The Impact of Power Distance and Uncertainty Avoidance on Real Options Exercise: Potential for Suboptimal Time Delays and Value Destruction

Real options (RO) valuation has been promoted as a way to evaluate investment opportunities and make investment decisions that takes into account the value of managerial flexibility in the face of uncertainty. Although RO enjoys a substantial body of literature considering its application and suitability in different situations, the impact of national culture on the application of RO has received little attention. Values embedded in national culture affect the behavior of managers. In particular, these values play a role in how managers frame information, communicate, and make decisions, directly and indirectly affecting behavior through cultural layers such as organizational culture. National culture, therefore, can systematically affect the application of RO, potentially leading to suboptimal outcomes. In this article, we hypothesize how 2 particularly relevant dimensions of culture, power distance and uncertainty avoidance, affect RO exercise, and estimate the magnitude suboptimality of these effects using a model we develop for the case of a put option. Our estimates suggest RO may not always be a superior valuation approach to net present value for national cultures with certain characteristics.     

The Politics of Inequality as Organised Spectacle: Why the Swiss Do Not Want to Tax the Rich

In 2015, Swiss voters had the opportunity to impose a tax on the super rich in a popular vote and thereby fund a redistributive policy. However, a large majority voted against its seemingly obvious self-interest and rejected the tax. We propose an explanation for this puzzling outcome, bridging the usually separate behavioralist and institutionalist perspectives on the politics of inequality. We start from the observation that political economy tends to neglect processes of preference formation. Theorising preferences as socially constructed, we show that interest groups played a major role in shaping the outcome of the vote. Business frames were multiplied through allied parties and the media and had a major impact on individual voting behaviour. In addition, we demonstrate that interest groups representing business interests derive the content of their communication from business’s structurally privileged position in the capitalist economy. Specifically, creating uncertainty about possible perverse effects of government policies on jobs and growth is a powerful tool to undermine popular support. Frames based on this structural power ultimately explain why the Swiss refrained from ‘soaking the rich.’     

Testing the asymmetric effects of exchange rate pass‐through in BRICS countries: Does the state of the economy matter?

This paper investigates not only the question of whether there is exchange rate pass‐through (ERPT) but also the extent to which the pass‐through is asymmetric or state‐dependent in the BRICS countries. Using monthly data from 1999:M1 to 2019:M12 and non‐linear smooth transition vector autoregressive (STVAR) model, our results provide evidence of period‐specific ERPT between the upper and lower regime periods, governed by the selected transition variables. The results further suggest that the pass‐through of exchange rate is higher when the economy is experiencing large appreciations and expansions as well as large depreciations and recessions. Theimplication for these findings is that ERPT is strongly affected by the state of the economy.     

A truly global crisis? Evidence from contagion dependence across international REIT markets

This study examines the global nature of the recent crisis under bivariate Markov-switching models for pre- and post-crisis periods using the breakpoint of August 9, 2007. It quantifies international synchronization of boom-bust regime switches to investigate contagion-type dynamic comovements of Real Estate Investment Trusts (REITs). Global REIT markets display persistent bust regimes from September 2008 to May 2009, whereas the regime-switching patterns are not significant in the pre-crisis period. The results provide new evidence for global REIT contagion phenomena and suggest greater difficulties in diversifying risks across global REIT markets during the post-crisis period.     

A Statistical Model to Investigate the Reproducibility Rate Based on Replication Experiments

The reproducibility crisis, that is, the fact that many scientific results are difficult to replicate, pointing to their unreliability or falsehood, is a hot topic in the recent scientific literature, and statistical methodologies, testing procedures and p‐values, in particular, are at the centre of the debate. Assessment of the extent of the problem–the reproducibility rate or the false discovery rate–and the role of contributing factors are still an open problem. Replication experiments, that is, systematic replications of existing results, may offer relevant information on these issues. We propose a statistical model to deal with such information, in particular to estimate the reproducibility rate and the effect of some study characteristics on its reliability. We analyse data from a recent replication experiment in psychology finding a reproducibility rate broadly coherent with other assessments from the same experiment. Our results also confirm the expected role of some contributing factor (unexpectedness of the result and room for bias) while they suggest that the similarity between original study and the replica is not so relevant, thus mitigating some criticism directed to replication experiments.     

Deep learning for modeling the collection rate for third-party buyers

This study evaluates a wide range of machine learning techniques such as deep learning, boosting, and support vector regression to predict the collection rate of more than 65,000 defaulted consumer credits from the telecommunications sector that were bought by a German third-party company. Weighted performance measures were defined based on the value of exposure at default for comparing collection rate models. The approach proposed in this paper is useful for a third-party company in managing the risk of a portfolio of defaulted credit that it purchases. The main finding is that one of the machine learning models we investigate, the deep learning model, performs significantly better out-of-sample than all other methods that can be used by an acquirer of defaulted credits based on weighted-performance measures. By using unweighted performance measures, deep learning and boosting perform similarly. Moreover, we find that using a training set with a larger proportion of the dataset does not improve prediction accuracy significantly when deep learning is used. The general conclusion is that deep learning is a potentially performance-enhancing tool for credit risk management.     

Health screening strategies for international air travelers during an epidemic or pandemic

Air travelers can carry an infectious disease's pathogenic microorganism in their bodies and spread the disease from one country to another in a few days. To delay the spread, health screening stations may be set up at airport terminals to screen travelers. This research tested three different health screening strategies, each with a different combination of screening stations at trip origins, destinations and connecting airports. Discrete event simulations were performed, based on the 2014 to 2016 Ebola virus epidemic, with special focus on travelers from the West African countries traveling to the United States, including travelers who transferred flights at airports in European Union member states. The effectiveness of the screening strategies was analyzed in terms of correct detection, missed detection and false alarm rate. The results showed that exit screening at trip origins brought big improvements in the performance measurements compared to no screening. However, additional screening at the destinations and connecting airports contributed marginal benefits.     

Sources of Productivity Growth Dynamics: Is Japan Suffering From Baumol's Growth Disease?

This study examines the sources of labor productivity growth dynamics in Japan (1970–2010) and investigates the extent to which Japanese economic performance has been affected by Baumol's growth disease (BGD). We find that BGD silently undermines Japanese economic growth. However, the magnitude is miniscule, and consequently the aggregate labor productivity growth rate has not been decreasing monotonically. We also explore how BGD is arising and why it is small in the Japanese economy. BGD is weak because (1) the positive Baumol growth effect is also working in certain services sectors and (2) BGD is not a durable phenomenon: even if a sector begins to suffer from BGD, it is likely to recover quickly.     

Estimating yield curves of the U.S. Treasury securities: An interpolation approach

Following the approach of interpolation, this paper proposes the multiple exponential decay model to fit yield curves for both the U.S. TIPS market and the conventional Treasury security market. Several estimation methods, including the unconstrained/constrained nonlinear minimization, quadratic programming, and the iterative linear least squares, are applied to estimate the unknown parameters according to different curve‐fitting purposes. Comparisons between the proposed model and the alternatives show that the multiple exponential decay successfully (1) adapts to a variety of shapes associated with yield curves, (2) (partially) keeps in line with the economic interpretations of Nelson–Siegel summarized by Diebold and Li ( 2006 ), and (3) dominates the competing models in curve‐fitting performance measured by mean fitted‐price errors over the sample period. In addition, the exact specification of a nonparametric interpolation model is pinned down by applying three statistical tools, which enable us to jointly take into account validity, optimality, and parsimoniousness of the proposed model.