ARIMA models are popular forecasting methods with lots of applications in the domain of finance. For example, using a linear combination of past returns and residuals, an attempt can be made to predict future returns. Sadly, when returns exhibit a change in variance over time, this family of models runs into problems. This phenomenon is often present and also known as conditional variance, or volatility clustering. Luckily though, there’s another family of models able to model this irregularity: (G)ARCH models. Both types of models are therefore combined in practice to optimize forecasting performance.

Python has great packages for training both ARIMA and GARCH models separately, but none that actually combine both (like R’s nifty package rugarch — *damn you R users*). Let’s take a look at how we can combine these models in Python while staying clear of too much theory. There’s plenty of other tutorials out there to expand on what we cover in this post, and I’ll conveniently provide you with links when necessary. Let’s delve into it!

#python #statistics #forecasting #predictions #timeseries

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