Financial time series and volatility prediction using NoVaS transformations

Abstract

We extend earlier work on the NoVaS transformation approach introduced by Politis (2003a, 2003b). 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 model forecasts in all cases we examined, except (as expected) when the data-generating process is itself a GARCH model. © 2008 Emerald Group Publishing Ltd. All rights reserved.