In this paper we present several new ¯ndings on the NoVaS transformation approach for volatility forecasting introduced by Politis (2003a,b, 2007). In particular: (a) we present a new method for accurate volatility forecasting using NoVaS ; (b) we introduce a \time- varying" version of NoVaS...

Many standard structural models in economics have the property that they induce persistent, partially predictable heteroskedasticity ("volatility clustering") in their key dependent variables, even when their underlying stochastic shock variables are all serially independent and homoskedastic,...

The evolution of financial markets is a complicated real-world phenomenon that ranks at the top in terms o fdifficulty of modeling and/or prediction. One reason for this difficulty is the well-documented nonlinearity that is inherently at work. The state-of-the-art on the nonlinear modeling of...

We review the notion of linearity of time series, and show that ARCH or stochastic volatility (SV) processes are not only non-linear: they are not even weakly linear, i.e., they do not even possess a martingale representation. Consequently, the use of Bartlett’s formula is unwarranted in...