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Multiple Days Ahead Realized Volatility Forecasting: Single, Combined and Average Forecasts

Stavros Degiannakis

MPRA Paper from University Library of Munich, Germany

Abstract: The task of this paper is the enhancement of realized volatility forecasts. We investigate whether a mixture of predictions (either the combination or the averaging of forecasts) can provide more accurate volatility forecasts than the forecasts of a single model.We estimate long-memory and heterogeneous autoregressive models under symmetric and asymmetric distributions for the major European Union stock market indices and the exchange rates of the Euro. The majority of models provide qualitatively similar predictions for the next trading day’s volatility forecast. However, with regard to the one-week forecasting horizon, the heterogeneous autoregressive model is statistically superior to the long-memory framework. Moreover, for the two-weeks-ahead forecasting horizon, the combination of realized volatility predictions increases the forecasting accuracy and forecast averaging provides superior predictions to those supplied by a single model. Finally, the modeling of volatility asymmetry is important for the two-weeks-ahead volatility forecasts.

Keywords: averaging forecasts; combining forecasts; heterogeneous autoregressive; intra-day data; long memory; model confidence set; predictive ability; realized volatility; ultra-high frequency (search for similar items in EconPapers)
JEL-codes: C14 C32 C50 G11 G15 (search for similar items in EconPapers)
Date: 2018
New Economics Papers: this item is included in nep-for, nep-mst and nep-ore
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Published in Global Finance Journal 36 (2018): pp. 41-61

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