MULTIFRACTALITY AND LONG-RANGE DEPENDENCE OF ASSET RETURNS: THE SCALING BEHAVIOR OF THE MARKOV-SWITCHING MULTIFRACTAL MODEL WITH LOGNORMAL VOLATILITY COMPONENTS

Ruipeng Liu, T. Di Matteo () and Thomas Lux ()
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T. Di Matteo: Department of Applied Mathematics, Research School of Physical Sciences and Engineering, The Australian National University, Canberra, ACT 0200, Australia
Thomas Lux: Department of Economics, University of Kiel, 24118 Kiel, Germany; Institute for the World Economy, Düsternbrooker Weg 120, 24105 Kiel, Germany

Advances in Complex Systems (ACS), 2008, vol. 11, issue 05, pages 669-684

Abstract: 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.

Keywords: Markov-switching multifractal; scaling; Hurst exponent (search for similar items in EconPapers)
Date: 2008

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Related works:
Working Paper: Multifractality and long-range dependence of asset returns: the scaling behaviour of the Markov-switching multifractal model with lognormal volatility components (2008)
Working Paper: Multifractality and Long-Range Dependence of Asset Returns: The Scaling Behaviour of the Markov-Switching Multifractal Model with Lognormal Volatility Components (2008)
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