Local Adaptive Multiplicative Error Models for High-Frequency Forecasts

  • We propose a local adaptive multiplicative error model (MEM) accommodating time-varying parameters. MEM parameters are adaptively estimated based on a sequential testing procedure. A data-driven optimal length of local windows is selected, yielding adaptive forecasts at each point in time. Analysing 1-minute cumulative trading volumes of five large NASDAQ stocks in 2008, we show that local windows of approximately 3 to 4 hours are reasonable to capture parameter variations while balancing modelling bias and estimation (in)efficiency. In forecasting, the proposed adaptive approach significantly outperforms a MEM where local estimation windows are fixed on an ad hoc basis.

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Metadaten
Author: Wolfgang Karl Härdle, Nikolaus Hautsch, Andrija Mihoci
URL:http://onlinelibrary.wiley.com/doi/10.1002/jae.2376/abstract
DOI:https://doi.org/10.1002/jae.2376
ISSN:1099-1255
Title of the source (English):Journal of Applied Econometrics
Document Type:Scientific journal article peer-reviewed
Language:English
Year of publication:2015
Tag:Forecasting; High-Frequency Processes; Local Adaptive Modelling; Multiplicative Error Model; Trading Volume
Volume/Year:30
Issue number:4
First Page:529
Last Page:550
Faculty/Chair:Fakultät 5 Wirtschaft, Recht und Gesellschaft / FG Wirtschaftsstatistik und Ökonometrie
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