ON CONDITIONALLY HETEROSCEDASTIC AR MODELS WITH THRESHOLDS

Conditional heteroscedasticity is prevalent in many time series. By viewing conditional heteroscedasticity as the consequence of a dynamic mixture of independent random variables, we develop a simple yet versatile observable mixing function, leading to the conditionally heteroscedastic AR model with thresholds, or a T-CHARM for short. We demonstrate its many attributes and provide comprehensive theoretical underpinnings with efficient computational procedures and algorithms. We compare, via simulation, the performance of T-CHARM with the GARCH model. We report some experiences using data from economics, biology, and geoscience.