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A Simple Nonparametric Long-Run Correlation Estimator - UCSD - math ucsd

A Simple Nonparametric Long-Run Correlation Estimator - UCSD - math ucsd 2025

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The document presents a nonparametric estimator for long-run correlation between two variables, specifically applied to stock returns in Latin America. It introduces a consistent method based on k-lag difference correlation, demonstrating its asymptotic equivalence to the Bartlett kernel spectral estimator. The paper discusses optimal lag-selection and alignment criteria, supported by Monte Carlo experiments that validate the estimator's effectiveness. Empirical results indicate significant increases in long-run correlations among American and Latin-American stock markets during the late 1990s, suggesting enhanced financial integration and common market components due to investment fund developments and capital account liberalization.

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