Financial Time Series and Volatility Prediction using NoVaS Transformations

We extend earlier work on the NoVaS transformation approach introduced by Politis (2003a,b). The proposed approach is model-free and especially relevant when making forecasts in the context of model uncertainty and structural breaks. We introduce a new implied distribution in the context of NoVaS , a number of additional methods for implementing NoVaS and we examine the relative forecasting performance of NoVaS for making volatility predictions using real and simulated time series. We pay particular attention to data generating processes with varying coefficients and structural breaks. Our results clearly indicate that the NoVaS approach outperforms GARCH-made forecasts models in all cases we examined, except (as expected) when the data generating process was itself a GARCH model.