Local adaptive multiplicative error models for high-frequency forecasts

Wolfgang Härdle, Nikolaus Hautsch and Andrija Mihoci

No 2012-031, SFB 649 Discussion Papers from Humboldt University Berlin, Collaborative Research Center 649: Economic Risk

Abstract: We propose a local adaptive multiplicative error model (MEM) accommodating timevarying 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. 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.

Keywords: multiplicative error model; local adaptive modelling; high-frequency processes; trading volume; forecasting (search for similar items in EconPapers)
JEL-codes: C41 C51 C53 G12 G17 (search for similar items in EconPapers)
Date: 2012
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Journal Article: Local Adaptive Multiplicative Error Models for High‐Frequency Forecasts (2015)
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