A simple model for now-casting volatility series

Nowcasting volatility of financial time series appears difficult with classical volatility models. This paper proposes a simple model, based on an ARMA representation of the log-transformed squared returns, that allows to estimate current volatility, given past and current returns, in a very simple way. The model can be viewed as a degenerate case of the stochastic volatility model with perfect correlation between the two error terms. It is shown that the volatility nowcasts do not depend on this correlation, so that both models provide the same nowcasts for given parameter values. A simulation study suggests that the ARMA and SV models have a similar performance, but that in cases of moderate persistence the ARMA model is preferable. An extension of the ARMA model is proposed that takes into account the so-called leverage effect. Finally, the alternative models are applied to a long series of daily S&P 500 returns.