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Estimating serial correlation and self-similarity in financial time series—A diversification approach with applications to high frequency data

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  • Gerlich, Nikolas
  • Rostek, Stefan

Abstract

We derive a heuristic method to estimate the degree of self-similarity and serial correlation in financial time series. Especially, we propagate the use of a tailor-made selection of different estimation techniques that are used in various fields of time series analysis but until now have not consequently found their way into the finance literature. Following the idea of portfolio diversification, we show that considerable improvements with respect to robustness and unbiasedness can be achieved by using a basket of estimation methods.

Suggested Citation

  • Gerlich, Nikolas & Rostek, Stefan, 2015. "Estimating serial correlation and self-similarity in financial time series—A diversification approach with applications to high frequency data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 434(C), pages 84-98.
    • Handle: RePEc:eee:phsmap:v:434:y:2015:i:c:p:84-98
    DOI: 10.1016/j.physa.2015.03.085
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    Cited by:

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    2. Zhang, Xili & Xiao, Weilin, 2017. "Arbitrage with fractional Gaussian processes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 620-628.

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