Robust volatility forecasts in the presence of structural breaks

Financial time series often undergo periods of structural change that yield biased estimates or forecasts of volatility and thereby risk management measures. We show that in the context of GARCH diffusion models ignoring structural breaks in the leverage coefficient and the constant can lead to biased and inefficient AR-RV and GARCH-type volatility estimates. Similarly, we find that volatility forecasts based on AR-RV and GARCH-type models that take into account structural breaks by estimating the parameters only in the post-break period, significantly outperform those that ignore them. Hence, we propose a Flexible Forecast Combination method that takes into account not only information from different volatility models, but from different subsamples as well. This methods consists of two main steps: First, it splits the estimation period in subsamples based on estimated structural breaks detected by a change-point test. Second, it forecasts volatility weighting information from all subsamples by minimizing a particular loss function, such as the Square Error and QLIKE. An empirical application using the S&P 500 Index shows that our approach performs better, especially in periods of high volatility, compared to a large set of individual volatility models and simple averaging methods as well as Forecast Combinations under Regime Switching.