@misc{HaerdleHautschMihoci, author = {H{\"a}rdle, Wolfgang Karl and Hautsch, Nikolaus and Mihoci, Andrija}, title = {Local Adaptive Multiplicative Error Models for High-Frequency Forecasts}, series = {Journal of Applied Econometrics}, volume = {30}, journal = {Journal of Applied Econometrics}, number = {4}, issn = {1099-1255}, doi = {10.1002/jae.2376}, pages = {529 -- 550}, abstract = {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.}, language = {en} } @techreport{HaerdleHautschMihoci, author = {H{\"a}rdle, Wolfgang Karl and Hautsch, Nikolaus and Mihoci, Andrija}, title = {Local Adaptive Multiplicative Error Models for High-Frequency Forecasts}, publisher = {SFB 649}, address = {Berlin}, pages = {30}, abstract = {We propose a local adaptive multiplicative error model (MEM) accommodating time varyingparameters. 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.}, language = {en} }