Why It Is Ok To Use The Har-Rv(1,5,21) Model
AbstractThe 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 InfoPaper provided by University of Central Missouri, Department of Economics & Finance in its series Working Papers with number 1201.
Length: 29 pages
Date of creation: Aug 2012
Date of revision: Aug 2012
Time Series; Financial Econometrics; HAR-RV Model;
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- Barucci, Emilio & Reno, Roberto, 2002. "On measuring volatility of diffusion processes with high frequency data," Economics Letters, Elsevier, vol. 74(3), pages 371-378, February.
- Hillebrand, Eric, 2005. "Neglecting parameter changes in GARCH models," Journal of Econometrics, Elsevier, vol. 129(1-2), pages 121-138.
- Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 1999.
"Exchange Rate Returns Standardized by Realized Volatility are (Nearly) Gaussian,"
New York University, Leonard N. Stern School Finance Department Working Paper Seires
99-060, New York University, Leonard N. Stern School of Business-.
- Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 2000. "Exchange Rate Returns Standardized by Realized Volatility are (Nearly) Gaussian," NBER Working Papers 7488, National Bureau of Economic Research, Inc.
- 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.
- Andersen, Torben G. & Bollerslev, Tim & Huang, Xin, 2011.
"A reduced form framework for modeling volatility of speculative prices based on realized variation measures,"
Journal of Econometrics,
Elsevier, vol. 160(1), pages 176-189, January.
- Torben G. Andersen & Tim Bollerslev & Xin Huang, 2007. "A Reduced Form Framework for Modeling Volatility of Speculative Prices based on Realized Variation Measures," CREATES Research Papers 2007-14, School of Economics and Management, University of Aarhus.
- Michael McAleer & Marcelo Medeiros, 2008.
"Realized Volatility: A Review,"
Taylor & Francis Journals, vol. 27(1-3), pages 10-45.
- Ole E. Barndorff-Nielsen & Peter R. Hansen & Asger Lunde & Neil Shephard, 2006.
"Subsampling realised kernels,"
OFRC Working Papers Series
2006fe06, Oxford Financial Research Centre.
- Neil Shephard & Ole E. Barndorff-Nielsen, 2006. "Subsampling realised kernels," Economics Series Working Papers 278, University of Oxford, Department of Economics.
- Ole E. Barndorff-Nielsen & Peter Reinhard Hansen & Asger Lunde & Neil Shephard, 2006. "Subsampling realised kernels," Economics Papers 2006-W10, Economics Group, Nuffield College, University of Oxford.
- Lamoureux, Christopher G & Lastrapes, William D, 1990. "Persistence in Variance, Structural Change, and the GARCH Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(2), pages 225-34, April.
- Maria Elvira Mancino & Paul Malliavin, 2002. "Fourier series method for measurement of multivariate volatilities," Finance and Stochastics, Springer, vol. 6(1), pages 49-61.
- Michael McAller & Marcelo C. Medeiros, 2007.
"A multiple regime smooth transition heterogeneous autoregressive model for long memory and asymmetries,"
Textos para discussÃ£o
544, Department of Economics PUC-Rio (Brazil).
- McAleer, Michael & Medeiros, Marcelo C., 2008. "A multiple regime smooth transition Heterogeneous Autoregressive model for long memory and asymmetries," Journal of Econometrics, Elsevier, vol. 147(1), pages 104-119, November.
- 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.
- Lan Zhang & Per A. Mykland & Yacine Ait-Sahalia, 2003. "A Tale of Two Time Scales: Determining Integrated Volatility with Noisy High Frequency Data," NBER Working Papers 10111, National Bureau of Economic Research, Inc.
- Andersen T. G & Bollerslev T. & Diebold F. X & Labys P., 2001. "The Distribution of Realized Exchange Rate Volatility," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 42-55, March.
- Christensen, Kim & Oomen, Roel & Podolskij, Mark, 2010.
"Realised quantile-based estimation of the integrated variance,"
Journal of Econometrics,
Elsevier, vol. 159(1), pages 74-98, November.
- Kim Christensen & Roel Oomen & Mark Podolskij, 2009. "Realised Quantile-Based Estimation of the Integrated Variance," CREATES Research Papers 2009-27, School of Economics and Management, University of Aarhus.
- Kim Christensen & Roel Oomen & Mark Podolskij, 2010. "Realised quantile-based estimation of the integrated variance," Post-Print peer-00732538, HAL.
- Thomas Mikosch & Catalin Starica, 2004. "Non-stationarities in financial time series, the long range dependence and the IGARCH effects," Econometrics 0412005, EconWPA.
- Baillie, Richard T. & Bollerslev, Tim & Mikkelsen, Hans Ole, 1996.
"Fractionally integrated generalized autoregressive conditional heteroskedasticity,"
Journal of Econometrics,
Elsevier, vol. 74(1), pages 3-30, September.
- Tom Doan, . "RATS programs to replicate Baillie, Bollerslev, Mikkelson FIGARCH results," Statistical Software Components RTZ00009, Boston College Department of Economics.
- Andersen, Torben G & Bollerslev, Tim, 1998. "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 885-905, November.
- 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.
- 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.
- 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.
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