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Modeling tick-by-tick realized correlations

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  • Audrino, Francesco
  • Corsi, Fulvio

Abstract

A tree-structured heterogeneous autoregressive (tree-HAR) process is proposed as a simple and parsimonious model for the estimation and prediction of tick-by-tick realized correlations. The model can account for different time and other relevant predictors' dependent regime shifts in the conditional mean dynamics of the realized correlation series. Testing the model on S&P 500 Futures and 30-year Treasury Bond Futures realized correlations, empirical evidence that the tree-HAR model reaches a good compromise between simplicity and flexibility is provided. The model yields accurate single- and multi-step out-of-sample forecasts. Such forecasts are also better than those obtained from other standard approaches, in particular when the final goal is multi-period forecasting.

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Bibliographic Info

Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

Volume (Year): 54 (2010)
Issue (Month): 11 (November)
Pages: 2372-2382

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Handle: RePEc:eee:csdana:v:54:y:2010:i:11:p:2372-2382

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References

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Citations

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Cited by:
  1. Aslanidis, Nektarios & Christiansen, Charlotte, 2011. "Smooth Transition Patterns in the Realized Stock- Bond Correlation," Working Papers 2072/152138, Universitat Rovira i Virgili, Department of Economics.
  2. 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.
  3. Gribisch, Bastian, 2013. "A latent dynamic factor approach to forecasting multivariate stock market volatility," Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order 79823, Verein für Socialpolitik / German Economic Association.
  4. Philip Bertram & Robinson Kruse & Philipp Sibbertsen, 2013. "Fractional integration versus level shifts: the case of realized asset correlations," Statistical Papers, Springer, vol. 54(4), pages 977-991, November.
  5. Tsunehiro Ishihara & Yasuhiro Omori & Manabu Asai, 2013. "Matrix Exponential Stochastic Volatility with Cross Leverage," CIRJE F-Series CIRJE-F-904, CIRJE, Faculty of Economics, University of Tokyo.
  6. Roxana Halbleib & Valeri Voev, 2011. "Forecasting Covariance Matrices: A Mixed Frequency Approach," CREATES Research Papers 2011-03, School of Economics and Management, University of Aarhus.
  7. Boudt, Kris & Cornelissen, Jonathan & Croux, Christophe, 2012. "Jump robust daily covariance estimation by disentangling variance and correlation components," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 2993-3005.

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