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Multifractality and long-range dependence of asset returns: the scaling behavior of the Markov-switching multifractal model with lognormal volatility components

journal contribution
posted on 01.10.2008, 00:00 authored by Ruipeng LiuRuipeng Liu, T Di Matteo, T Lux
In this paper, we consider daily financial data from various sources (stock market indices, foreign exchange rates and bonds) and analyze their multiscaling properties by estimating the parameters of a Markov-switching multifractal (MSM) model with Lognormal volatility components. In order to see how well estimated models capture the temporal dependency of the empirical data, we estimate and compare (generalized) Hurst exponents for both empirical data and simulated MSM models. In general, the Lognormal MSM models generate "apparent" long memory in good agreement with empirical scaling provided that one uses sufficiently many volatility components. In comparison with a Binomial MSM specification [11], results are almost identical. This suggests that a parsimonious discrete specification is flexible enough and the gain from adopting the continuous Lognormal distribution is very limited.

History

Journal

Advances in complex systems

Volume

11

Issue

5

Pagination

669 - 684

Publisher

World Scientific Publishing

Location

Singapore

ISSN

0219-5259

eISSN

1793-6802

Language

eng

Publication classification

C1 Refereed article in a scholarly journal

Copyright notice

2008, World Scientific Publishing Company