Occasional structural breaks and long memory with an application to the S&P 500 absolute stock returns

This paper shows that occasional breaks generate slowly decaying autocorrelations and other properties of I( d) processes, where d can be a fraction. Some theory and simulation results show that it is not easy to distinguish between the long memory property from the occasional-break process and the one from the I( d) process. We compare two time series models, an occasional-break model and an I( d) model to analyze S&P 500 absolute stock returns. An occasional-break model performs marginally better than an I( d) model in terms of in-sample fitting. In general, we found that an occasional-break model provides less competitive forecasts, but not significantly. However, the empirical results suggest a possibility such that, at least, part of the long memory may be caused by the presence of neglected breaks in the series. We show that the forecasts by an occasional break model incorporate incremental information regrading future volatility beyond that found in I( d) model. The findings enable improvements of volatility prediction by combining I( d) model and occasional-break model.