A Robust Sign-Based Estimator for Intraday Correlation and Market Betas

Summary

The document introduces a subsampled quadrant correlation estimator for high-frequency financial data. It addresses the difficulty of measuring correlation when volatility changes over time: the standard quadrant estimator is robust to volatility dynamics but can be inefficient under Gaussian sampling. The proposed variant uses sign information and subsampling to improve efficiency while retaining robustness and computational simplicity. Apart from the sampling horizon, it does not require a smoothing parameter, which may help when analyzing large cross sections.

In an empirical study of a broad panel of U.S. stocks, the authors find substantial intraday variation in market betas. Their decomposition attributes broadly similar declines in relative volatility across stocks as the day progresses, while differences in individual stocks' beta patterns are mainly associated with intraday changes in correlation. The summary does not give estimator settings, sample dates, or accuracy comparisons, so it offers no basis here for judging performance in other markets or under different sampling choices.

Key ideas

• Changing volatility can make high-frequency correlation measurement difficult.
• The proposed subsampled quadrant estimator uses signs to remain robust to volatility dynamics.
• Subsampling is intended to improve the efficiency of the quadrant estimator under Gaussian sampling.
• The estimator needs no smoothing parameter beyond the sampling horizon.
• In a broad U.S. stock panel, intraday beta differences are attributed mainly to changing correlations across stocks.

Tags

# Robust Estimation of Realized Correlation: New Insights about Intraday Fluctuations in Market Betas


# Robust Estimation of Realized Correlation: New Insights about Intraday Fluctuations in Market Betas









Time-varying volatility is an inherent feature of economic time series and complicates correlation measurement at high frequencies. While the quadrant correlation estimator is robust to volatility dynamics, it has low efficiency under Gaussian sampling. We introduce a subsampled quadrant correlation estimator that improves efficiency while retaining robustness and computational simplicity. The estimator is based solely on sign information and involves no smoothing parameter beyond the sampling horizon, which makes it attractive for high-frequency financial data and large cross sections. An empirical application to a broad panel of U.S. stocks reveals substantial intraday variation in market betas. Decomposing beta dynamics shows that relative volatility declines over the day by a similar amount for all stocks, so that the differences across stocks in how their betas move within the day are predominantly driven by differences in intraday correlation changes.

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