  In this paper we formulate a regression problem to predict realized
volatility by using option price data and enhance VIX-styled volatility
indices' predictability and liquidity. We test algorithms including regularized
regression and machine learning methods such as Feedforward Neural Networks
(FNN) on S&P 500 Index and its option data. By conducting a time series
validation we find that both Ridge regression and FNN can improve volatility
indexing with higher prediction performance and fewer options required. The
best approach found is to predict the difference between the realized
volatility and the VIX-styled index's prediction rather than to predict the
realized volatility directly, representing a successful combination of human
learning and machine learning. We also discuss suitability of different
regression algorithms for volatility indexing and applications of our findings.
