An Innovative Financial Time Series Model: The Geometric Process Model

2014 ◽  
pp. 81-99
Author(s):  
Jennifer S. K. Chan ◽  
Connie P. Y. Lam ◽  
S. T. Boris Choy
Keyword(s):  
Time Series ◽  
Process Model ◽  
Financial Time Series ◽  
Time Series Model ◽  
Geometric Process ◽  
Financial Time

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 Nonlinear Fluctuation Behavior of Financial Time Series Model by Statistical Physics System

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Wuyang Cheng ◽  
Jun Wang
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Statistical Physics ◽  
Stock Exchange ◽  
Financial Time Series ◽  
Contact Process ◽  
Composite Index ◽  
Return Time ◽  
Time Series Model ◽  
Financial Time

We develop a random financial time series model of stock market by one of statistical physics systems, the stochastic contact interacting system. Contact process is a continuous time Markov process; one interpretation of this model is as a model for the spread of an infection, where the epidemic spreading mimics the interplay of local infections and recovery of individuals. From this financial model, we study the statistical behaviors of return time series, and the corresponding behaviors of returns for Shanghai Stock Exchange Composite Index (SSECI) and Hang Seng Index (HSI) are also comparatively studied. Further, we investigate the Zipf distribution and multifractal phenomenon of returns and price changes. Zipf analysis and MF-DFA analysis are applied to investigate the natures of fluctuations for the stock market.

Robert Engle and Clive Granger: New Spheres of Economic Research (Nobel Prize in Economics 2003)

2004 ◽  
pp. 37-48
Author(s):  
G. Kantorovich ◽  
M. Touruntseva
Keyword(s):  
Time Series ◽  
Financial Market ◽  
Nobel Prize ◽  
Financial Time Series ◽  
Market Analysis ◽  
Time Series Model ◽  
Economic Research ◽  
Estimation Methods ◽  
Financial Time ◽  
Financial Market Analysis

This paper is dedicated to the achievements of Robert Engle and Clive Granger which allowed to overcome a serious crisis in macroeconomics and financial market analysis. The main concepts of cointegration theory and different estimation methods of cointegration equations are considered in the first part of the paper. The areas of application of cointegration theory and possible extensions are briefly described as well. The financial time series model with conditional heteroskedastisity is analyzed in the second part of the paper. The main prerequisites of the method suggested by R. Engle are formulated and its extensions and areas of application are defined.

scholarly journals A Trend-Switching Financial Time Series Model with Level-Duration Dependence

2012 ◽  
Vol 2012 ◽  
pp. 1-20
Author(s):  
Qingsheng Wang ◽  
Aifan Ling ◽  
Tao Huang ◽  
Yong Jiang ◽  
Min Chen
Keyword(s):  
Time Series ◽  
Point Process ◽  
Turning Point ◽  
Financial Time Series ◽  
Difficult Problem ◽  
Time Series Model ◽  
Financial Time ◽  
Proposed Model ◽  
Long Time ◽  
Local Trend

The financial time series model that can capture the nonlinearity and asymmetry of stochastic process has been paid close attention for a long time. However, it is still open to completely overcome the difficult problem that motivates our researches in this paper. An asymmetric and nonlinear model with the change of local trend depending on local high-low turning point process is first proposed in this paper. As the point process can be decomposed into the two different processes, a high-low level process and an up-down duration process, we then establish the so-called trend-switching model which depends on both level and duration (Trend-LD). The proposed model can predict efficiently the direction and magnitude of the local trend of a time series by incorporating the local high-low turning point information. The numerical results on six indices in world stock markets show that the proposed Trend-LD model is suitable for fitting the market data and able to outperform the traditional random walk model.

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