From the course: Modeling Market Prices Using Stochastic Processes with Wolfram Language
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Differences - Wolfram Language Tutorial
From the course: Modeling Market Prices Using Stochastic Processes with Wolfram Language
Differences
If we go and look at the differences function, it allows us to look at a sequence of differences as we go down further into the data. So if we've got two-dimensional data here, then looking at differences {0, 1} finds the differences between the columns, that is, the differences in the values. And we can now look at that log returns. We'll see we've got for five stocks. And because we've taken differences, it's 252-1. Now what I want to do at the next stage is just slightly complex. What I want to do basically here is a bit of smoothing. And the first notion should be what's the cause of the problem here? The cause of the problem is the high volatilities and the range in volatilities. Now there is a stochastic process that allows us to look at this. There's the ARCH and the GARCH processes. In particular, if we look at the ARCH process, this is the autoregressive conditionally heteroscedastic process. Okay, so a big mouthful. The main point here is it's conditional. It's a conditional…
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