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Obtaining and Predicting the Bounds of Realized Correlations

Summary

This paper argues that the inherent data problems make precise point identification of realized correlation difficult but identification bounds in the spirit of Manski (1995) can be derived. These identification bounds allow for a more robust approach to inference especially when the realized correlation is used for estimating other risk measures. We forecast the identification bounds using the HAR model of Corsi (2003) using data during the year of onset of the credit crisis and find that the bounds provide good predictive coverage of the realized correlation for both 1- and 10-step forecasts even in volatile periods.

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Correspondence to Lidan Grossmass.

Additional information

The research leading to these results has received funding from the European Community’s Seventh Framework Programme FP7-PEOPLE-ITN-2008 under grant agreement number PITN-GA-2009-237984. The funding is gratefully acknowledged. We would also like to thank Klaus Neusser (the Editor), an anonymous referee, Charles Manski, Winfried Pohlmeier, Peter Reinhard Hansen and Hao Liu for their insightful comments and suggestions.

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Grossmass, L. Obtaining and Predicting the Bounds of Realized Correlations. Swiss J Economics Statistics 150, 191–226 (2014). https://doi.org/10.1007/BF03399406

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Keyword

  • High Frequency Data
  • Realized Covariance
  • Partial Identification
  • Bounds

JEL-Classification

  • C14
  • C18
  • C58
  • G17