scholarly journals Adaptive Portfolio Analysis based on the Trend Decomposition of a Financial Time Series: Case Study of the Moscow Exchange

2021 ◽  
Vol 9 (5) ◽  
pp. 1159-1168
Author(s):  
Dmitry A. Endovitsky ◽  
Larisa S. Korobeinikova ◽  
Viacheslav V. Korotkikh
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Portfolio Analysis ◽  
Financial Time

scholarly journals A case study of the residual-based cointegration procedure

2003 ◽  
Vol 7 (1) ◽  
pp. 29-48
Author(s):  
Riccardo Biondini ◽  
Yan-Xia Lin ◽  
Michael Mccrae
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Real Life ◽  
Arima Models ◽  
Financial Time ◽  
Long Run ◽  
Data Generating Process ◽  
Finance Theory ◽  
The Relationship

The study of long-run equilibrium processes is a significant component of economic and finance theory. The Johansen technique for identifying the existence of such long-run stationary equilibrium conditions among financial time series allows the identification of all potential linearly independent cointegrating vectors within a given system of eligible financial time series. The practical application of the technique may be restricted, however, by the pre-condition that the underlying data generating process fits a finite-order vector autoregression (VAR) model with white noise. This paper studies an alternative method for determining cointegrating relationships without such a pre-condition. The method is simple to implement through commonly available statistical packages. This ‘residual-based cointegration’ (RBC) technique uses the relationship between cointegration and univariate Box-Jenkins ARIMA models to identify cointegrating vectors through the rank of the covariance matrix of the residual processes which result from the fitting of univariate ARIMA models. The RBC approach for identifying multivariate cointegrating vectors is explained and then demonstrated through simulated examples. The RBC and Johansen techniques are then both implemented using several real-life financial time series.

The Application Research on Fuzzy Theory and Genetic Algorithm

2012 ◽  
Vol 241-244 ◽  
pp. 1768-1771
Author(s):  
Xiao Qin Wu
Keyword(s):  
Time Series ◽  
Genetic Algorithms ◽  
Bp Neural Network ◽  
Financial Time Series ◽  
Fuzzy Theory ◽  
Application Research ◽  
Computing Technology ◽  
Financial Time ◽  
Better Than

Fuzzy theory is one of the newly adduced self-adaptive strategies,which is applied to dynamically adjust the parameters of genetic algorithms for the purpose of enhancing the performance.In this paper, the financial time series analysis and forecasting as the main case study to the theory of soft computing technology framework that focuses on the fuzzy logic genetic algorithms(FGA) as a method of integration. the financial time series forecasting model based on fuzzy theory and genetic algorithms was built. the ShangZheng index cards as an example. The experimental results show that FGA perform s much better than BP neural network，not only in the precision.but also in the searching speed.The hybrid algorithm has a strong feasibility and superiority.

scholarly journals Using Lyapunov’s method for analysing of chaotic behaviour on financial time series data: a case study on Tehran stock exchange

2020 ◽  
Vol 2 (3) ◽  
pp. 297-308
Author(s):  
Mohammad Reza Abbaszadeh ◽  
◽  
Mehdi Jabbari Nooghabi ◽  
Mohammad Mahdi Rounaghi ◽  
◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Stock Exchange ◽  
Financial Time Series ◽  
Chaotic Behaviour ◽  
Series Data ◽  
Financial Time ◽  
Lyapunov's Method ◽  
Lyapunov’S Method

Efficient filtering of financial time series and extreme value theory

2005 ◽  
Vol 7 (2) ◽  
pp. 63-84 ◽  
Author(s):  
Kaj Nyström ◽  
Jimmy Skoglund
Keyword(s):  
Time Series ◽  
Extreme Value Theory ◽  
Financial Time Series ◽  
Value Theory ◽  
Extreme Value ◽  
Financial Time
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