During the last two decades there has been a growing interest in Particle Filtering (PF).
However, PF suffers from two long-standing problems that are referred to as sample …

This new edition updates Durbin & Koopman's important text on the state space approach to
time series analysis. The distinguishing feature of state space time series models is that …

We extend the well-known static spatial Durbin model by introducing a time-varying spatial
dependence parameter. The updating steps for this model are functions of past data and …

We verify whether parameter-driven and observation-driven classes of dynamic models can
outperform each other in predicting time-varying parameters. We consider existing and new …

We develop a systematic framework for the joint modeling of returns and multiple daily
realized measures. We assume a linear state space representation for the log realized …

A flexible predictive density combination is introduced for large financial data sets which
allows for model set incompleteness. Dimension reduction procedures that include learning …

The efficient importance sampling (EIS) method is a general principle for the numerical
evaluation of high-dimensional integrals that uses the sequential structure of target …

The paper develops a novel realized matrix-exponential stochastic volatility model of
multivariate returns and realized covariances that incorporates asymmetry and long memory …

A dynamic asset-allocation model is specified in probabilistic terms as a combination of
return distributions resulting from multiple pairs of dynamic models and portfolio strategies …

We study intraday stochastic volatility for four liquid stocks traded on the New York Stock
Exchange using a new dynamic Skellam model for high-frequency tick-by-tick discrete price …