In this paper we propose a multivariate discount weighted regression technique to give a tractable solution to the problem of variance estimation and forecasting for the multivariate local levelâ€¦ (More)

A number of recent emerging applications call for studying data streams, potentially infinite flows of information updated in realtime. When multiple co-evolving data streams are observed, anâ€¦ (More)

Statistical arbitrage strategies, such as pairs trading and its generalizations, rely on the construction of mean-reverting spreads enjoying a certain degree of predictability. Gaussian linearâ€¦ (More)

This paper develops a Bayesian procedure for estimation and forecasting of the volatility of multivariate time series. The foundation of this work is the matrix-variate dynamic linear model, for theâ€¦ (More)

This paper gives a methodology for decompositions of a very wide class of time series, including normal and non-normal time series, which are represented in state-space form. In particular the linkedâ€¦ (More)

where r1 + Â· Â· Â· + rm = k and rj â‰¥ 1. The problem of the moments consists of calculating Î¼r1,...,rm(X) in terms of Î¾, C. Until 1988 there was no general formula for any moment of arbitrary order kâ€¦ (More)

In multivariate time series, the estimation of the covariance matrix of the observation innovations plays an important role in forecasting as it enables the computation of the standardized forecastâ€¦ (More)

Limiting results for the posterior precision of the states are provided for discount Bayesian state space models. Time varying design vectors are considered as well as the usual constant designâ€¦ (More)

This paper develops a methodology for approximating the posterior first two moments of the posterior distribution in Bayesian inference. Partially specified probability models, which are defined onlyâ€¦ (More)