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Common Intraday Periodicity

  • Alain Hecq
  • Sébastien Laurent
  • Franz C. Palm

Using a reduced rank regression framework as well as information criteria, we investigate the presence of commonalities in the intraday periodicity, a dominant feature in the return volatility of most intraday financial time series. We find that the test has little size distortion and reasonable power even in the presence of jumps. We also find that only three factors are needed to describe the intraday periodicity of 30 U.S. asset returns sampled at the 5-minute frequency. Interestingly, we find that for most series, the models imposing these commonalities deliver better forecasts of the conditional intraday variance than those where the intraday periodicity is estimated for each asset separately. Copyright The Author 2011. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail:, Oxford University Press.

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Article provided by Society for Financial Econometrics in its journal Journal of Financial Econometrics.

Volume (Year): 10 (2011)
Issue (Month): 2 (2012 20 12)
Pages: 325-353

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Handle: RePEc:oup:jfinec:v:10:y:2011:i:2:p:325-353
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  1. Engle, Robert F & Hylleberg, Svend, 1996. "Common Seasonal Features: Global Unemployment," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 58(4), pages 615-30, November.
  2. Marcel P. Visser, 2011. "GARCH Parameter Estimation Using High-Frequency Data," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 9(1), pages 162-197, Winter.
  3. Raffaella Giacomini & Halbert White, 2003. "Tests of conditional predictive ability," Boston College Working Papers in Economics 572, Boston College Department of Economics.
  4. Torben G. Andersen & Dobrislav Dobrev & Ernst Schaumburg, 2009. "Jump-Robust Volatility Estimation using Nearest Neighbor Truncation," CREATES Research Papers 2009-52, School of Economics and Management, University of Aarhus.
  5. Hansen, Peter Reinhard, 2005. "A Test for Superior Predictive Ability," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 365-380, October.
  6. Dionne, Georges & Duchesne, Pierre & Pacurar, Maria, 2009. "Intraday Value at Risk (IVaR) using tick-by-tick data with application to the Toronto Stock Exchange," Journal of Empirical Finance, Elsevier, vol. 16(5), pages 777-792, December.
  7. Pierre Giot, 2005. "Market risk models for intraday data," The European Journal of Finance, Taylor & Francis Journals, vol. 11(4), pages 309-324.
  8. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold, 2007. "Roughing It Up: Including Jump Components in the Measurement, Modeling and Forecasting of Return Volatility," CREATES Research Papers 2007-18, School of Economics and Management, University of Aarhus.
  9. George Athanasopoulos & Osmani Teixeira de Carvalho Guillén & João Victor Issler & Farshid Vahid, 2010. "Model selection, estimation and forecasting in VAR models with short-run and long-run restrictions," Working Papers Series 205, Central Bank of Brazil, Research Department.
  10. Hansen, Peter Reinhard & Lunde, Asger, 2006. "Consistent ranking of volatility models," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 97-121.
  11. Hecq Alain & Laurent Sébastien & Palm Franz, 2011. "On the Univariate Representation of Multivariate Volatility Models with Common Factors," Research Memorandum 011, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
  12. Nelson, Daniel B., 1990. "ARCH models as diffusion approximations," Journal of Econometrics, Elsevier, vol. 45(1-2), pages 7-38.
  13. Drost, Feike C. & Werker, Bas J. M., 1996. "Closing the GARCH gap: Continuous time GARCH modeling," Journal of Econometrics, Elsevier, vol. 74(1), pages 31-57, September.
  14. 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.
  15. Sébastien Laurent & Jeroen V.K. Rombouts & Francesco Violante, 2010. "On the Forecasting Accuracy of Multivariate GARCH Models," Cahiers de recherche 1021, CIRPEE.
  16. Taylor, Stephen J. & Xu, Xinzhong, 1997. "The incremental volatility information in one million foreign exchange quotations," Journal of Empirical Finance, Elsevier, vol. 4(4), pages 317-340, December.
  17. Engle, Robert F. & Marcucci, Juri, 2006. "A long-run Pure Variance Common Features model for the common volatilities of the Dow Jones," Journal of Econometrics, Elsevier, vol. 132(1), pages 7-42, May.
  18. Ole E. Barndorff-Nielsen & Neil Shephard, 2003. "Power and bipower variation with stochastic volatility and jumps," Economics Papers 2003-W17, Economics Group, Nuffield College, University of Oxford.
  19. Fulvio Corsi, 2009. "A Simple Approximate Long-Memory Model of Realized Volatility," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 7(2), pages 174-196, Spring.
  20. Suzanne S. Lee & Per A. Mykland, 2008. "Jumps in Financial Markets: A New Nonparametric Test and Jump Dynamics," Review of Financial Studies, Society for Financial Studies, vol. 21(6), pages 2535-2563, November.
  21. Andrew Patton, 2006. "Volatility Forecast Comparison using Imperfect Volatility Proxies," Research Paper Series 175, Quantitative Finance Research Centre, University of Technology, Sydney.
  22. Andersen, Torben G. & Bollerslev, Tim, 1997. "Intraday periodicity and volatility persistence in financial markets," Journal of Empirical Finance, Elsevier, vol. 4(2-3), pages 115-158, June.
  23. Engle, Robert F & Susmel, Raul, 1993. "Common Volatility in International Equity Markets," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(2), pages 167-76, April.
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