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Long-Term Dependence and Least Squares Regression in Investment Analysis

Author

Listed:
  • Myron T. Greene

    (Georgia State University)

  • Bruce D. Fielitz

    (Georgia State University)

Abstract

It is widely assumed that common stock returns approximate a random walk, i.e., the returns are assumed to be serially independent. As a consequence, estimates of systematic risk and efficient portfolios are usually developed using any convenient differencing interval with the implication that they are applicable to any investor regardless of his horizon period. This paper derives the relationships between least-squares estimators and the differencing interval in the presence of long-term dependence. These relationships are then used to show how long-term dependence affects estimates of systematic risk and efficient portfolios selected with the Sharpe index model. The major implication is that, because of long-term dependence, systematic risk estimates and efficient portfolios must be developed using a differencing interval exactly equal to the investor's horizon period.

Suggested Citation

  • Myron T. Greene & Bruce D. Fielitz, 1980. "Long-Term Dependence and Least Squares Regression in Investment Analysis," Management Science, INFORMS, vol. 26(10), pages 1031-1038, October.
    • Handle: RePEc:inm:ormnsc:v:26:y:1980:i:10:p:1031-1038
    DOI: 10.1287/mnsc.26.10.1031
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    Cited by:

    1. Li, Meiyu & Gençay, Ramazan & Xue, Yi, 2016. "Is it Brownian or fractional Brownian motion?," Economics Letters, Elsevier, vol. 145(C), pages 52-55.
    2. Martínez Patiño, Manuel Andrés & Ariza Garzón, Miller Janny & Cadena Lozano, Javier Bernardo, 2021. "Relevancia del patrón de persistencia de Hurst en la gestión de portafolios de renta variable|| Relevance of Hurst's pattern in equity portfolio management," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 32(1), pages 66-82, December.

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