A simple model for now-casting volatility series

The popular volatility models focus on the conditional variance given past observations, whereas the (arguably most important) information in the current observation is ignored. This paper proposes a simple model for now-casting volatilities based on a specific ARMA representation of the log-transformed squared returns that allows us to estimate the current volatility as a function of current and past returns. The model can be viewed as a stochastic volatility model with perfect correlation between the two error terms. It is shown that the volatility nowcasts are invariant to this correlation, and therefore the estimated volatilities coincide. We propose an extension of our nowcasting model that takes into account the so-called leverage effect. The alternative models are used to estimate daily return volatilities from the S&P 500 stock price index.