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...

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Bibliographic Details
Published in:Journal of applied econometrics (Chichester, England) England), 2015-06, Vol.30 (4), p.529-550
Main Authors: Härdle, Wolfgang K., Hautsch, Nikolaus, Mihoci, Andrija
Format: Article
Language:English
Subjects:
Bias
Error correction models
Estimation
Forecasts
Modelling
Trade
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Summary: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.
ISSN:0883-7252
1099-1255
DOI:10.1002/jae.2376