Functional Inference for the Mean Curve of Intraday Spot Correlation
In this paper, we propose estimators for the mean of the daily spot-correlation curves, which are robust to the presence of finite-variation jumps in the price processes. The related feasible functional central limit theorems for the proposed estimators have been established under a joint infill and long-span setting. Based on the asymptotic theory, we develop a test procedure for flatness of the curves. Monte Carlo simulations confirm the strong finite-sample performance of the estimators. In an empirical application to 30 U.S. stocks from 2010 to 2019, we find a pronounced end-of-day surge in the mean of correlation curves across diverse stock groupings, alongside substantial calendar-time non-flatness in the curves.
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