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The properties of realized correlation: Evidence from the French, German and Greek equity markets

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  • Vortelinos, Dimitrios I.

Abstract

In this paper I examine the properties of four realized correlation estimators and model their jumps. The correlations are between the French, German and Greek equity markets. Using intraday data I first construct four state-of-the-art realized correlation estimators which I then use to testing for normality, long-memory, asymmetries and jumps and also to modeling for jumps. Jumps are detected when the realized correlation is higher than 0.99 and lower than 0.01 in absolute values. Then the realized correlation is modeled with the simple Heterogeneous Autoregressive (HAR) model and the Heterogeneous Autoregressive model with Jumps (HAR-J).

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Bibliographic Info

Article provided by Elsevier in its journal The Quarterly Review of Economics and Finance.

Volume (Year): 50 (2010)
Issue (Month): 3 (August)
Pages: 273-290

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Handle: RePEc:eee:quaeco:v:50:y:2010:i:3:p:273-290

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Web page: http://www.elsevier.com/locate/inca/620167

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Keywords: DAX CAC40 Athens Stock Exchange Realized correlation Bipower variation Range Optimal sampling Long memory Asymmetry Jumps Heterogeneous autoregressive models;

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References

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  1. Michael W. Brandt & Francis X. Diebold, 2001. "A No-Arbitrage Approach to Range-Based Estimation of Return Covariances and Correlations," PIER Working Paper Archive 03-013, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 01 Apr 2003.
  2. Fulvio Corsi & Davide Pirino & Roberto Reno, 2009. "Volatility Forecasting: The Jumps Do Matter," Global COE Hi-Stat Discussion Paper Series gd08-036, Institute of Economic Research, Hitotsubashi University.
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  7. Neil Shephard & Ole Barndorff-Nielsen, 2003. "Econometrics of testing for jumps in financial economics using bipower variation," Economics Series Working Papers 2004-FE-01, University of Oxford, Department of Economics.
  8. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold, 2005. "Roughing it Up: Including Jump Components in the Measurement, Modeling and Forecasting of Return Volatility," NBER Working Papers 11775, National Bureau of Economic Research, Inc.
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  10. GIOT, Pierre & LAURENT, Sébastien & PETITJEAN, Mikael, . "Trading activity, realized volatility and jumps," CORE Discussion Papers RP -2223, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  11. Ferland, Rene & Lalancette, Simon, 2006. "Dynamics of realized volatilities and correlations: An empirical study," Journal of Banking & Finance, Elsevier, vol. 30(7), pages 2109-2130, July.
  12. Karim Bannouh & Dick van Dijk & Martin Martens, 2009. "Range-Based Covariance Estimation Using High-Frequency Data: The Realized Co-Range -super-," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 7(4), pages 341-372, Fall.
  13. Bollerslev, Tim & Zhou, Hao, 2006. "Volatility puzzles: a simple framework for gauging return-volatility regressions," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 123-150.
  14. 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.
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Cited by:
  1. Isao Ishida & Michael McAleer & Kosuke Oya, 2011. "Estimating the Leverage Parameter of Continuous-time Stochastic Volatility Models Using High Frequency S&P 500 and VIX," KIER Working Papers 759, Kyoto University, Institute of Economic Research.

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