Econometric Analysis of Realized Covariation: High Frequency Based Covariance, Regression, and Correlation in Financial Economics

TLDR

The paper analyzes multivariate high‑frequency financial data using realized covariation. The study introduces a new asymptotic distribution theory for regression, correlation, and covariance methods applied to high‑frequency financial data. The theory is derived for fixed time intervals (e.g., a day or week) where the number of high‑frequency returns tends to infinity, enabling analysis of how correlations, regressions, and covariances evolve over time. The authors provide confidence intervals for high‑frequency correlations, regressions, and covariances.

Abstract

This paper analyses multivariate high frequency financial data using realized covariation. We provide a new asymptotic distribution theory for standard methods such as regression, correlation analysis, and covariance. It will be based on a fixed interval of time (e.g., a day or week), allowing the number of high frequency returns during this period to go to infinity. Our analysis allows us to study how high frequency correlations, regressions, and covariances change through time. In particular we provide confidence intervals for each of these quantities.