Obtaining and Predicting the Bounds of Realized Correlations

45 Pages Posted: 10 May 2013

See all articles by Lidan Grossmass

Lidan Grossmass

Heinrich Heine University Dusseldorf

Date Written: May 9, 2013

Abstract

The problem of estimation of realized correlation, which is analogous to realized covariance, is compounded by effects of market microstructure, noise, and asynchronous trading. Various methods have been proposed to decrease the biases, but they require assumptions to be made that may be unrealistic. 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 one step and ten step forecasts, even in volatile periods.

Keywords: high frequency data, realized covariance, partial identification, bounds

JEL Classification: C14, C18, C58, G17

Suggested Citation

Grossmass, Lidan, Obtaining and Predicting the Bounds of Realized Correlations (May 9, 2013). Available at SSRN: https://ssrn.com/abstract=2261402 or http://dx.doi.org/10.2139/ssrn.2261402

Lidan Grossmass (Contact Author)

Heinrich Heine University Dusseldorf ( email )

Universitätsstrasse 1
Duesseldorf, DE NRW 40225
Germany

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