In this paper, we carry out a comprehensive comparison of Gaussian generalized autoregressive conditional heteroskedasticity (GARCH) and autoregressive stochastic volatility (ARSV) models for volatility forecasting using the S&P 500 Index. In particular, we investigate their performance using...

Background: Accurate inventory management of intermittent spare parts requires precise demand forecasting. The sporadic and irregular nature of demand, characterized by long intervals between occurrences, results in a significant data imbalance, where demand events are vastly outnumbered by...

Markets are information aggregators. But how do they incorporate new data into their pricing? We examine the response of prediction markets to a novel information shock in a quasi-natural experiment: How did the absence announcements of elite soccer players influence the betting odds of affected...

In this paper, we propose a component-based dynamic factor model for nowcasting GDP growth. We combine ideas from "bottom-up" approaches, which utilize the national income accounting identity through modelling and predicting sub-components of GDP, with a dynamic factor (DF) model, which is...