SMU Data Science Review

Improving VIX Futures Forecasts using Machine Learning Methods

James Hosker, Southern Methodist UniversityFollow
Slobodan Djurdjevic, Southern Methodist UniversityFollow
Hieu Nguyen, Southern Methodist UniversityFollow
Robert Slater, Southern Methodist UniversityFollow

Abstract

The problem of forecasting market volatility is a difficult task for most fund managers. Volatility forecasts are used for risk management, alpha (risk) trading, and the reduction of trading friction. Improving the forecasts of future market volatility assists fund managers in adding or reducing risk in their portfolios as well as in increasing hedges to protect their portfolios in anticipation of a market sell-off event. Our analysis compares three existing financial models that forecast future market volatility using the Chicago Board Options Exchange Volatility Index (VIX) to six machine/deep learning supervised regression methods. This analysis determines which models provide best market volatility forecast. Using VIX futures and options data along with other technical indicators, our analysis compares multiple forecasting models for estimating the 1-month VIX futures contract (UX1) both 3 and 5-days forward. This analysis finds that machine/deep learning methods of Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) provide improved results over existing linear regression, principal components analysis (PCA) and ARIMA methods. Comparing estimated versus actual test data, both the RNN and LSTM methods show lower mean squared error (MSE), lower mean absolute error (MAE), higher explained variance, and higher correlation. Finally, an accuracy matrix was generated for each model, which showed RNN and LSTM had better overall accuracy due to high true positive and negative forecasts as well as much lower false positive forecasts.

Recommended Citation

Hosker, James; Djurdjevic, Slobodan; Nguyen, Hieu; and Slater, Robert (2018) "Improving VIX Futures Forecasts using Machine Learning Methods," SMU Data Science Review: Vol. 1: No. 4, Article 6.
Available at: https://scholar.smu.edu/datasciencereview/vol1/iss4/6