Machine learning tools are well known for their success in prediction. But prediction is not causation, and causal discovery is at the core of most questions concerning economic policy. Recently, however, the literature has focused more on issues of causality. This paper gently introduces some...

This paper proposes a new ‘World Volatility Index’, coined WVIX, by constructing the first index that approximates the aggregate volatility level of the G20 countries. The empirical analysis makes use of the factor dynamic conditional correlation model – with an automated methodology to...

This paper uses the novel quantile coherency approach to examine the tail dependence network of 49 international stock markets in the frequency domain. We find that geographical proximity and state of market development are important factors in stock markets networks. Both the short- and...

The paper investigates the life-cycle of the 2008-2009 financial crisis by linking the Macroeconomic Imbalance Procedure (MIP) Scoreboard of the European Commission to the crisis database of the European Systemic Risk Board (ESRB). The novelty of the analysis is that early warning capacity of...

This study examined the distribution of new firm formation in New England from 1999 through 2009. Using discrete entropy and entropy decomposition, it was found that single-unit firm births are spatially dispersed. The distributional patterns do not vary substantially across the study period....

Financial contagion and systemic risk measures are commonly derived from conditional quantiles by using imposed model assumptions such as a linear parametrization. In this paper, we provide model free measures for contagion and systemic risk which are independent of the specifcation of...

We develop early warning models for financial crisis prediction by applying machine learning techniques to macrofinancial data for 17 countries over 1870–2016. Most nonlin-ear machine learning models outperform logistic regression in out-of-sample predictions and forecasting. We identify...