Heuristic Solutions to Technical Issues Associated with Clustered Volatility Prediction using Support Vector Machines

We outline technological issues and our findings for the problem of prediction of relative volatility bursts in dynamic time-series utilizing support vector classifiers (SVC). The core approach used for prediction has been applied successfully to detection of relative volatility clusters. In applying it to prediction, the main issue is the selection of the SVC training/testing set. We describe three selection schemes and experimentally compare their performances in order to propose a method for training the SVC for the prediction problem. In addition to performing cross-validation experiments, we propose an improved variation to sliding window experiments utilizing the output from SVC's decision function. Together with these experiments, we show that accurate and robust prediction of volatile bursts can be achieved with our approach