We propose a novel observation-driven finite mixture model for the study of banking data.
The model accommodates time-varying component means and covariance matrices, normal …

The analysis of the intraday dynamics of covariances among high-frequency returns is
challenging due to asynchronous trading and market microstructure noise. Both effects lead …

In this article, we develop a general framework to analyze state space models with time-
varying system matrices, where time variation is driven by the score of the conditional …

In this paper we develop a new theoretical framework for the analysis of state space models
with time-varying parameters. We let the driver of the time variation be the score of the …

This paper extends the canonical model of epidemiology, the SIRD model, to allow for time-
varying parameters for real-time measurement and prediction of the trajectory of the Covid …

The analysis of the intraday dynamics of correlations among high-frequency returns is
challenging due to the presence of asynchronous trading and market microstructure noise …

We argue that existing methods for the treatment of missing observations in time-varying
parameter observation-driven models lead to inconsistent inference. We provide a formal …

This article deals with the problem of estimating the volatility of a financial security in a
market with frictions. We propose a microstructural model with time-varying fundamental …

We propose a model of price formation in which the trading price varies only if the value of
the information signal is large enough to guarantee a profit in excess of transaction costs …

We argue that existing methods for the treatment of missing observations in observation-
driven models lead to inconsistent inference. We provide a formal proof of this inconsistency …