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Why It Is Ok To Use The Har-Rv(1,5,21) Model

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Author Info

  • Mihaela Craioveanu

    ()
    (University of Central Missouri)

  • Eric Hillebrand

    ()
    (Aarhus University)

Abstract

The lag structure (1,5,21) is most commonly used for the HAR-RV model for realized volatility (Corsi 2009), where the terms are thought to represent a daily, a weekly, and a monthly time scale. The aggregation of the three scales approximates long mem- ory. We explore flexible lag selection for the model on realized volatility constructed from tick-level data of the thirty constituting stocks of the Dow Jones Industrial Average between 1995 and 2007. The computational costs for flexible lag selection are substantial, and we use a parallel computing environment. We find that flexible lags do not improve in-sample or out-of-sample fit. Our results therefore confirm the standard practice in a large-scale data application.

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Bibliographic Info

Paper provided by University of Central Missouri, Department of Economics & Finance in its series Working Papers with number 1201.

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Length: 29 pages
Date of creation: Aug 2012
Date of revision: Aug 2012
Handle: RePEc:umn:wpaper:1201

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Keywords: Time Series; Financial Econometrics; HAR-RV Model;

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References

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  7. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 1999. "Exchange Rate Returns Standardized by Realized Volatility Are (Nearly) Gaussian," Center for Financial Institutions Working Papers 00-29, Wharton School Center for Financial Institutions, University of Pennsylvania.
  8. Michael McAller & Marcelo C. Medeiros, 2007. "A multiple regime smooth transition heterogeneous autoregressive model for long memory and asymmetries," Textos para discussão 544, Department of Economics PUC-Rio (Brazil).
  9. Zhou, Bin, 1996. "High-Frequency Data and Volatility in Foreign-Exchange Rates," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(1), pages 45-52, January.
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  15. Zhang, Lan & Mykland, Per A. & Ait-Sahalia, Yacine, 2005. "A Tale of Two Time Scales: Determining Integrated Volatility With Noisy High-Frequency Data," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 1394-1411, December.
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Cited by:
  1. Shcherba, Alexandr, 2014. "Comparing «Realized volatility» models in the VaR calculation for the Russian equity market," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 34(2), pages 120-136.
  2. Fengler, Matthias R. & Mammen, Enno & Vogt, Michael, 2013. "Additive modeling of realized variance: tests for parametric specifications and structural breaks," Economics Working Paper Series 1332, University of St. Gallen, School of Economics and Political Science.
  3. Chevallier, Julien & Ielpo, Florian & Sévi, Benoît, 2011. "Do jumps help in forecasting the density of returns?," Economics Papers from University Paris Dauphine 123456789/6805, Paris Dauphine University.
  4. Audrino, Francesco & Knaus, Simon, 2012. "Lassoing the HAR model: A Model Selection Perspective on Realized Volatility Dynamics," Economics Working Paper Series 1224, University of St. Gallen, School of Economics and Political Science.

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