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Local Adaptive Multiplicative Error Models for High-Frequency Forecasts

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  • Wolfgang Karl Härdle
  • Nikolaus Hautsch
  • Andrija Mihoci

Abstract

We propose a local adaptive multiplicative error model (MEM) accommodating timevarying 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.

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Bibliographic Info

Paper provided by Sonderforschungsbereich 649, Humboldt University, Berlin, Germany in its series SFB 649 Discussion Papers with number SFB649DP2012-031.

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Length: 32 pages
Date of creation: Apr 2012
Date of revision:
Handle: RePEc:hum:wpaper:sfb649dp2012-031

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Keywords: multiplicative error model; local adaptive modelling; high-frequency processes; trading volume; forecasting;

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  1. Robert F. Engle & Jose Gonzalo Rangel, 2008. "The Spline-GARCH Model for Low-Frequency Volatility and Its Global Macroeconomic Causes," Review of Financial Studies, Society for Financial Studies, vol. 21(3), pages 1187-1222, May.
  2. Hautsch, Nikolaus & Malec, Peter & Schienle, Melanie, 2011. "Capturing the zero: A new class of zero-augmented distributions and multiplicative error processes," CFS Working Paper Series 2011/25, Center for Financial Studies (CFS).
  3. Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 253-63, July.
  4. Manganelli, Simone, 2002. "Duration, volume and volatility impact of trades," Working Paper Series 0125, European Central Bank.
  5. Meitz, Mika & Teräsvirta, Timo, 2004. "Evaluating models of autoregressive conditional duration," Working Paper Series in Economics and Finance 557, Stockholm School of Economics, revised 13 Dec 2004.
  6. Chen, Ying & Härdle, Wolfgang Karl & Pigorsch, Uta, 2010. "Localized Realized Volatility Modeling," Journal of the American Statistical Association, American Statistical Association, vol. 105(492), pages 1376-1393.
  7. Christian T. Brownlees & Fabrizio Cipollini & Giampiero M. Gallo, 2009. "Intra-daily Volume Modeling and Prediction for Algorithmic Trading," Econometrics Working Papers Archive wp2009_01, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
  8. P. Č�žek & W. H�rdle & V. Spokoiny, 2009. "Adaptive pointwise estimation in time-inhomogeneous conditional heteroscedasticity models," Econometrics Journal, Royal Economic Society, vol. 12(2), pages 248-271, 07.
  9. Robert F. Engle & Jeffrey R. Russell, 1998. "Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transaction Data," Econometrica, Econometric Society, vol. 66(5), pages 1127-1162, September.
  10. Robert Engle, 2002. "New frontiers for arch models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(5), pages 425-446.
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