Hands-On Machine Learning for Algorithmic Trading

How to use time series models to forecast volatility

A particularly important area of application for univariate time series models is the prediction of volatility. The volatility of financial time series is usually not constant over time but changes, with bouts of volatility clustering together. Changes in variance create challenges for time series forecasting using the classical ARIMA models. To address this challenge, we will now model volatility so that we can predict changes in variance.

Heteroskedasticity is the technical term for changes in a variable's variance. The autoregressive conditional heteroskedasticity (ARCH) model expresses the variance of the error term as a function of the errors in previous periods. More specifically, ...