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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 2008-10-01, 00:00 authored by Ruipeng LiuRuipeng Liu, T Di Matteo, T LuxIn 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 systemsVolume
11Issue
5Pagination
669 - 684Publisher
World Scientific PublishingLocation
SingaporeISSN
0219-5259eISSN
1793-6802Language
engPublication classification
C1 Refereed article in a scholarly journalCopyright notice
2008, World Scientific Publishing CompanyUsage metrics
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