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AbstractThis paper proposes world steel production as an indicator of global real economic activity. World steel production data is published with only a one‐month delay, thereby providing timely information for world real GDP forecasters. We find that world steel production and Lutz Kilian's (2009) index of global real economic activity generate large gains in forecasting world real GDP, relative to an autoregressive benchmark. A forecast combination of world steel production, Kilian's (2009) index of global real economic activity and an index of the industrial production of OECD countries plus six non‐OECD emerging economies produces significant gains in forecasting world real GDP, relative to an autoregressive benchmark

Since the advent of Markov chain Monte Carlo (MCMC) methods in the early 1990s, Bayesian methods have been proposed for a large and growing number of applications. One of the main advantages of Bayesian inference is the ability to deal with many different sources of uncertainty, including data, models, parameters and parameter restriction uncertainties, in a unified and coherent framework. This book contributes to this literature by collecting a set of carefully evaluated contributions that are grouped amongst two topics in financial economics. The first three papers refer to macro-finance issues for real economy, including the elasticity of factor substitution (ES) in the Cobb-Douglas production function, the effects of government public spending components, and quantitative easing, monetary policy and economics. The last three contributions focus on cryptocurrency and stock market predictability. All arguments are central ingredients in the current economic discussion and their importance has only been further emphasized by the COVID-19 crisis.

Cryptocurrencies have recently gained a lot of interest from investors, central banks and governments worldwide. The lack of any form of political regu- lation and their market far from being "efficient", require new forms of regulation in the near future. From an econometric viewpoint, the process underlying the evo- lution of the cryptocurrencies' volatility has been found to exhibit at the same time differences and similarities with other financial time–series, e.g. foreign exchanges returns. This short note focuses on predicting the conditional volatility of the four most traded cryptocurrencies: Bitcoin, Ethereum, Litecoin and Ripple. We investi- gate the effect of accounting for long memory in the volatility process as well as its asymmetric reaction to past values of the series to predict: one day, one and two weeks volatility levels.