A new model for time-varying spatial dependencies is introduced. It forms an extension to the popular spatial lag model and can be estimated conveniently by maximum likelihood. The spatial dependence parameter is assumed to follow a generalized autoregressive score (GAS) process. The theoretical...

U.S. trading in non-U.S. stocks has grown dramatically. Round-the-clock, these stocks trade in the home market, in the U.S. marketand, potentially, in both markets simultaneously. We use a state space model to study 24-hour price discovery. As opposed to thestandard "variance ratio'' approach,...

This discussion paper resulted in an article in the <I>Journal of Empirical Finance</I> (2005). Vol. 12, issue 3, pages 445-475.<p> The increasing availability of financial market data at intraday frequencies has not only led to the development of improved volatility measurements but has also inspired...</p></i>

The increasing availability of financial market data at intraday frequencies has not only led to the development of improved ex-post volatility measurements but has also inspired research into their potential value as an informa-tion source for longer horizon volatility forecasts. In this paper...

We introduce a dynamic network model with probabilistic link functions that depend on stochastically time-varying parameters. We adopt the widely used blockmodel framework and allow the high-dimensional vector of link probabilities to be a function of a low-dimensional set of dynamic factors....

U.S. trading in non-U.S. stocks has grown dramatically. Round-the-clock, these stocks trade in the home market, in the U.S. marketand, potentially, in both markets simultaneously. We use a state space model to study 24-hour price discovery. As opposed to thestandard variance ratio'' approach,...

The increasing availability of financial market data at intraday frequencies has not only led to the development of improved ex-post volatility measurements but has also inspired research into their potential value as an informa-tion source for longer horizon volatility forecasts. In this paper...

The increasing availability of financial market data at intraday frequencies has not only led to the development of improved volatility measurements but has also inspired research into their potential value as an information source for volatility forecasting. In this paper we explore the...

We introduce a dynamic network model with probabilistic link functions that depend on stochastically time-varying parameters. We adopt the widely used blockmodel framework and allow the highdimensional vector of link probabilities to be a function of a low-dimensional set of dynamic factors. The...

The increasing availability of financial market data at intraday frequencies has not only led to the development of improved ex-post volatility measurements but has also inspired research into their potential value as an informa-tion source for longer horizon volatility forecasts. In this paper...