Do foreign exchange forecasters apply asymmetric loss functions? Evidence from three major exchange rates

This article investigates which type of loss function is consistent with the hypothesis that major exchange rate forecasts, i.e. the euro, the British pound, and the Japanese yen vis-à-vis the US dollar, are rational. We apply a comprehensive data set, which also allows us to examine different forecast horizons and heterogeneity of forecasters.     

Bayesian Inference in Spatial Sample Selection Models

In this study, we consider Bayesian methods for the estimation of a sample selection model with spatially correlated disturbance terms. We design a set of Markov chain Monte Carlo algorithms based on the method of data augmentation. The natural parameterization for the covariance structure of our model involves an unidentified parameter that complicates posterior analysis. The unidentified parameter – the variance of the disturbance term in the selection equation – is handled in different ways in these algorithms to achieve identification for other parameters. The Bayesian estimator based on these algorithms can account for the selection bias and the full covariance structure implied by the spatial correlation. We illustrate the implementation of these algorithms through a simulation study and an empirical application.     

Good global citizen? ASEAN’s image building in the United Nations

ABSTRACT

Based on role theory, the article examines the images that ASEAN member governments project of their organization. It rests on a discourse analysis of 198 speeches in the United Nations General Assembly between 1998 and 2017. Findings suggest that ASEAN does not figure as a top priority for delegates and that an overarching ASEAN role conception is missing. However, their addresses reveal parameters on which a collective role conception can be built. Individual ASEAN countries undertake great efforts to project themselves as ‘good global citizens,’ a role conception that could also be applied to ASEAN.     

Forecast-error-based estimation of forecast uncertainty when the horizon is increased

Past forecast errors are employed frequently in the estimation of the unconditional forecast uncertainty, and several institutions have increased their forecast horizons in recent times. This work addresses the question of how forecast-error-based estimation can be performed if there are very few errors available for the new forecast horizons. It extends the results of Knüppel (2014) in order to relax the condition on the data structure that is required for the SUR estimator to be independent of unknown quantities. It turns out that the SUR estimator of the forecast uncertainty, which estimates the forecast uncertainty for all horizons jointly, tends to deliver large efficiency gains relative to the OLS estimator (i.e., the sample mean of the squared forecast errors for each individual horizon) in the case of increased forecast horizons. The SUR estimator is applied to the forecast errors of the Bank of England, the US Survey of Professional Forecasters, and the FOMC.     

The more others care,the more you share? – Social contagion as a stardom trigger of social media superstars –

Emerging superstars on social media platforms reshape the media landscape. This research analyses social contagion as a stardom trigger of social media superstars (SMS). We argue that in addition to serving as a quality indicator, the number of observed consumers of SMS performances also indicates the suitability of discussing the SMS performances with others. We experimentally manipulated the number of previous views of a YouTube video and find that a high number of previous views significantly increases the perceived quality and the video’s discussion suitability even when holding all objective video characteristics constant. We discuss implications for aspiring SMS and (online) marketers.     

Secrets to design an effective message on Facebook: an application to a touristic destination based on big data analysis

The objective of this research is to identify which are the key variables for designing a message in a social network that can be used by an advertiser to generate Positive/Negative Engagement. The message’s design variables have been classified into four main groups: (i) Message Tools (presence of text, images, video, labels, applications, interactive games and events calendar or others), (ii) Appropriate Message Structure (length and intelligibility), (iii) Informative Cues (links to the brand, orientation towards the product or the brand, topics relevant to the audience, and a remuneration’s promise) and (iv) Persuasive and Emotional Cues (emotional signals, valence, endorsement and influencer mentions). The focus has been a tourist destination: Brand Spain that is advertised through Facebook. A content analysis was carried out and regression analysis with optimal scaling was used on 180 Spain brand’s publications; 57,626 audience reactions to such publications; and 1361 audience comments on the Brand Spain Official Fan Page. According to our results, from the four blocks of predictive variables, only two of them are useful to predict Positive/Negative Engagement: (i) the use of Message Tools (videos), and (ii) the use of informative cues (relevant topics, links on posts, and post’s orientation towards product).     

Contagion of network products in small-world networks

We formulate a model in which agents embedded in an exogenous social network decide whether to adopt a new network product or not. In the theoretical part of the paper, we characterize the stochastically stable equilibria for complete networks and cycles. For an arbitrary network structure, we develop a novel graph decomposition method to characterize the set of recurrent communication states, which is a superset of stochastically stable equilibria of the adoption game presented in our model. In the simulation part, we study the contagion process of a network product in small-world networks that systematically represent social networks. We simulate a generalization of the Morris (Rev Econ Stud 67(1):57–78, 2000) Contagion model that can explain the chasm between early adopters and early majority. Our numerical analysis shows that the failure of a new network product is less likely in a highly cliquish network. In addition, the contagion process reaches to steady state faster in random networks than in highly cliquish networks. It turns out that marketers should work with mixed marketing strategies, which will result in a full contagion of a network product and faster contagion rates with a higher probability.

     

“Just a Little Respect”: Effects of a Layoff Agent’s Actions on Employees’ Reactions to a Dismissal Notification Meeting

For several decades, most discussion on financial fraud has centered on the fraud triangle, which has evolved over time through various extensions and re-interpretations. While this has served the profession well, the articulation of the human side of the act is indirect and diffused. To address this limitation, this research develops a model to explain the role of human desires, intentions, and actions in indulgence of, or resistance to, the act of financial fraud. Evidence from religion, philosophy, sociology, neurology, behavioral economics, and social psychology is integrated to develop and support an alternative fraud model, called the disposition-based fraud model (DFM). To articulate the model, its two primary components, disposition and temptation, are further developed and extended. Although the DFM is generally applicable to any act of fraud, this paper focuses on executive fraud. The similarities and differences between the DFM and extant fraud models are discussed. Importantly, in light of the DFM, a re-interpretation of the fraud triangle is made to improve our understanding of the human element in it. Additionally, potential implications of the model for corporate governance are discussed, suggestions for further research are offered, and the DFM’s strengths and limitations are noted.     

Portfolio optimization based on GARCH-EVT-Copula forecasting models

This study uses GARCH-EVT-copula and ARMA-GARCH-EVT-copula models to perform out-of-sample forecasts and simulate one-day-ahead returns for ten stock indexes. We construct optimal portfolios based on the global minimum variance (GMV), minimum conditional value-at-risk (Min-CVaR) and certainty equivalence tangency (CET) criteria, and model the dependence structure between stock market returns by employing elliptical (Student-$t$ and Gaussian) and Archimedean (Clayton, Frank and Gumbel) copulas. We analyze the performances of 288 risk modeling portfolio strategies using out-of-sample back-testing. Our main finding is that the CET portfolio, based on ARMA-GARCH-EVT-copula forecasts, outperforms the benchmark portfolio based on historical returns. The regression analyses show that GARCH-EVT forecasting models, which use Gaussian or Student-$t$ copulas, are best at reducing the portfolio risk.