Publication detail

Avdulaj, K. & Kristoufek, L.: On tail dependence and multifractality

Author(s): Mgr. Krenar Avdulaj Ph.D.,
prof. PhDr. Ladislav Krištoufek Ph.D.,
Type: Articles in journals with impact factor
Year: 2020
Number: 0
ISSN / ISBN:
Published in: Mathematics 8(10):1767 PDF
Publishing place:
Keywords: multifractality, tail dependence, serial correlation, copulas
JEL codes:
Suggested Citation:
Grants: PRIMUS/19/HUM/17 2019-2021 Behavioral finance and macroeconomics: New insights for the mainstream
Abstract: We study whether, and if yes then how, a varying auto-correlation structure in different parts of distributions is reflected in the multifractal properties of dynamic process. Utilizing the quantile autoregressive process with Gaussian copula using three popular estimators of the generalized Hurst exponent, our Monte Carlo simulation study shows that such dynamics translates into multifractal dynamics of the generated series. The tail-dependence of the auto-correlations forms strong enough non-linear dependencies to be reflected in the estimated multifractal spectra and separated from the case of the standard auto-regressive process. With a quick empirical example from financial markets, we argue that the interaction is more important for the asymmetric tail dependence. In addition, we discuss and explain the often reported paradox of higher multifractality of shuffled series compared to the original financial series. In short, the quantile-dependent auto-correlation structures qualify as sources of multifractality and they are worth further theoretical examination.

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