Modeling and forecasting outliers and level shifts in absolute returns

Due to high and low volatility periods, time series of absolute returns experience temporary level shifts (that is, periods with outliers) which differ in length and size. In this paper we put forward a new model which can describe and forecast the location and size of such level shifts. Our so called Switching Regime Censored Latent Effects Autoregression [SR-CLEAR] assumes that technical trading rules may have explanatory value for future volatility. It is assumed that these rules have a time-varying effect on absolute returns, and that this effect appears as an outlier or a level shift. We apply the SR-CLEAR model to nine stock markets and we document its excellent fit and competitive forecasting ability.