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

Ladislav Krištoufek ()

Papers from arXiv.org

Abstract: In this paper, we present the results of Monte Carlo simulations for two popular techniques of long-range correlations 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 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 8 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.

Date: 2012-01
New Economics Papers: this item is included in nep-ecm and nep-ets
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Published in Physica A 391(17), pp. 4252-4260, 2012

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Journal Article: How are rescaled range analyses affected by different memory and distributional properties? A Monte Carlo study (2012)
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