Wolfgang Karl Härdle
;
Nikolaus Hautsch
;
Andrija Mihoci

local adaptive multiplicative error models for high-frequency forecasts (replication data)

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.

Data and Resources

Suggested Citation

Härdle, Wolfgang Karl; Hautsch, Nikolaus; Mihoci, Andrija (2015): Local Adaptive Multiplicative Error Models for High-Frequency Forecasts (replication data). Version: 1. Journal of Applied Econometrics. Dataset. http://dx.doi.org/10.15456/jae.2022321.0721115370