Chaos, randomness and multi-fractality in bitcoin market

DOI10.1016/J.CHAOS.2017.11.005zbMath1394.91324OpenAlexW2769091037MaRDI QIDQ722915

Salim Lahmiri, Stelios D. Bekiros

Publication date: 30 July 2018

Published in: Chaos, Solitons and Fractals (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.chaos.2017.11.005

zbMATH Keywords

chaosLyapunov exponentShannon entropybitcoinmultifractal detrended fluctuation analysis

Mathematics Subject Classification ID

Economic time series analysis (91B84) Financial applications of other theories (91G80) Time series analysis of dynamical systems (37M10)

Related Items (14)

Dynamic characteristic of Bitcoin cryptocurrency in the reconstruction scheme ⋮ Visibility graph analysis of bitcoin price series ⋮ Multifractal behavior relationship between crypto markets and Wikipedia-Reddit online platforms ⋮ High- and low-level chaos in the time and frequency market returns of leading cryptocurrencies and emerging assets ⋮ Time-dependent complexity measurement of causality in international equity markets: a spatial approach ⋮ Cryptocurrency forecasting with deep learning chaotic neural networks ⋮ On predefined-time synchronisation of chaotic systems ⋮ A novel integrated measure for energy market efficiency ⋮ Forecasting volatility in bitcoin market ⋮ Detecting chaos and predicting in Dow Jones Index ⋮ Solving the chaos model-data paradox in the cryptocurrency market ⋮ Applying least squares support vector machines to mean-variance portfolio analysis ⋮ Complexity traits and synchrony of cryptocurrencies price dynamics ⋮ The digital asset value and currency supervision under deep learning and blockchain technology

Cites Work

• A Mathematical Theory of Communication
• Multifractal detrended fluctuation analysis of nonstationary time series
• Testing for nonlinearity in time series: the method of surrogate data
• No evidence of chaos but some evidence of dependence in the US stock market.
• Nonlinear dynamics of equity, currency and commodity markets in the aftermath of the global financial crisis
• Asymmetric multi-fractality in the U.S. stock indices using index-based model of A-MFDFA
• Disturbances and complexity in volatility time series
• High level chaos in the exchange and index markets

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