  Time-varying volatility is an inherent feature of most economic time-series,
which causes standard correlation estimators to be inconsistent. The quadrant
correlation estimator is consistent but very inefficient. We propose a novel
subsampled quadrant estimator that improves efficiency while preserving
consistency and robustness. This estimator is particularly well-suited for
high-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 market
betas by decomposing them into time-varying correlations and relative
volatility changes. Our results show that intraday variation in betas is
primarily driven by intraday variation in correlations.
