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

Smail Tigani, Hasna Chaibi and Rachid Saadane
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Smail Tigani: Euromed Research Center, Engineering Unit, Euro-Mediterranean University, Fes 51, Morocco
Hasna Chaibi: SIME Lab, ENSIAS, Mohammed V-Souissi University, Rabat 713, Morocco
Rachid Saadane: Electrical Engineering Department, Hassania School of Public Labors, Casablanca 8108, Morocco

Data, 2019, vol. 4, issue 1, 1-11

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.

Keywords: foreign exchange market; gaussian mixture model; kernel density estimation; algorithmic trading (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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