Predicting Historical Volatility for the S&P500

rapidminer, d3js, radoop

Predicting Historical Volatility is easy with RapidMiner. The attached process uses RapidMiner to recreate a research paper (Options trading driven by volatility directional accuracy) on how to predict historical volatility (HV) for the S&P500. The idea was to predict the HV 5 trading days ahead from Friday to Friday and then compare it with the Implied Volatility (IV) of the S&P500.  If the directions of HV and IV converge or diverge, then you would execute a specific type of option trade.

I did take some liberties with the research paper. At first I did use a Neural Net algorithm to train the data and I got a greater than 50% directional accuracy. When I switched to a SVM with and RBF kernel, I got it over 60%. Then when I added optimization for the Training and Testing Windows, gamma, and C parameters, I managed to get this over 70%.

I did test this “live” by paper trading it and managed to be right 7 out of 10 times. I did not execute any actually trades.

The data file is here: ^GSPC