The use of machine learning in volatility: a review using K-means
Recientemente, el uso de técnicas de machine learning (ML) en diferentes disciplinas científicas ha experimentado un aumento sin precedentes. Esto, como consecuencia de los avances en computación que han permitido obtener resultados satisfactorios a costos computacionales moderados. El área de las f...
- Autores:
- Tipo de recurso:
- Fecha de publicación:
- 2021
- Institución:
- Universidad del Rosario
- Repositorio:
- Repositorio EdocUR - U. Rosario
- Idioma:
- eng
- OAI Identifier:
- oai:repository.urosario.edu.co:10336/33363
- Acceso en línea:
- https://doi.org/10.48713/10336_33363
https://repository.urosario.edu.co/handle/10336/33363
- Palabra clave:
- Análisis bibliométrico
K-means
Literatura financiera
Machine learning
Volatilidad
Economía financiera
Bibliometric analysis; financial literatura; K-means; Machine learning; Volatility
Bibliometric analysis
Financial literatura
K-means
Machine learning
Volatility
- Rights
- License
- Abierto (Texto Completo)
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| dc.title.es.fl_str_mv |
The use of machine learning in volatility: a review using K-means |
| dc.title.TranslatedTitle.es.fl_str_mv |
El uso de machine learning en volatilidad: una revisión utilizando k-means |
| title |
The use of machine learning in volatility: a review using K-means |
| spellingShingle |
The use of machine learning in volatility: a review using K-means Análisis bibliométrico K-means Literatura financiera Machine learning Volatilidad Economía financiera Bibliometric analysis; financial literatura; K-means; Machine learning; Volatility Bibliometric analysis Financial literatura K-means Machine learning Volatility |
| title_short |
The use of machine learning in volatility: a review using K-means |
| title_full |
The use of machine learning in volatility: a review using K-means |
| title_fullStr |
The use of machine learning in volatility: a review using K-means |
| title_full_unstemmed |
The use of machine learning in volatility: a review using K-means |
| title_sort |
The use of machine learning in volatility: a review using K-means |
| dc.contributor.advisor.none.fl_str_mv |
Molina Muñoz, Jesús Enrique |
| dc.subject.es.fl_str_mv |
Análisis bibliométrico K-means Literatura financiera Machine learning Volatilidad |
| topic |
Análisis bibliométrico K-means Literatura financiera Machine learning Volatilidad Economía financiera Bibliometric analysis; financial literatura; K-means; Machine learning; Volatility Bibliometric analysis Financial literatura K-means Machine learning Volatility |
| dc.subject.ddc.es.fl_str_mv |
Economía financiera |
| dc.subject.keyword.es.fl_str_mv |
Bibliometric analysis; financial literatura; K-means; Machine learning; Volatility Bibliometric analysis Financial literatura K-means Machine learning Volatility |
| description |
Recientemente, el uso de técnicas de machine learning (ML) en diferentes disciplinas científicas ha experimentado un aumento sin precedentes. Esto, como consecuencia de los avances en computación que han permitido obtener resultados satisfactorios a costos computacionales moderados. El área de las finanzas no ha sido una excepción. En los últimos años, se han publicado numerosos trabajos utilizando técnicas de ML. Sin embargo, uno de los temas con menor número de artículos desarrollados en este contexto, es el de la volatilidad. Este panorama ha cambiado. Datos obtenidos de la base Web of Science muestran que para los años 2001 y 2010 había 2 y 1 artículos asociados con este tema, respectivamente. Sorprendentemente, entre 2019 y 2021 se han publicado 37 manuscritos relacionados con esta temática. El propósito de este artículo, es revisar los trabajos relacionados con las aplicaciones de ML en volatilidad. Para ello, se propone una clasificación de las principales propuestas sobre este tema, acompañada de un análisis estadístico y bibliométrico en el que se utilizan técnicas novedosas como K-means. Los resultados son sugerentes. Aunque la mayoría de los artículos se centran en la predicción de la volatilidad a través de redes neuronales y support vector machines, se evidencia una ausencia de artículos relacionados con transmisión de la volatilidad, calibración de superficies de volatilidad, financiación de proyectos y finanzas corporativas. |
| publishDate |
2021 |
| dc.date.accessioned.none.fl_str_mv |
2021-12-16T21:57:05Z |
