We propose a methodology for filtering, smoothing and assessing parameter and filtering uncertainty in misspecified score-driven models. Our technique is based on a general representation of the well-known Kalman filter and smoother recursions for linear Gaussian models in terms of the score of...

In the context of high-frequency financial data it is often assumed that sampling times are exogenous. This entails that financial asset prices, sampled on a grid of trade instants, are independent from the sampling times. We derive statistical tests capable of determining whether or not, and to...

In the context of high-frequency financial data it is often assumed that sampling times are exogenous. This entails that financial asset prices, sampled on a grid of trade instants, are independent from the sampling times. We derive statistical tests capable of determining whether or not, and to...

Equi-spaced sampling is the norm in applied work using high-frequency data. Due to trade intermittency, however, this traditional sampling scheme amounts to implicit random sampling. Under implicit random sampling and on continuous trajectories, we quantify the asymptotic biases of even realized...