Local Adaptive Multiplicative Error Models for High-Frequency Forecasts

Wolfgang Karl Härdle · Nikolaus Hautsch · Andrija Mihoci

43 citations0/0 files storedJournal of Applied Econometrics

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Forecasting Techniques and Applications Financial Risk and Volatility Modeling Stock Market Forecasting Methods

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. Copyright © 2014 John Wiley & Sons, Ltd.

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References

1982Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation 1994Comparing Predictive Accuracy 1995Comparing Predictive Accuracy 1998Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transaction Data 1981On the bias in flexible functional forms and an essentially unbiased form 2008The Spline-GARCH Model for Low-Frequency Volatility and Its Global Macroeconomic Causes 2002New frontiers for arch models 2006Selection of estimation window in the presence of breaks

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