In this paper we consider the forecasting performance of a range of semi- and non- parametric methods applied to high frequency electricity price data. Electricity price time-series data tend to be highly seasonal, mean reverting with price jumps/spikes and time- and price-dependent volatility....

In this paper graphical modelling is used to select a sparse structure for a multivariate time series model of New Zealand interest rates. In particular, we consider a recursive structural vector autoregressions that can subsequently be described parsimoniously by a directed acyclic graph, which...

This paper describes the various stages in building a statistical model to predict temperatures in the core of a reactor, and compares the benefits of this model with those of a physical model. We give a brief background to this study and the applications of the model to rapid online monitoring...

In canonical vector time series autoregressions, which permit dependence only on past values, the errors generally show contemporaneous correlation. By contrast structural vector autoregressions allow contemporaneous series dependence and assume errors with no contemporaneous correlation. Such...

This paper investigates empirically the nature of the interactions between mass media, investor attention and the stock market using data from a sample of 16 spin-off deals traded on NYSE and published between 2004 and 2010 in 'Wall Street Journal', the US's second-largest newspaper by...