True and apparent scaling: The proximity of the Markov-switching multifractal model to long-range dependence

Ruipeng Liu, T. Di Matteo and Thomas Lux

Physica A: Statistical Mechanics and its Applications, 2007, vol. 383, issue 1, 35-42

Abstract: In this paper, we consider daily financial data of a collection of different stock market indices, exchange rates, and interest rates, and we analyze their multi-scaling properties by estimating a simple specification of the Markov-switching multifractal (MSM) model. In order to see how well the estimated model captures the temporal dependence of the data, we estimate and compare the scaling exponents H(q) (for q=1,2) for both empirical data and simulated data of the MSM model. In most cases the multifractal model appears to generate ‘apparent’ long memory in agreement with the empirical scaling laws.

Keywords: Scaling; Generalized Hurst exponent; Multifractal model; GMM estimation (search for similar items in EconPapers)
Date: 2007
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (41)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378437107004682
Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

Related works:
Working Paper: True and Apparent Scaling: The Proximity of the Markov-Switching Multifractal Model to Long-Range Dependence (2007)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:383:y:2007:i:1:p:35-42

DOI: 10.1016/j.physa.2007.04.085

Access Statistics for this article

Physica A: Statistical Mechanics and its Applications is currently edited by K. A. Dawson, J. O. Indekeu, H.E. Stanley and C. Tsallis

More articles in Physica A: Statistical Mechanics and its Applications from Elsevier
Bibliographic data for series maintained by Catherine Liu ().