In the recent years, a new wave of interest spurred the involvement of complexity in finance which might provide
a guideline to understand the mechanism of financial markets, and researchers with different backgrounds have
made increasing contributions introducing new techniques and methodologies. In this paper, Markov-switching
multifractal models (MSM) are briefly reviewed and the multi-scaling properties of different financial data are
analyzed by computing the scaling exponents by means of the generalized Hurst exponent H(q). In particular
we have considered H(q) for price data, absolute returns and squared returns of different empirical financial time
series. We have computed H(q) for the simulated data based on the MSM models with Binomial and Lognormal
distributions of the volatility components. The results demonstrate the capacity of the multifractal (MF) models
to capture the stylized facts in finance, and the ability of the generalized Hurst exponents approach to detect
the scaling feature of financial time series.
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