So in this lecture, we will be introducing the next section of this course, which is on a Financial
Times series technique known as Gargash.
So what's the story behind Gurche?
Well, I've said pretty often that in finance it's common for people who are just starting out to ask
the question, how do I predict stock prices?
In most finance courses?
You quickly learn that this is not the right question.
The correct question is how do I predict stock returns?
Now, that's just the first step.
You see, what happens when you try to predict stock returns is that you find it is pretty hard.
The next step is to consider the question, well, why are stock returns so hard to predict?
The answer is that they very closely resemble a random walk.
In fact, random walks are so accurate that we even use them in finance, for example, to price options.
There is even a book written about stock prices being random walks.
Unfortunately, one characteristic of random stocks is that they are completely unpredictable.
Now, it turns out that there are some attributes of stock returns that make them unlike random walks,
one of those attributes has to do with what we call volatility clustering.
So what does this mean?
Well, notice how at certain points in time we have very large returns, both positive and negative.
One thing to notice about these large returns is that they tend to be surrounded by other large returns.
In other words, large movements tend to cluster together.
So Financial Times series is really all about figuring out what you cannot model and then asking new
questions to figure out what you can.
We know that it's very difficult to predict returns, but clearly if we consider the variance of the
returns, there seems to be some pattern that may be explainable.
OK, so let's go through a quick outline for this section.
This section will start out with the arch model, which is like an auto regressive model of squared
errors.
Now, the reason we want to study art first is because Gurche seems to be like a Rimma for variance.
But there are a few details we need to consider if we really want to understand this relationship.
The next step is to study Darch, which adds one more component to Arch.
The next step, as usual, is to try everything in code so you can see how it works.
We'll be using the arch library in Python, although there are others available.
Well, then go back into theory mode and discuss how Gargash models might be improved, for instance,
using deep neural networks.
I'll go through a conceptual derivation of how you might apply deep learning to Gurche, although we
won't be implementing these concepts at this time.
However, note that I have done examples like this in the past, so feel free to send me an email if
you want to learn more.
They do require more advanced knowledge of deep learning libraries.
The reason I mention this now is because it should give you a better understanding of how Gargash works,
even though deep learning is not a large topic.
So the purpose of this conceptual exercise isn't to actually do deep learning.
It's more to enhance your understanding of garbage once we're done.
We will summarize this section and I'll give you a preview of some other ways Gargash has been extended.
There are many variations of garbage and it really could be a course on its own.
So if you would be interested in such a course that goes in-depth into Gargash, just let me know when
my suggestion box or contact me by email.