{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T14:03:30Z","timestamp":1760709810867,"version":"build-2065373602"},"reference-count":22,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2019,1,24]],"date-time":"2019-01-24T00:00:00Z","timestamp":1548288000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Data"],"abstract":"<jats:p>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\u2019s 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.<\/jats:p>","DOI":"10.3390\/data4010019","type":"journal-article","created":{"date-parts":[[2019,1,24]],"date-time":"2019-01-24T11:12:48Z","timestamp":1548328368000},"page":"19","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Gaussian Mixture and Kernel Density-Based Hybrid Model for Volatility Behavior Extraction From Public Financial Data"],"prefix":"10.3390","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9074-4318","authenticated-orcid":false,"given":"Smail","family":"Tigani","sequence":"first","affiliation":[{"name":"Euromed Research Center, Engineering Unit, Euro-Mediterranean University, Fes 51, Morocco"}]},{"given":"Hasna","family":"Chaibi","sequence":"additional","affiliation":[{"name":"SIME Lab, ENSIAS, Mohammed V-Souissi University, Rabat 713, Morocco"}]},{"given":"Rachid","family":"Saadane","sequence":"additional","affiliation":[{"name":"Electrical Engineering Department, Hassania School of Public Labors, Casablanca 8108, Morocco"}]}],"member":"1968","published-online":{"date-parts":[[2019,1,24]]},"reference":[{"key":"ref_1","first-page":"45","article-title":"Predicting financial time series data using artificial immune system inspired neural networks","volume":"5","author":"Lamb","year":"2015","journal-title":"Int. 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Mark.","DOI":"10.1016\/j.finmar.2018.09.003"},{"key":"ref_5","first-page":"352","article-title":"The impact of interest rate volatility on financial market inclusion: Evidence from emerging markets","volume":"42","author":"Hajilee","year":"2018","journal-title":"J. Financ. Mark."},{"key":"ref_6","first-page":"581","article-title":"An examination of the REIT return\u2013implied volatility relation: A frequency domain approach","volume":"41","author":"Anoruo","year":"2016","journal-title":"J. Financ. Mark."},{"key":"ref_7","first-page":"339","article-title":"Differences of opinion and stock market volatility: Evidence from a nonparametric causality-in-quantiles approach","volume":"42","author":"Mehmet","year":"2017","journal-title":"J. Financ. Mark."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.finmar.2017.07.002","article-title":"Market volatility and stock returns: The role of liquidity providers","volume":"37","author":"Chung","year":"2017","journal-title":"J. Financ. Mark."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Tigani, S., and Saadane, R. (2018, January 27\u201328). Multivariate Statistical Model based Currency Market Proftability Binary Classifer. Proceedings of the 2nd Mediterranean Conference on Pattern Recognition and Artifcial Intelligence, Rabat, Morocco.","DOI":"10.1145\/3177148.3180093"},{"key":"ref_10","unstructured":"MIT Laboratory for Information and Decision Systems (2013). Relationship between Trading Volume and Security Prices and Returns, Massachusetts Institute of Technology. Tech. Rep. P-2638."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Kanungsukkasem, N., and Leelanupab, T. (2015, January 29\u201330). Finding potential influences of a specific financial market in Twitter. Proceedings of the 7th International Conference on Information Technology and Electrical Engineering (ICITEE), Chiang Mai, Thailand.","DOI":"10.1109\/ICITEED.2015.7408982"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.econmod.2016.08.020","article-title":"Inflation targeting and financial stability in emerging markets","volume":"60","author":"Fouejieu","year":"2017","journal-title":"Econ. Model."},{"key":"ref_13","first-page":"569","article-title":"Market share, firm innovation, and idiosyncratic volatility","volume":"41","author":"Adjei","year":"2016","journal-title":"J. Financ. Mark."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Branquinho, A.A.B., Lopes, C.R., and Baffa, A.C.E. (2016, January 6\u20138). Probabilistic Planning for Multiple Stocks of Financial Markets. Proceedings of the IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI), San Jose, CA, USA.","DOI":"10.1109\/ICTAI.2016.0083"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Bui Quang, P., Klein, T., Nguyen, N.H., and Walther, T. (2018). Value-at-Risk for South-East Asian Stock Markets: Stochastic Volatility vs. GARCH. J. Risk Financ. Manag., 11.","DOI":"10.3390\/jrfm11020018"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Tegn\u00e9r, M., and Poulsen, R. (2018). Volatility Is Log-Normal But Not for the Reason You Think. Risks, 6.","DOI":"10.3390\/risks6020046"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Alam, M.D., Farnham, C., and Emura, K. (2018). Best-Fit Probability Models for Maximum Monthly Rainfall in Bangladesh Using Gaussian Mixture Distributions. Geosciences, 8.","DOI":"10.3390\/geosciences8040138"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.ijepes.2018.01.008","article-title":"Markov Chain Monte Carlo simulation of electric vehicle use for network integration studies","volume":"99","author":"Wang","year":"2018","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Kaiser, W., Popp, J., Rinderle, M., Albes, T., and Gagliardi, A. (2018). Generalized Kinetic Monte Carlo Framework for Organic Electronics. Algorithms, 11.","DOI":"10.3390\/a11040037"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Marnissi, Y., Chouzenoux, E., Benazza-Benyahia, A., and Pesquet, J.C. (2018). An Auxiliary Variable Method for Markov Chain Monte Carlo Algorithms in High Dimension. Entropy, 20.","DOI":"10.3390\/e20020110"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/j.sigpro.2018.04.013","article-title":"Gaussian mixture learning via adaptive hierarchical clustering","volume":"150","author":"Li","year":"2018","journal-title":"Signal Process."},{"key":"ref_22","first-page":"1","article-title":"mixtools: An R Package for Analyzing Mixture Models","volume":"32","author":"Benaglia","year":"2010","journal-title":"J. Stat. 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