Detail publikace

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

Autor: prof. PhDr. Ladislav Krištoufek Ph.D.,
Typ: Články v impaktovaných časopisech
Rok: 2012
Číslo: 0
ISSN / ISBN:
Publikováno v: Physica A: Statistical Mechanics and its Applications 391, pp. 4252-4260 arXiv PDF
Místo vydání:
Klíčová slova: Rescaled range analysis, Modified rescaled range analysis Hurst exponent, Long-term memory, Short-term memory
JEL kódy:
Citace:
Granty: GAUK 5183/2010 (118310) Fraktalita a multifraktalita finančních trhů: metody a aplikace Výzkumný záměr IES (2005-2011) Integrace české ekonomiky do Evropské unie a její rozvoj
Abstrakt: 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.
Prosinec 2020
poútstčtsone
 123456
78910111213
14151617181920
21222324252627
28293031   

Partneři

Deloitte

Sponzoři

CRIF
McKinsey
Patria Finance