Final answer:
GARCH, ARIMA, and exponential smoothing are all time series forecasting models used in statistics. The main difference between GARCH and ARIMA/exponential smoothing lies in the type of data they can handle and the assumptions they make.
Step-by-step explanation:
GARCH, ARIMA, and exponential smoothing are all time series forecasting models used in statistics. The main difference between GARCH and ARIMA/exponential smoothing lies in the type of data they can handle and the assumptions they make.
GARCH (Generalized Autoregressive Conditional Heteroskedasticity) is specifically designed to model and forecast volatility in financial data. It takes into account the conditional variance or volatility of the time series.
On the other hand, ARIMA (Autoregressive Integrated Moving Average) and exponential smoothing are more general-purpose models used for forecasting a wide range of time series data. ARIMA combines autoregressive, moving average, and differencing components, while exponential smoothing calculates a weighted average of past observations.