El uso de machine learning en volatilidad: una revisión usando K-means
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Recientemente, el uso de técnicas de machine learning (ML) en diferentes disciplinas científicas ha experimentado un aumento sin precedentes. 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. A pesar de los anterior, los datos analizados en este articulo sugieren cambios al respecto. Datos obtenidos de la base Web of Science muestran entre 2001 y 2010 había 33 artículos asociados con este tema. Sorprendentemente, entre 2019 y 2023 se han publicado 189 manuscritos relacionados con este tipo de modelos. 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 esta
temática siguiendo una metodología narrativa, 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, y finanzas corporativas. Además, los resultados obtenidos indican que se presentan vacíos en la producción de trabajos relacionados con estos tópicos en revistas especializadas en finanzas y economía.
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