20.11.2013
Andrija Mihoci
(HU Berlin)

Härdle, W.K., Hautsch, N. and Mihoci, A.: "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. 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.