Optimal combinations of realised volatility estimators

Recent advances in financial econometrics have led to the development of new estimators of asset price variability using frequently-sampled price data, known as ''realised volatility estimators'' or simply ''realised measures''. These estimators rely on a variety of different assumptions and take many different functional forms. Motivated by the empirical success of combination forecasts, this paper presents a novel approach for combining individual realised measures to form new estimators of price variability. In an application to high frequency IBM price data over the period 1996-2008, we consider 32 different realised measures from 8 distinct classes of estimators. We find that a simple equally-weighted average of these estimators cannot generally be out-performed, in terms of accuracy, by any individual estimator. Moreover, we find that none of the individual estimators encompasses the information in all other estimators, providing further support for the use of combination realised measures.