Volatility trading with machine learning forecasting methods (2018)
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.