The prices of financial securities are often shaken by large and time-varying shocks. The amplitudes of these price movements are not constant over time. There are periods of high volatility and periods of low volatility. Within these periods, volatility seems to be positively autocorrelated: high amplitudes are likely to be followed by high amplitudes and low amplitudes by low amplitudes. This observation which is particularly relevant for high-frequency data, such as daily stock market returns, implies that the conditional variance of the one-period forecast error is no longer constant (homoskedastic), but time-varying (heteroskedastic) and autocorrelated.