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Modeling Tick-by-Tick Realized Correlations

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  • Fulvio Corsi

    ()

  • Francesco Audrino

    ()

Abstract

We propose a tree-structured heterogeneous autoregressive (tree-HAR) process 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 and 30-year treasury bond futures realized correlations, we provide empirical evidence that the tree-HAR model reaches a good compromise between simplicity and flexibility, and yields accurate single- and multi-step out-of-sample forecasts. Such forecasts are also better then those obtained from other standard approaches.

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Bibliographic Info

Paper provided by Department of Economics, University of St. Gallen in its series University of St. Gallen Department of Economics working paper series 2008 with number 2008-05.

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Length: 29 pages
Date of creation: Jan 2008
Date of revision:
Handle: RePEc:usg:dp2008:2008-05

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Keywords: High frequency data; Realized correlation; Stock-bond correlation; Tree-structured models; HAR; Regimes;

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References

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  1. Fabio Trojani & Francesco Audrino, 2005. "A general multivariate threshold GARCH model with dynamic conditional correlations," University of St. Gallen Department of Economics working paper series 2005 2005-04, Department of Economics, University of St. Gallen.
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Citations

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Cited by:
  1. Roxana Halbleib & Valeri Voev, 2012. "Forecasting Covariance Matrices: A Mixed Frequency Approach," Working Paper Series of the Department of Economics, University of Konstanz 2012-30, Department of Economics, University of Konstanz.
  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. 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.
  5. 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.
  6. 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.
  7. Nektarios Aslanidis & Charlotte Christiansen, 2010. "Smooth Transition Patterns in the Realized Stock Bond Correlation," CREATES Research Papers 2010-15, School of Economics and Management, University of Aarhus.

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