scholarly journals Chaotic Dynamics Analysis Based on Financial Time Series

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Zheng Gu ◽  
Yuhua Xu
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Dynamic Properties ◽  
Taylor Series Expansion ◽  
Scientific Methods ◽  
Financial Time ◽  
Bifurcation Phenomena ◽  
Expansion Form ◽  
Financial Research ◽  
The Empirical Analysis

It is a common phenomenon in the field of financial research to study the dynamic of financial market and explore the complexity of financial system by using various complex scientific methods. In this paper, the chaotic dynamic properties of financial time series are analyzed. Firstly, the nonlinear characteristics of the data are discussed through the empirical analysis of agriculture index data; the daily agriculture index returns can be decomposed into the different scales based on wavelet analysis. Secondly, the dynamic system of some nonlinear characteristic data is established according to the Taylor series expansion form, and the corresponding dynamic characteristics are analyzed. Finally, the bifurcation diagram of the system shows complicated bifurcation phenomena, which provides a perspective for the analysis of chaotic phenomena of economic data.

Fluctuation complexity of agent-based financial time series model by stochastic Potts system

2015 ◽  
Vol 26 (11) ◽  
pp. 1550123 ◽  
Author(s):  
Weijia Hong ◽  
Jun Wang
Keyword(s):  
Time Series ◽  
Dynamic System ◽  
Financial Time Series ◽  
Composite Index ◽  
Time Series Model ◽  
Agent Based ◽  
Financial Time ◽  
System A ◽  
Challenging Tasks ◽  
Financial Research

Financial market is a complex evolved dynamic system with high volatilities and noises, and the modeling and analyzing of financial time series are regarded as the rather challenging tasks in financial research. In this work, by applying the Potts dynamic system, a random agent-based financial time series model is developed in an attempt to uncover the empirical laws in finance, where the Potts model is introduced to imitate the trading interactions among the investing agents. Based on the computer simulation in conjunction with the statistical analysis and the nonlinear analysis, we present numerical research to investigate the fluctuation behaviors of the proposed time series model. Furthermore, in order to get a robust conclusion, we consider the daily returns of Shanghai Composite Index and Shenzhen Component Index, and the comparison analysis of return behaviors between the simulation data and the actual data is exhibited.

scholarly journals Detecting Predictable Segments of Chaotic Financial Time Series via Neural Network

Electronics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 823
Author(s):  
Tianle Zhou ◽  
Chaoyi Chu ◽  
Chaobin Xu ◽  
Weihao Liu ◽  
Hao Yu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Phase Space ◽  
Financial Market ◽  
Financial Time Series ◽  
Influential Factors ◽  
Series Data ◽  
Phase Point ◽  
Price Fluctuations ◽  
Financial Time

In this study, a new idea is proposed to analyze the financial market and detect price fluctuations, by integrating the technology of PSR (phase space reconstruction) and SOM (self organizing maps) neural network algorithms. The prediction of price and index in the financial market has always been a challenging and significant subject in time-series studies, and the prediction accuracy or the sensitivity of timely warning price fluctuations plays an important role in improving returns and avoiding risks for investors. However, it is the high volatility and chaotic dynamics of financial time series that constitute the most significantly influential factors affecting the prediction effect. As a solution, the time series is first projected into a phase space by PSR, and the phase tracks are then sliced into several parts. SOM neural network is used to cluster the phase track parts and extract the linear components in each embedded dimension. After that, LSTM (long short-term memory) is used to test the results of clustering. When there are multiple linear components in the m-dimension phase point, the superposition of these linear components still remains the linear property, and they exhibit order and periodicity in phase space, thereby providing a possibility for time series prediction. In this study, the Dow Jones index, Nikkei index, China growth enterprise market index and Chinese gold price are tested to determine the validity of the model. To summarize, the model has proven itself able to mark the unpredictable time series area and evaluate the unpredictable risk by using 1-dimension time series data.

scholarly journals Selecting between causal and noncausal models with quantile autoregressions

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Alain Hecq ◽  
Li Sun
Keyword(s):  
Time Series ◽  
Maximum Likelihood ◽  
Latin American ◽  
Financial Time Series ◽  
Selection Criterion ◽  
Likelihood Methods ◽  
Approximate Maximum Likelihood ◽  
Financial Time ◽  
Maximum Likelihood Methods ◽  
Latin American Countries

AbstractWe propose a model selection criterion to detect purely causal from purely noncausal models in the framework of quantile autoregressions (QAR). We also present asymptotics for the i.i.d. case with regularly varying distributed innovations in QAR. This new modelling perspective is appealing for investigating the presence of bubbles in economic and financial time series, and is an alternative to approximate maximum likelihood methods. We illustrate our analysis using hyperinflation episodes of Latin American countries.

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