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Gaussian Mixture and Kernel Density-Based Hybrid Model for Volatility Behavior Extraction From Public Financial Data
[journal article]
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 ... view more
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’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.... view less
Keywords
financial market; foreign exchange; statistical method; algorithm; artificial intelligence
Classification
National Economy
Methods and Techniques of Data Collection and Data Analysis, Statistical Methods, Computer Methods
Free Keywords
foreign exchange market; gaussian mixture model; kernel density estimation; algorithmic trading
Document language
English
Publication Year
2019
Journal
Data, 4 (2019) 1
Issue topic
Data Analysis for Financial Markets
DOI
https://doi.org/10.3390/data4010019
ISSN
2306-5729
Status
Published Version; peer reviewed