On Tail Dependence and Multifractality

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 a dynamic process. Utilizing the quantile autoregressive process with Gaussian copula using three popular estimators of the generalized Hurst...

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Main Authors: Krenar Avdulaj, Ladislav Kristoufek
Format: Article
Language:English
Published: MDPI AG 2020-10-01
Series:Mathematics
Subjects:
Online Access:https://www.mdpi.com/2227-7390/8/10/1767
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author Krenar Avdulaj
Ladislav Kristoufek
author_facet Krenar Avdulaj
Ladislav Kristoufek
author_sort Krenar Avdulaj
collection DOAJ
description 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 a 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 translate 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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spelling doaj.art-2cb661ceb7d54c60b300a190e80dc7162023-11-20T16:54:59ZengMDPI AGMathematics2227-73902020-10-01810176710.3390/math8101767On Tail Dependence and MultifractalityKrenar Avdulaj0Ladislav Kristoufek1Institute of Information Theory and Automation, Czech Academy of Sciences, Pod Vodarenskou Vezi 4, 182 08 Prague, Czech RepublicInstitute of Information Theory and Automation, Czech Academy of Sciences, Pod Vodarenskou Vezi 4, 182 08 Prague, Czech RepublicWe study whether, and if yes then how, a varying auto-correlation structure in different parts of distributions is reflected in the multifractal properties of a 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 translate 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.https://www.mdpi.com/2227-7390/8/10/1767multifractalitytail dependenceserial correlationcopulas
spellingShingle Krenar Avdulaj
Ladislav Kristoufek
On Tail Dependence and Multifractality
Mathematics
multifractality
tail dependence
serial correlation
copulas
title On Tail Dependence and Multifractality
title_full On Tail Dependence and Multifractality
title_fullStr On Tail Dependence and Multifractality
title_full_unstemmed On Tail Dependence and Multifractality
title_short On Tail Dependence and Multifractality
title_sort on tail dependence and multifractality
topic multifractality
tail dependence
serial correlation
copulas
url https://www.mdpi.com/2227-7390/8/10/1767
work_keys_str_mv AT krenaravdulaj ontaildependenceandmultifractality
AT ladislavkristoufek ontaildependenceandmultifractality