Title: A simple model correction for modelling and forecasting (un)reliable realized volatility
Authors: Rodrigo Hizmeri - Lancaster University (United Kingdom) [presenting]
Marwan Izzeldin - Lancaster University Management School (United Kingdom)
Mike Tsionas - Lancaster University (United Kingdom)
Abstract: We propose a dilution bias correction approach to deal with the errors-in-variables problem observed in realized volatility (RV) measures. The absolute difference between daily and monthly RV is shown to be proportional to the relative magnitude of the measurement error. Therefore, in implementing the latter metric, and in allowing the daily autoregressive parameter to vary as a function of the error term, the result is more responsive forecasts with greater persistence (faster mean-reversion) when the measurement error is low (high). Empirical results indicate that our models outperform some of the most popular HAR and GARCH models across various forecasting horizons.