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Optimal Time Interval Selection in Long-Run Correlation Estimation

Author

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  • Pedro Albuquerque

    (KEDGE Business School [Marseille], AMSE - Aix-Marseille Sciences Economiques - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique)

Abstract

This paper presents an asymptotically optimal time interval selection criterion for the long-run correlation block estimator (Bartlett kernel estimator) based on the Newey–West and Andrews–Monahan approaches. An alignment criterion that enhances finite-sample performance is also proposed. The procedure offers an optimal alternative to the customary practice in finance and economics of heuristically or arbitrarily choosing time intervals or lags in correlation studies. A Monte Carlo experiment using parameters derived from Dow Jones returns data confirms that the procedure can be MSE-superior to alternatives such as aggregation over arbitrary time intervals, parametric VAR, and Newey–West covariance matrix estimation with automatic lag selection.

Suggested Citation

  • Pedro Albuquerque, 2020. "Optimal Time Interval Selection in Long-Run Correlation Estimation," Post-Print hal-02482675, HAL.
    • Handle: RePEc:hal:journl:hal-02482675
    DOI: 10.1007/s40953-019-00175-x
    Note: View the original document on HAL open archive server: https://amu.hal.science/hal-02482675
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    2. Nektarios Aslanidis & Christos S. Savva, 2011. "Are There Still Portfolio Diversification Benefits In Eastern Europe? Aggregate Versus Sectoral Stock Market Data," Manchester School, University of Manchester, vol. 79(6), pages 1323-1352, December.

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    More about this item

    Keywords

    Bartlett; Lag selection; Alignment; Newey–West; Andrews–Monahan; Long-run correlation;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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