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Gaussian Mixture and Kernel Density-Based Hybrid Model for Volatility Behavior Extraction From Public Financial Data

1
Euromed Research Center, Engineering Unit, Euro-Mediterranean University, Fes 51, Morocco
2
SIME Lab, ENSIAS, Mohammed V-Souissi University, Rabat 713, Morocco
3
Electrical Engineering Department, Hassania School of Public Labors, Casablanca 8108, Morocco
*
Author to whom correspondence should be addressed.
This paper is an extended version of our paper published in MedPRAI’2018.
Received: 7 December 2018 / Revised: 22 January 2019 / Accepted: 22 January 2019 / Published: 24 January 2019
(This article belongs to the Special Issue Data Analysis for Financial Markets)
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PDF [988 KB, uploaded 24 January 2019]
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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’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 Full-Text
Keywords: foreign exchange market; gaussian mixture model; kernel density estimation; algorithmic trading foreign exchange market; gaussian mixture model; kernel density estimation; algorithmic trading
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Tigani, S.; Chaibi, H.; Saadane, R. Gaussian Mixture and Kernel Density-Based Hybrid Model for Volatility Behavior Extraction From Public Financial Data. Data 2019, 4, 19.

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