1. Gaussian Mixture and Kernel Density-Based Hybrid Model for Volatility Behavior Extraction From Public Financial Data
- Author
-
Rachid Saadane, Hasna Chaibi, and Smail Tigani
- Subjects
Information Systems and Management ,Volkswirtschaftstheorie ,Computer science ,Economics ,Gaussian ,Devisen ,050208 finance ,Sozialwissenschaften, Soziologie ,05 social sciences ,statistische Methode ,algorithmic trading ,Wirtschaft ,04 agricultural and veterinary sciences ,artificial intelligence ,Computer Science Applications ,symbols ,ddc:300 ,kernel density estimation ,Information Systems ,National Economy ,Mathematical optimization ,statistical method ,Kernel density estimation ,Probability density function ,symbols.namesake ,foreign exchange market ,0502 economics and business ,Expectation–maximization algorithm ,ddc:330 ,gaussian mixture model ,foreign exchange ,Cluster analysis ,Social sciences, sociology, anthropology ,Finanzmarkt ,künstliche Intelligenz ,040101 forestry ,Erhebungstechniken und Analysetechniken der Sozialwissenschaften ,Volatility clustering ,algorithm ,Mixture model ,lcsh:Z ,lcsh:Bibliography. Library science. Information resources ,Methods and Techniques of Data Collection and Data Analysis, Statistical Methods, Computer Methods ,Algorithmus ,financial market ,0401 agriculture, forestry, and fisheries ,Volatility (finance) - Abstract
This paper carried out a hybrid clustering model for foreign exchange market volatility clustering. The proposed model is built using a Gaussian Mixture Model and the inference is done using an Expectation Maximization algorithm. A mono-dimensional kernel density estimator is used in order to build a probability density based on all historical observations. That allows us to evaluate the behavior&rsquo, s probability of each symbol of interest. The computation result shows that the approach is able to pinpoint risky and safe hours to trade a given currency pair.
- Published
- 2019