the standard deviation of returns as a risk measure is based on a critical
assumption that stock returns follow a normal distribution. Because of this, we
introduce several normality tests. In addition, we graphically show volatility
clustering—high volatility is usually followed by a high-volatility period, while
low volatility is usually followed by a low-volatility period. To deal with this
phenomenon, the Autoregressive Conditional Heteroskedasticity (ARCH) process
was developed by Angel (1982), and the Generalized AutoRegressive Conditional
Heteroskedasticity (GARCH) processes, which are extensions of ARCH, were
developed by Bollerslev (1986). Their graphical presentations and related Python
programs will also be covered in the next chapter.
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