RT Journal Article
T1 Solving the chaos model-data paradox in the cryptocurrency market
A1 Pietrych, Lukasz
A1 Sandubete Galán, Julio Emilio
A1 Escot Mangas, Lorenzo
AB In this paper we test for nonlinearity and chaos in some cryptocurrencies returns and volatility. Financial markets are characterized by the so-called chaos model-data paradox, that is, it is relatively easy to design theoretical dynamic financial models that behave chaotically, but it is hard to find robust evidence of this kind of chaotic behaviour in real dataset. In fact, this paradox has been taken as an evidence that support the Efficient Market Hypothesis (EMH). In this paper we apply new robust computational methods based on statistical procedures to reconstruct the underlying attractor and to estimate the Lyapunov exponents based on the Jacobian neural nets. We have tested nonlinearity and chaos in some digital cryptocurrencies (Bitcoin, Ethereum, Ripple and Litecoin). The results show strong evidence against EMH supporting the hypothesis that those time series come from an underlying unknown generating process that behave nonlinear and chaotically. This fact points out that a potential explication to the chaos model-data paradox lies in the methods traditionally used in the literature which are not robust and do not have the capability to find chaos in financial time-series data.
PB Elsevier
SN 1007-5704
YR 2021
FD 2021
LK https://hdl.handle.net/20.500.14352/99653
UL https://hdl.handle.net/20.500.14352/99653
LA eng
NO Pietrych, L.; Sandubete, J.E.; Escot, L. “Solving the chaos model-data paradox in the cryptocurrency market” Communications in Nonlinear Science and Numerical Simulation, vol 102 (nov 2021), num 105901, https://doi.org/10.1016/j.cnsns.2021.105901
NO Ministerio de Ciencia e Innovación (España) 
NO Warsaw University 
DS Docta Complutense
RD 9 abr 2025