ARCH and GARCH models
Topic of Forecasting, part of Time series
ARCH and GARCH models
| Type | Topic |
| Taught in | Forecasting |
| Part | Time series |
| Questions using it | 1 |
Autoregressive conditional heteroscedasticity: models in which the variance of today’s innovation depends on yesterday’s squared innovations (ARCH) and, in GARCH, also on yesterday’s conditional variance. They describe volatility clustering in financial returns. The course names them and does not develop them; concept check C50 asks about them.
In Forecasting
From Forecasting
Autoregressive conditional heteroscedasticity, and its generalisations, put a time-series model on the variance rather than only on the mean. They are not developed here. Count-valued series are named and not developed, for a reason that has run through the earlier chapters: a Poisson observation is not a mean plus an innovation that can be separated off. Forecasting is the end of the material.
Used in
- Taught in Forecasting
- Questions that use it (1) — C50
- All 2 questions on Forecasting
See also
Autoregression and moving averages · Variance · Accidental-deaths data · Traffic data · Partial autocorrelation · ARIMA model · Unit root · Seasonality