Volatility trading with machine learning forecasting methods

Volatility trading has become a prominent alternative to the traditional stock trading as the rapid development of web-trading in recent years has reduced significantly the costs of operating in the market. Moreover, machine learning techniques have enabled traders to rely heavily on statistical dec...

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Autores:
González Orjuela, Sergio Andrés
Tipo de recurso:
Fecha de publicación:
2018
Institución:
Universidad de los Andes
Repositorio:
Séneca: repositorio Uniandes
Idioma:
eng
OAI Identifier:
oai:repositorio.uniandes.edu.co:1992/34879
Acceso en línea:
http://hdl.handle.net/1992/34879
Palabra clave:
Volatilidad económica - Investigaciones - Métodos estadísticos
Toma de decisiones - Investigaciones - Estudio de casos
Aprendizaje automático (Inteligencia artificial) - Aplicaciones - Investigaciones - Estudio de casos
Mercado de capitales - Predicciones - Investigaciones - Estudio de casos
Análisis cluster - Investigaciones - Estudio de casos
Ingeniería
Rights
openAccess
License
https://repositorio.uniandes.edu.co/static/pdf/aceptacion_uso_es.pdf
Description
Summary:Volatility trading has become a prominent alternative to the traditional stock trading as the rapid development of web-trading in recent years has reduced significantly the costs of operating in the market. Moreover, machine learning techniques have enabled traders to rely heavily on statistical decision-making models to enhance the commonly used technical analysis. In this paper, a machine learning approach is used to predict proxies of short-term implied volatility clusters with high-frequency data, in order to perform trading strategies using vanilla options on a commercial platform. The empirical results indicate that tree-based methods outperform linear models in classifying these clusters using the time of the day as a key variable in the forecasting task. Financial results were mixed due to the high costs of operating in a 5-hour horizon, but it was found that long positions on at the money straddle strategies expiring in one day were profitable. The framework developed here can be used by small investors as a guidance to implement and assess theoretical strategies in accessible markets.