The method of sieves has been widely used in estimating semiparametric and nonparametric models. In this paper, we first provide a general theory on the asymptotic normality of plug-in sieve M estimators of possibly irregular functionals of semi/nonparametric time series models. Next, we...

We propose a new score-driven model to capture the time-varying volatility and tail behavior of realized kernels. We assume realized kernels follow an F distribution with two time-varying degrees-of-freedom parameters, accounting for the Vol-of-Vol and the tail shape of the realized kernel...

We present a new model to decompose total daily return volatility into a filtered (high-frequency based) open-to-close volatility and a time-varying scaling factor. We use score-driven dynamics based on fat-tailed distributions to limit the impact of incidental large observations. Applying our...