In this paper we consider the value of Google Trends search data for nowcasting (and forecasting) GDP growth for a developed (U.S.) and emerging-market economy (Brazil). Our focus is on the marginal contribution of "Big Data" in the form of Google Trends data over and above that of traditional...

A structural break is viewed as a permanent change in the parameter vector of a model. Using taxonomies of all sources of forecast errors for both conditional mean and conditional variance processes, we consider the impacts of breaks and their relevance in forecasting models: (a) where the...

This paper considers the impact of US and UK Quantitative Easing (QE) on their respective economies with a particular focus on the stock market, production and price levels. We conduct an empirical quantitative exercise based on a novel six-variable VAR model, which combines macroeconomic and...

In recent work, we have developed a theory of economic forecasting for empirical econometric models when there are structural breaks. This research shows that well-specified models may forecast poorly, whereas it is possible to design forecasting devices more immune to the effects of breaks. In...

We examine the clustering behaviour of price and variance jumps using high-frequency data, modelled as a marked Hawkes process embedded in a bivariate jump-diffusion model. After de-periodisation of the intraday data, we find that the jumps of both individual stocks and a broad index exhibit...

In this paper, we forecast Bitcoin's returns and return jumps using a self-exciting process embedded in a stochastic volatility model. We show the existence of the jump clustering feature, which varies depending on the frequency of the data. In an out-of-sample setting, we use a particle filter...