Detecting chaos and predicting in Dow Jones Index

DOI10.1016/J.CHAOS.2018.03.034zbMath1394.91322OpenAlexW2795447369WikidataQ130043934 ScholiaQ130043934MaRDI QIDQ721762

Paulo Ricardo L. Alves, L. G. S. Duarte, L. A. C. P. da Mota

Publication date: 20 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.2018.03.034

zbMATH Keywords

chaostime series analysiseconophysics

Mathematics Subject Classification ID

Applications of statistical and quantum mechanics to economics (econophysics) (91B80) Financial applications of other theories (91G80) Time series analysis of dynamical systems (37M10)

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Uses Software

• ISLR
• LinMapTS
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Cites Work

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• Approximately normal tests for equal predictive accuracy in nested models
• Network models in economics and finance
• A Maple package for improved global mapping forecast
• Chaos, randomness and multi-fractality in bitcoin market
• Nonlinear dynamics of equity, currency and commodity markets in the aftermath of the global financial crisis
• Improvement in global forecast for chaotic time series
• A new method for improved global mapping forecast
• A new characterization of chaos from a time series
• Disturbances and complexity in volatility time series
• Alternative predictors in chaotic time series
• The maple symbolic computation system
• An Introduction to Statistical Learning
• Introduction to Econophysics
• Robust Statistics

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