A Multiplicative Error Model with Heterogeneous Components for Forecasting Realized Volatility

To forecast realized volatility, this paper introduces a multiplicative error model that incorporates heterogeneous components: weekly and monthly realized volatility measures. While the model captures the long-memory property, estimation simply proceeds using quasi-maximum likelihood estimation. This paper investigates its forecasting ability using the realized kernels of 34 different assets provided by the Oxford-Man Institute's Realized Library. The model outperforms benchmark models such as ARFIMA, HAR, Log-HAR and HEAVY-RM in within-sample fitting and out-of-sample (1-, 10- and 22-step) forecasts. It performed best in both pointwise and cumulative comparisons of multi-step-ahead forecasts, regardless of loss function (QLIKE or MSE). Copyright (c) 2015John Wiley & Sons, Ltd.