A case study of the residual-based cointegration procedure

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.

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On the Connection between the GEP Performances and the Time Series Properties

Mathematics ◽

10.3390/math9161853 ◽

2021 ◽

Vol 9 (16) ◽

pp. 1853

Author(s):

Alina Bărbulescu ◽

Cristian Ștefan Dumitriu

Keyword(s):

Time Series ◽

Goodness Of Fit ◽

Functional Form ◽

Financial Time Series ◽

Gene Expression Programming ◽

Parametric Models ◽

Statistical Characteristics ◽

Data Series ◽

Financial Time ◽

Data Generating Process

Artificial intelligence (AI) methods are interesting alternatives to classical approaches for modeling financial time series since they relax the assumptions imposed on the data generating process by the parametric models and do not impose any constraint on the model’s functional form. Even if many studies employed these techniques for modeling financial time series, the connection of the models’ performances with the statistical characteristics of the data series has not yet been investigated. Therefore, this research aims to study the performances of Gene Expression Programming (GEP) for modeling monthly and weekly financial series that present trend and/or seasonality and after the removal of each component. It is shown that series normality and homoskedasticity do not influence the models’ quality. The trend removal increases the models’ performance, whereas the seasonality elimination results in diminishing the goodness of fit. Comparisons with ARIMA models built are also provided.

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Case Study on Modeling the Silver and Nasdaq Financial Time Series with Simulated Annealing

Advances in Intelligent Systems and Computing - Trends and Advances in Information Systems and Technologies ◽

10.1007/978-3-319-77712-2_71 ◽

2018 ◽

pp. 755-763 ◽

Cited By ~ 3

Author(s):

Filippo Neri

Keyword(s):

Time Series ◽

Simulated Annealing ◽

Financial Time Series ◽

Financial Time

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The Application Research on Fuzzy Theory and Genetic Algorithm

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.241-244.1768 ◽

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.

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Technical Analysis on Financial Time Series Data Based on Map-Reduce Programming Model: A Case Study

2018 International Congress on Big Data, Deep Learning and Fighting Cyber Terrorism (IBIGDELFT) ◽

10.1109/ibigdelft.2018.8625357 ◽

2018 ◽

Cited By ~ 1

Author(s):

Yasin Uygun ◽

Mehmet Onur Erboy ◽

Mehmet S. Aktas ◽

Oya Kalipsiz ◽

Ishak Aykurt

Keyword(s):

Time Series ◽

Time Series Data ◽

Programming Model ◽

Financial Time Series ◽

Technical Analysis ◽

Map Reduce ◽

Series Data ◽

Financial Time

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The structure and dynamics of granular complex networks deriving from financial time series

International Journal of Modern Physics C ◽

10.1142/s0129183120500874 ◽

2020 ◽

Vol 31 (06) ◽

pp. 2050087

Author(s):

Li Tingting ◽

Luo Chao ◽

Shao Rui

Keyword(s):

Time Series ◽

Complex Networks ◽

Financial Time Series ◽

Markov Chain Model ◽

Real Life ◽

Chain Model ◽

Sequential Data ◽

Information Granule ◽

Financial Time ◽

Structure And Dynamics

High noise and strong volatility are the typical characteristics of financial time series. Combined with pseudo-randomness, nonsteady and self-similarity exhibiting in different time scales, it is a challenging issue for the pattern analysis of financial time series. Different from the existing works, in this paper, financial time series are converted into granular complex networks, based on which the structure and dynamics of network models are revealed. By using variable-length division, an extended polar fuzzy information granule (FIGs) method is used to construct granular complex networks from financial time series. Considering the temporal characteristics of sequential data, static networks and temporal networks are studied, respectively. As to the static network model, some features of topological structures of granular complex networks, such as distribution, clustering and betweenness centrality are discussed. Besides, by using the Markov chain model, the transfer processes among different granules are investigated, where the fluctuation pattern of data in the coming step can be evaluated from the transfer probability of two consecutive granules. Shanghai composite index and foreign exchange data as two examples in real life are applied to carry out the related discussion.

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Long-run wavelet-based correlation for financial time series

European Journal of Operational Research ◽

10.1016/j.ejor.2018.05.028 ◽

2018 ◽

Vol 271 (2) ◽

pp. 676-696 ◽

Cited By ~ 3

Author(s):

Thomas Conlon ◽

John Cotter ◽

Ramazan Gençay

Keyword(s):

Time Series ◽

Financial Time Series ◽

Financial Time ◽

Long Run

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A Novel Ensemble Neuro-Fuzzy Model for Financial Time Series Forecasting

Data ◽

10.3390/data4030126 ◽

2019 ◽

Vol 4 (3) ◽

pp. 126 ◽

Cited By ~ 4

Author(s):

Vlasenko ◽

Vynokurova ◽

Bodyanskiy ◽

Peleshko

Keyword(s):

Decision Making ◽

Time Series ◽

Financial Time Series ◽

Selection Process ◽

Real Life ◽

Fuzzy Model ◽

Financial Data ◽

Financial Time ◽

Fuzzy Models ◽

Neuro Fuzzy

Neuro-fuzzy models have a proven record of successful application in finance. Forecasting future values is a crucial element of successful decision making in trading. In this paper, a novel ensemble neuro-fuzzy model is proposed to overcome limitations and improve the previously successfully applied a five-layer multidimensional Gaussian neuro-fuzzy model and its learning. The proposed solution allows skipping the error-prone hyperparameters selection process and shows better accuracy results in real life financial data.

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Heterogeneous Gain Learning and Long Swings in Asset Prices

Rethinking Expectations ◽

10.23943/princeton/9780691155234.003.0006 ◽

2013 ◽

Cited By ~ 2

Author(s):

Blake LeBaron

Keyword(s):

Time Series ◽

Asset Prices ◽

Financial Time Series ◽

Asset Price ◽

Financial Time ◽

Basic Model ◽

Long Run ◽

Fundamental Values ◽

Over Time ◽

Regular Patterns

This chapter focuses on heterogeneous gain learning and long swings in asset prices. Many asset prices deviate from their fundamental values, yielding potential long-run predictability. Asset price swings can be both short or long in duration, and their time series shows few regular patterns when one analyzes their long-range behavior. This chapter considers an underparameterized learning model with heterogeneous gain parameters and traders using differing perspectives on history. It first provides an overview of the basic model and some benchmark simulation runs before discussing the output of the model compared to actual financial time series. It then describes a range of internal mechanisms of the agents and forecasts in use and how wealth moves across them over time. It shows that learning algorithms appear to be behaving in a predictable fashion, and that interesting dynamics come from how agent wealth selects rules over time. The chapter concludes by addressing some questions for researchers working on learning in financial markets.

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