How are rescaled range analyses affected by different memory and distributional properties? A Monte Carlo study

Ladislav Krištoufek ()

Physica A: Statistical Mechanics and its Applications, 2012, vol. 391, issue 17, 4252-4260

Abstract: In this paper, we present the results of Monte Carlo simulations for two popular techniques of long-range correlation detection — classical and modified rescaled range analyses. A focus is put on an effect of different distributional properties on an ability of the methods to efficiently distinguish between short-term memory and long-term memory. To do so, we analyze the behavior of the estimators for independent, short-range dependent, and long-range dependent processes with innovations from eight different distributions. We find that apart from a combination of very high levels of kurtosis and skewness, both estimators are quite robust to distributional properties. Importantly, we show that R/S is biased upwards (yet not strongly) for short-range dependent processes, while M-R/S is strongly biased downwards for long-range dependent processes regardless of the distribution of innovations.

Keywords: Rescaled range analysis; Modified rescaled range analysis; Hurst exponent; Long-term memory; Short-term memory (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (15)

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Working Paper: How are rescaled range analyses affected by different memory and distributional properties? A Monte Carlo study (2012)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:391:y:2012:i:17:p:4252-4260

DOI: 10.1016/j.physa.2012.04.005

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