This paper present a variety of approaches to estimating error-correction relationships between CPI inflation and selected commodity price indices, based on their ability to to forecast out-of-sample predictions of CPI inflation. Depending on specification, commodity prices have marginal value...

Discussions of the future lie within the realm of soothsayers and other such prognosticators. The track record of those ‘professionals', often rightly described as charlatans, is famously not so great. Nor have many of the great scientists or politicians of modern times been more successful....

We investigate the role of crude oil spot and futures prices in the process of price discovery by using a cost-of-carry model with an endogenous convenience yield and daily data over the period from January 1990 to December 2008. We provide evidence that futures markets play a more important...

We investigate the role of crude oil spot and futures prices in the process of price discovery by using a cost-of-carry model with an endogenous convenience yield and daily data over the period from January 1990 to December 2008. We provide evidence that futures markets play a more important...

The ability of Google Trends data to forecast the number of new daily cases and deaths of COVID-19 is examined using a dataset of 158 countries. The analysis includes the computations of lag correlations between confirmed cases and Google data, Granger causality tests, and an out-of-sample...

We propose a new data-rich environment model of the yield curve, the macroeconomy, monetary policies and effective exchange rates for a panel of 11 countries: the iDREAM. The endogenous variables are observable (short- and long-term interest rates, exchange rates) and latent factors (economic...

Value-at-Risk (VaR) forecasting via a computational Bayesian framework is considered. A range of parametric models are compared, including standard, threshold nonlinear and Markov switching GARCH specifications, plus standard and nonlinear stochastic volatility models, most considering four...

We develop a new targeted maximum likelihood estimation method that provides improved forecasting for misspecified linear autoregressive models. The method weighs data points in the observed sample and is useful in the presence of data generating processes featuring structural breaks, complex...