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.
Author: | Wolfgang Karl Härdle, Nikolaus Hautsch, Andrija Mihoci |
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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 |