Local Adaptive Multiplicative Error Models for High-Frequency Forecasts

  • We propose a local adaptive multiplicative error model (MEM) accommodating time varyingparameters. 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. Analyzing one-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:https://sfb649.wiwi.hu-berlin.de/papers/pdf/SFB649DP2012-031.pdf
Publisher:SFB 649
Place of publication:Berlin
Document Type:Report
Language:English
Year of publication:2012
Tag:Forecasting; High-Frequency Processes; Local Adaptive Modelling; Multiplicative Error Model; Trading Volume
Number of pages:30
Series ; volume number:SFB 649 Discussion Paper ; 2012,031
Faculty/Chair:Fakultät 5 Wirtschaft, Recht und Gesellschaft / FG Wirtschaftsstatistik und Ökonometrie
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