Publication detail

Kristoufek, L.: How are rescaled range analyses affected by different memory and distributional properties? A Monte Carlo study

Author(s): prof. PhDr. Ladislav Krištoufek Ph.D.,
Type: Articles in journals with impact factor
Year: 2012
Number: 0
Published in: Physica A: Statistical Mechanics and its Applications 391, pp. 4252-4260 arXiv PDF
Publishing place:
Keywords: Rescaled range analysis, Modified rescaled range analysis Hurst exponent, Long-term memory, Short-term memory
JEL codes:
Suggested Citation:
Grants: GAUK 5183/2010 (118310) Fractality and multi-fractality of financial markets: methods and applications IES Research Framework Institutional task (2005-2011) Integration of the Czech economy into European union and its development
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.




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