NONLINEAR ANALYSIS AND PREDICTION OF BITCOIN RETURN’S VOLATILITY

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dc.contributor.authorYin, Tao
dc.contributor.authorWang, Yiming
dc.contributor.otherEkonomická fakultacs
dc.date.accessioned2022-06-07T07:48:08Z
dc.date.available2022-06-07T07:48:08Z
dc.description.abstractThis paper mainly studies the market nonlinearity and the prediction model based on the intrinsic generation mechanism (chaos) of Bitcoin’s daily return’s volatility from June 27, 2013 to November 7, 2019 with an econophysics perspective, so as to avoid the forecasting model misspecification. Firstly, this paper studies the multifractal and chaotic nonlinear characteristics of Bitcoin volatility by using multifractal detrended fluctuation analysis (MFDFA) and largest Lyapunov exponent (LLE) methods. Then, from the perspective of nonlinearity, the measured values of multifractal and chaos show that the volatility of Bitcoin has short-term predictability. The study of chaos and multifractal dynamics in nonlinear systems is very important in terms of their predictability. The chaos signals may have short-term predictability, while multifractals and self-similarity can increase the likelihood of accurately predicting future sequences of these signals. Finally, we constructed a number of chaotic artificial neural network models to forecast the Bitcoin return’s volatility avoiding the model misspecification. The results show that chaotic artificial neural network models have good prediction effect by comparing these models with the existing Artificial Neural Network (ANN) models. This is because the chaotic artificial neural network models can extract hidden patterns and accurately model time series from potential signals, while the benchmark ANN models are based on Gaussian kernel local approximation of non-stationary signals, so they cannot approach the global model with chaotic characteristics. At the same time, the multifractal parameters are further mined to obtain more market information to guide financial practice. These above findings matter for investors (especially for investors in quantitative trading) as well as effective supervision of financial institutions by government.en
dc.formattext
dc.identifier.doi10.15240/tul/001/2022-2-007
dc.identifier.eissn2336-5604
dc.identifier.issn1212-3609
dc.identifier.urihttps://dspace.tul.cz/handle/15240/164989
dc.language.isoen
dc.publisherTechnická Univerzita v Libercics
dc.publisherTechnical university of Liberec, Czech Republicen
dc.publisher.abbreviationTUL
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dc.relation.ispartofEkonomie a Managementcs
dc.relation.ispartofEconomics and Managementen
dc.relation.isrefereedtrue
dc.rightsCC BY-NC
dc.subjectnonlinearen
dc.subjectmultifractalen
dc.subjectchaosen
dc.subjectBitcoinen
dc.subjectpredictionen
dc.subject.classificationA10
dc.subject.classificationE44
dc.subject.classificationF37
dc.titleNONLINEAR ANALYSIS AND PREDICTION OF BITCOIN RETURN’S VOLATILITYen
dc.typeArticleen
local.accessopen
local.citation.epage117
local.citation.spage102
local.facultyFaculty of Economics
local.filenameEM_2_2022_7
local.fulltextyes
local.relation.abbreviationE+Mcs
local.relation.abbreviationE&Men
local.relation.issue2
local.relation.volume25
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