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. Th...

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Veröffentlicht in:Journal of forecasting 2015-04, Vol.34 (3), p.209-219
Hauptverfasser: Han, Heejoon, Park, Myung D., Zhang, Shen
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description 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 © 2015 John Wiley & Sons, Ltd.
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source Wiley Online Library Journals Frontfile Complete; Business Source Complete
subjects Benchmarking
Comparative analysis
Econometrics
Economic models
Estimation
forecasting
Forecasting techniques
long-memory property
Maximum likelihood method
multiplicative error model
realized volatility
Studies
Volatility
title A Multiplicative Error Model with Heterogeneous Components for Forecasting Realized Volatility
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