USING MACHINE LEARNING TO PREDICT REALIZED VARIANCE

Volatility index is a portfolio of options and represents market expectation of the underlying security's future realized volatility/variance. Traditionally the index weighting is based on a variance swap pricing formula. In this paper we propose a new method for building volatility index by formulating a variance prediction problem using machine learning. We test algorithms including Ridge regression, Feedforward Neural Networks and Random Forest on S&P 500 Index option data. By conducting a time series validation we show that the new weighting method can achieve higher predictability to future return variance and require fewer options. It is also shown that the weighting method combining the traditional and the machine learning approaches performs the best.