Autoregressive conditional heteroskedasticity (ARCH) is a statistical model used to analyze volatility in time series to forecast future volatility. ARCH modeling shows that periods of high volatility are followed by more high volatility and periods of low volatility are followed by more low volatility.

Generalized Autoregressive Conditional Heteroskedasticity (GARCH) is an extension of the ARCH model that incorporates a moving average component together with the autoregressive component. GARCH models assume that the variance of the error term follows an autoregressive moving average process.

The structure of the talk will flow like this:
• Quick refresher on Time Series
• Overview of ARCH/GARCH models
• Importance of these two models, when & why
• How to configure these models
• How to estimate and forecast them
• Real time case study in Python (from loading the data to model evaluation)

Goal

By the end of the talk, I promise you that you will get a fair understanding about ARCH/GARCH models, and you can straight away put them practice in your time series data.