| dc.date.available.none.fl_str_mv |
2021-12-16T21:57:05Z |
| dc.date.created.none.fl_str_mv |
2021-12-05 |
| dc.type.eng.fl_str_mv |
bachelorThesis |
| dc.type.coar.fl_str_mv |
http://purl.org/coar/resource_type/c_7a1f |
| dc.type.document.es.fl_str_mv |
Artículo |
| dc.type.spa.spa.fl_str_mv |
Trabajo de grado |
| dc.identifier.doi.none.fl_str_mv |
https://doi.org/10.48713/10336_33363 |
| dc.identifier.uri.none.fl_str_mv |
https://repository.urosario.edu.co/handle/10336/33363 |
| url |
https://doi.org/10.48713/10336_33363 https://repository.urosario.edu.co/handle/10336/33363 |
| dc.language.iso.es.fl_str_mv |
eng |
| language |
eng |
| dc.rights.coar.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
| dc.rights.acceso.es.fl_str_mv |
Abierto (Texto Completo) |
| rights_invalid_str_mv |
Abierto (Texto Completo) http://purl.org/coar/access_right/c_abf2 |
| dc.format.extent.es.fl_str_mv |
41 pp |
| dc.format.mimetype.es.fl_str_mv |
application/pdf |
| dc.publisher.none.fl_str_mv |
Universidad del Rosario |
| dc.publisher.department.none.fl_str_mv |
Escuela de Administración |
| dc.publisher.program.none.fl_str_mv |
Administración de Negocios Internacionales |
| publisher.none.fl_str_mv |
Universidad del Rosario |
| institution |
Universidad del Rosario |
| dc.source.bibliographicCitation.none.fl_str_mv |
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Molina Muñoz, Jesús Enrique87219960600Castañeda Torres, RicardAdministrador de Negocios InternacionalesPregradoFull timee28e71e5-f792-45ba-ba35-14ab9212a2306002021-12-16T21:57:05Z2021-12-16T21:57:05Z2021-12-05Recientemente, el uso de técnicas de machine learning (ML) en diferentes disciplinas científicas ha experimentado un aumento sin precedentes. Esto, como consecuencia de los avances en computación que han permitido obtener resultados satisfactorios a costos computacionales moderados. El área de las finanzas no ha sido una excepción. En los últimos años, se han publicado numerosos trabajos utilizando técnicas de ML. Sin embargo, uno de los temas con menor número de artículos desarrollados en este contexto, es el de la volatilidad. Este panorama ha cambiado. Datos obtenidos de la base Web of Science muestran que para los años 2001 y 2010 había 2 y 1 artículos asociados con este tema, respectivamente. Sorprendentemente, entre 2019 y 2021 se han publicado 37 manuscritos relacionados con esta temática. El propósito de este artículo, es revisar los trabajos relacionados con las aplicaciones de ML en volatilidad. Para ello, se propone una clasificación de las principales propuestas sobre este tema, acompañada de un análisis estadístico y bibliométrico en el que se utilizan técnicas novedosas como K-means. Los resultados son sugerentes. Aunque la mayoría de los artículos se centran en la predicción de la volatilidad a través de redes neuronales y support vector machines, se evidencia una ausencia de artículos relacionados con transmisión de la volatilidad, calibración de superficies de volatilidad, financiación de proyectos y finanzas corporativas.Recently, 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.2022-01-01 00:00:01: Script de automatizacion de embargos. Correo enviado 16 dic 2021: Hemos realizado la publicación de su documento: The use of machine learning in volatility: a review using K-means, el cual puede consultar en el siguiente enlace: https://repository.urosario.edu.co/handle/10336/33363 Identificamos que ha marcado como restringido en el formulario, pero no realizo la notificación al correo edocur@urosario.edu.co, justificando la medida restrictiva al acceso del texto completo de su obra, frente a lo cual, el documento ha quedado embargado solo por dos meses hasta el 16 de febrero de 2022 en concordancia con las Políticas de Acceso Abierto de la Universidad. 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La solicitud de asesoría puede agendarla en el siguiente link: https://n9.cl/agendamiento_servicios_crai2022-02-16 01:01:01: Script de automatizacion de embargos. info:eu-repo/date/embargoEnd/2022-02-1541 ppapplication/pdfhttps://doi.org/10.48713/10336_33363 https://repository.urosario.edu.co/handle/10336/33363engUniversidad del RosarioEscuela de AdministraciónAdministración de Negocios InternacionalesAbierto (Texto Completo)EL 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.http://purl.org/coar/access_right/c_abf2Al-Fattah, Saud M. 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