Local Adaptive Multiplicative Error Models for High- Frequency Forecasts

SFB 649 Discussion Paper No. 2012-031

33 Pages Posted: 25 Aug 2013

See all articles by Wolfgang K. Härdle

Wolfgang K. Härdle

Humboldt University of Berlin - Institute for Statistics and Econometrics; Humboldt University of Berlin - Center for Applied Statistics and Economics (CASE)

Nikolaus Hautsch

University of Vienna - Department of Statistics and Operations Research; Center for Financial Studies (CFS); Vienna Graduate School of Finance (VGSF)

Andrija Mihoci

Humboldt University of Berlin - C.A.S.E., Center for Applied Statistics and Economics

Date Written: 2012

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. 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 modeling, high-frequency processes, trading volume, forecasting

JEL Classification: C41, C51, C53, G12, G17

Suggested Citation

Härdle, Wolfgang K. and Hautsch, Nikolaus and Mihoci, Andrija, Local Adaptive Multiplicative Error Models for High- Frequency Forecasts (2012). SFB 649 Discussion Paper No. 2012-031. Available at SSRN: https://ssrn.com/abstract=2315830 or http://dx.doi.org/10.2139/ssrn.2315830

Wolfgang K. Härdle

Humboldt University of Berlin - Institute for Statistics and Econometrics ( email )

Unter den Linden 6
Berlin, D-10099
Germany
+49 30 2093 5631 (Phone)
+49 30 2093 5649 (Fax)

Humboldt University of Berlin - Center for Applied Statistics and Economics (CASE)

Unter den Linden 6
Berlin, D-10099
Germany

Nikolaus Hautsch (Contact Author)

University of Vienna - Department of Statistics and Operations Research ( email )

Oskar-Morgenstern-Platz 1
Vienna, A-1090
Austria

Center for Financial Studies (CFS) ( email )

Grüneburgplatz 1
Frankfurt am Main, 60323
Germany

Vienna Graduate School of Finance (VGSF) ( email )

Welthandelsplatz 1
Vienna, 1020
Austria

Andrija Mihoci

Humboldt University of Berlin - C.A.S.E., Center for Applied Statistics and Economics ( email )

Unter den Linden 6
Berlin, AK 10099
Germany

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