Machine Learning for Volatility Trading (Presentation Slides)

Academics and practitioners have developed many models for volatility measurement and forecast – I estimate that the total number of available models to be about 200-300 if we count all modifications of intraday estimators, GARCH-type and continuous-time models. In practice, the estimate and forecast of the volatility serves provide vital inputs to many applications ranging from signal construction to algorithmic strategies and quantitative methods for portfolio allocation. By applying machine learning to the volatility modeling, we can reduce the back-test bias and, as a result, improve the performance of live strategies. First, I implemented about 40 different volatility models from 4 separate model classes including intraday estimators, GARCH-type and Bayesian models, and Hidden Markov Chain (HMC) models. Then, I applied the supervised learning for each of the volatility models with the goal is to analyze the out-of-sample fit of the model prediction to the time series data. I propose a few regression-based tests which are applied to gauge the performance of all volatility models. The final step is the reinforcement learning that includes aggregation and analysis of the test results from the supervised learning. The goal is to dynamically select the best model out of 40 that provides the best predicative power out-of-sample. I use the analogy to the web-search to weight the importance of the test results when producing volatility forecasts for specific trading algorithms. One of key discoveries is that Hidden Markov Chain model is one of the best model for volatility forecast across many asset classes. I also observe the cyclical pattern in the rankings of the best models. On one hand, Hidden Chain models perform the best in periods with strong trends. On the other hand, simple intraday estimators perform the best in periods with range-bound markets. The machine learning enables to dynamically choose the best model for the present cycle.