Abstract
In time series analyses, just as in regression, it is assumed that the residuals (or errors) are homoscedastic. In a seminal article, Engel (1982) suggested that heteroscedasticity of residuals might well occur in certain time series contexts. Engle had noticed that in studies of forecasting, especially in speculative markets such as foreign exchange rates and stock market returns, large and small errors tended to occur in clusters. The evidence is that in the context of financial time series, volatility clustering is common. Volatility clustering describes the tendency of large changes (of either sign) in, for example, asset prices to follow other large changes; small changes (of either sign) tend to follow small changes. In other words, the current level of volatility tends to be positively (auto) correlated with its level during the immediately preceding time periods.
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Aljandali, A., Tatahi, M. (2018). Modelling Volatility in Finance and Economics: ARCH, GARCH and EGARCH Models. In: Economic and Financial Modelling with EViews. Statistics and Econometrics for Finance. Springer, Cham. https://doi.org/10.1007/978-3-319-92985-9_8
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