About this Research Topic
The deterministic chaos was discovered as a result of nonlinear dependencies in deterministic systems. The classical examples are the logistic map, Lorenz system etc. The key point is the sensitivity of the behavior of the system on the initial values. The dependence is so strong that even small changes in the initial value result in strong changes in the behavior of the system. This also the case of a financial market. On one hand, the investors are usually highly qualified specialists, who are supposed to behave rationally. In other words, one can expect that such a system can be deterministic. On the other hand, there are such events as panic, herd behavior, crashes – many unpredictable events which make the system chaotic.
There are many attempts to understand and predict financial time series starting from various time series models (Auto-Regressive (AR), Moving Average (MA), Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models etc.), which supposed to mimic real time series, through log-periodicity, fractal, multifractal analysis up to numerous models of stock markets analyzing different strategies, agent interactions or even influence of information flow on markets. The literature is intensively growing.
In this Research Topic, we welcome authors to submit works related to investigations on deterministic chaos evidence in financial time series. The contributions can present novel results but review articles are also welcome.
Keywords: deterministic chaos, financial time series, time series analysis, time series modelling
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