Modeling volatility with time-varying FIGARCH models

This paper puts the light on a new class of time-varying FIGARCH or TV-FIGARCH processes to model the volatility. This new model has the feature to account for the long memory and the structural change in the conditional variance process. The structural change is modeled by a logistic function allowing the intercept to vary over time. We also implement a modeling strategy for our TV-FIGARCH specification whose performance is examined by a Monte Carlo study. An empirical application to the crude oil price and the S&P 500 index is carried out to illustrate the usefulness of our techniques. The main result of this paper is that the long memory behavior of the absolute returns is not only explained by the existence of the long memory in the volatility but also by deterministic changes in the unconditional variance.

Research Highlights► The TV-FIGARCH model captures the long memory and the changes in the variance. ► In this study we implement a modelling strategy using a sequential testing procedure. ► We model the WTI and the S&P 500 returns. ► Parameter constancy hypothesis is strongly rejected for both returns. ► Long memory is present in both volatilities.