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Time-varying volatility is an inherent feature of most economic time-series,which causes standard correlation estimators to be inconsistent. The quadrantcorrelation estimator is consistent but very inefficient. We propose a novelsubsampled quadrant estimator that improves efficiency while preservingconsistency and robustness. This estimator is particularly well-suited forhigh-frequency financial data and we apply it to a large panel of US stocks.Our empirical analysis sheds new light on intra-day fluctuations in marketbetas by decomposing them into time-varying correlations and relativevolatility changes. Our results show that intraday variation in betas isprimarily driven by intraday variation in correlations.

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