The nonlinear dynamic relationship of exchange rates: Parametric and nonparametric causality testing

The present study investigates the linear and nonlinear causal linkages among six currencies denoted relative to United States dollar (USD), namely Euro (EUR), Great Britain Pound (GBP), Japanese Yen (JPY), Swiss Frank (CHF), Australian Dollar (AUD) and Canadian Dollar (CAD). The data spans two periods between 3/20/1991 and 3/20/2007. We apply a new nonparametric test for Granger non-causality by Diks and Panchenko [Diks, C., Panchenko, V., 2005. A note on the Hiemstra–Jones test for Granger noncausality. Studies in Nonlinear Dynamics and Econometrics 9 (art. 4); Diks, C., Panchenko, V., 2006. A new statistic and practical guidelines for nonparametric Granger causality testing. Journal of Economic Dynamics & Control 30, 1647–1669] and the linear Granger test on the return time series. To detect strictly nonlinear causality, we examine the pairwise VAR-filtered residuals as well as in a six-variate formulation. We find remaining significant bi- and uni-directional causal nonlinear relationships in the series. Finally, we investigate causality after controlling for conditional heteroskedasticity using a GARCH–BEKK model. Whilst the nonparametric test statistics are smaller in some cases, significant nonlinear causal linkages persisted even after GARCH filtering during both periods. This indicates that currency returns may exhibit asymmetries and statistically significant higher-order moments.     

Ethical implications of text generation in the age of artificial intelligence

We are at a turning point in the debate on the ethics of Artificial Intelligence (AI) because we are witnessing the rise of general-purpose AI text agents such as GPT-3 that can generate large-scale highly refined content that appears to have been written by a human. Yet, a discussion on the ethical issues related to the blurring of the roles between humans and machines in the production of content in the business arena is lacking. In this conceptual paper, drawing on agenda setting theory and stakeholder theory, we challenge the current debate on the ethics of AI and aim to stimulate studies that develop research around three new challenges of AI text agents: automated mass manipulation and disinformation (i.e., fake agenda problem), massive low-quality content production (i.e., lowest denominator problem) and the creation of a growing buffer in the communication between stakeholders (i.e., the mediation problem).