Institute of Information Theory and Automation

Publication details

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

Journal Article

Krištoufek Ladislav


serial: Physica. A : Statistical Mechanics and its Applications vol.391, 17 (2012), p. 4252-4260

project(s): 118310, GA UK, 261 501, SVV, GA402/09/0965, GA ČR

keywords: Rescaled range analysis, Modified rescaled range analysis, Hurst exponent, Long-term memory, Short-term memory

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abstract (eng):

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.

RIV: AH

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Last modification: 21.12.2012
Institute of Information Theory and Automation