dc.contributorMolina-Muñoz, Jesús
dc.creatorCastañeda Torres, Ricard
dc.date.accessioned2021-12-16T21:57:05Z
dc.date.accessioned2022-09-22T15:10:26Z
dc.date.available2021-12-16T21:57:05Z
dc.date.available2022-09-22T15:10:26Z
dc.date.created2021-12-16T21:57:05Z
dc.identifierhttps://repository.urosario.edu.co/handle/10336/33363
dc.identifierhttps://doi.org/10.48713/10336_33363
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/3445709
dc.description.abstractRecently, the use of machine learning (ML) in scientific disciplines has experienced an unprecedented increase. This, as a consequence of the advances in computing that have allowed the obtaining of satisfactory results at moderate computational costs. Finance has not been an exception. Several works have been published in recent years using ML techniques. However, one of the topics with the least number of developed papers in this context is volatility. This panorama has changed. Data obtained from the Web of Science database show that for the years 2001 and 2010 there were 2 and 1 papers associated with this topic, respectively. Surprisingly, between 2019 and 2021, 37 manuscripts have been published related to this theme. The purpose of this work is to review the Works related to the applications of ML in volatility. For this, a classification of the main proposals on this topic is proposed, accompanied by a statistical and bibliometric analysis in which novel techniques such as K-means are used. The results are suggestive. Although most papers focus on volatility prediction through neural networks and support vector machine, there is a lack of works related to volatility transmission, calibration of volatility surfaces, project finance and corporate finance.
dc.languageeng
dc.publisherUniversidad del Rosario
dc.publisherAdministración de Negocios Internacionales
dc.publisherEscuela de Administración
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightsAbierto (Texto Completo)
dc.rightsEL AUTOR, manifiesta que la obra objeto de la presente autorización es original y la realizó sin violar o usurpar derechos de autor de terceros, por lo tanto la obra es de exclusiva autoría y tiene la titularidad sobre la misma. PARGRAFO: En caso de presentarse cualquier reclamación o acción por parte de un tercero en cuanto a los derechos de autor sobre la obra en cuestión, EL AUTOR, asumirá toda la responsabilidad, y saldrá en defensa de los derechos aquí autorizados; para todos los efectos la universidad actúa como un tercero de buena fe. EL AUTOR, autoriza a LA UNIVERSIDAD DEL ROSARIO, para que en los términos establecidos en la Ley 23 de 1982, Ley 44 de 1993, Decisión andina 351 de 1993, Decreto 460 de 1995 y demás normas generales sobre la materia, utilice y use la obra objeto de la presente autorización. -------------------------------------- POLITICA DE TRATAMIENTO DE DATOS PERSONALES. Declaro que autorizo previa y de forma informada el tratamiento de mis datos personales por parte de LA UNIVERSIDAD DEL ROSARIO para fines académicos y en aplicación de convenios con terceros o servicios conexos con actividades propias de la academia, con estricto cumplimiento de los principios de ley. Para el correcto ejercicio de mi derecho de habeas data cuento con la cuenta de correo habeasdata@urosario.edu.co, donde previa identificación podré solicitar la consulta, corrección y supresión de mis datos.
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dc.sourceinstname:Universidad del Rosario
dc.sourcereponame:Repositorio Institucional EdocUR
dc.subjectAnálisis bibliométrico
dc.subjectK-means
dc.subjectLiteratura financiera
dc.subjectMachine learning
dc.subjectVolatilidad
dc.titleThe use of machine learning in volatility: a review using K-means
dc.typebachelorThesis


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