Can unpredictable risk exposure be priced?

We study the link between beta predictability and the price of risk. An investor who desires exposure to a certain risk factor needs to predict what next period’s beta will be. We use a simple model to show that an ambiguity averse agent’s demand is lower when betas are hard to predict, leading to a reduction in risk premiums. We test the implications for downside betas and VIX betas. We find that they have economically and statistically small prices of risk once we account for the fact that an investor cannot observe ex-post realized betas when determining asset demand.     

Household Determinants of the Adoption of Improved Cassava Varieties using DNA Fingerprinting to Identify Varieties in Farmer Fields: A Case Study in Colombia

We examine factors affecting the adoption of improved cassava varieties of 217 households in the Cauca Department in southwest Colombia. Using DNA fingerprinting through Single Nucleotide Polymorphisms (SNPs), we identified different cultivars in farmers fields. We also used this information to remove possible bias in the adoption model that could have resulted from a misclassification of improved varieties (IVs). As a result, we found that farmers substantially overestimate their use of IVs and there are important differences in the determinants of adoption between farmer self‐identification and DNA fingerprinting. This finding implies that the incorporation of DNA fingerprinting in IV adoption studies is important to ensure the accuracy of future agricultural economic research and the relevance of subsequent policy recommendations.     

A systematic mapping study on enterprise architecture mining

Enterprise architecture allows companies to deal with digital transformation through the proactive presentation and alignment of business and IT in a holistic manner. One important challenge is EA modelling since it is time-consuming (thus expensive), error-prone, and biased owing to experts’ subjective opinions. This challenge might consequently be addressed through automatic EA modelling. We conducted a systematic mapping study to classify and evaluate the research concerning EA mining proposals developed specifically for TOGAF and Archimate. After analysing results, we concluded that the research field is not sufficiently mature and further research on EA mining is necessary.     

Policy Dimensions of Progressive Institutional Change: Lessons from China’s Construction of a Socialist Market Economy

Abstract:

Through a comprehensive review of the progressive institutional change (PIC) literature, I first discuss four possible trajectories of PIC by considering the forces of societal reaction that might frame the path, as well as the scale and scope of the changes. Thus, I pose four questions that need to be asked when related policies are formulated for PIC. To illustrate this method, I scrutinize the evidence from the construction of a socialist market economy by the Chinese government since the 1980s and argue that related policies have successfully promoted PIC from three dimensions: (1) curbing potential conflicts with power groups; (2) promoting a sense of awareness among stakeholders; and (3) minimizing disturbances to the community. Actually, the related changes demonstrate a dynamic “displacement process” for PICs. Despite the success of this sociosystem, it is found that the sustaining of PIC which requires policy factors that enhance instrumental efficiency in the Chinese context will serve as a challenge to the Chinese government ahead.     

Consumption,asset wealth,equity premium,term spread,and flight to quality

We link transitory deviations of consumption from its equilibrium relationship with aggregate wealth and labor income to equity returns on the one hand, and to two characteristics of bond investors—the premium demanded to hold long‐term assets, and “flight to quality” behavior—on the other hand. Using a panel of 10 euro area countries over the period 1984Q1–2017Q4, we show that a rise in the consumption–wealth ratio predicts both higher equity returns and the future term spread, while a fall in the consumption–wealth ratio explains a large fraction of the rise in the spread between the “risky” and the “safe‐haven” bond.     

Attracting potential blood donors: The role of personality and social marketing

Globally, society has been facing several and growing health problems, which increased the importance of efforts towards social change. In this context, using social marketing to create and implement programs that foster behavioural change has increased in the public health community. One of the health problems that society has been facing is the unceasing need for blood products, so the subject of blood donation became an important research topic in the area of social marketing, as social marketing strategies can contribute to solving this problem. It is essential to understand the factors that motivate individuals to donate blood, to enhance the recruitment and retention of blood donors. Our study aims at evaluating the key personality factors that influence blood donation behaviour to reach a higher understanding of what concerns this global issue. The method used to analyse the data is ANOVA. We conclude that the Personality of potential donors is significantly different from the non-donors' regarding Openness to Experience and Agreeableness. These results suggest that potential donors might perceive blood donation, as well as the associated barriers, differently from non-donors. Based on our findings, we suggest some perspectives for future research in this area.     

Diversity and business performance: 50 years of research

This study has carried out a review of the literature appearing on diversity in the last 50 years. Research findings from this period reveal it is impossible to assume there is a pure and simple relationship between diversity and performance without considering a series of variables that affect this relationship. In this study, emphasis has been placed on the analysis of results arrived at through empirical investigation on the relation between the most studied dimensions of diversity and performance. The results presented are part of a more extensive research.     

Identifying cycling-inducing neighborhoods: A latent class approach

Understanding how spatial attributes of cities and neighborhoods induce cycling is relevant for urban planning and policy making. In this work, ordered logit and latent class models are specified and estimated to analyze how the built environment affects bicycle-commuting frequency. Data come from a survey to 1,487 people in the city of Santiago, Chile, including sociodemographic information, travel behavior patterns and place of residence and work. Using geographic information systems tools, the built environment was characterized with variables calculated for a 500-m-radius buffer around the residential and work locations of each individual. Two models are estimated, first an ordered logit model confirms that built environment variables effect on cycling is similar to what has been reported in the literature, with some new findings such as an increase in cycling when public transport accessibility is low and the role of built environment attributes at the destination. Second, a latent class ordered logit is used to identify two classes of neighborhood in term of their cycling patterns, as a function of their density, presence of cycling infrastructure and distance to the main activity center of the city. This result allows to map the class membership probabilities, potentially helping to identify neighborhoods that encourage cycling and providing relevant information for policy making and infrastructure decisions.     

A critical overview of privacy-preserving approaches for collaborative forecasting

Cooperation between different data owners may lead to an improvement in forecast quality—for instance, by benefiting from spatiotemporal dependencies in geographically distributed time series. Due to business competitive factors and personal data protection concerns, however, said data owners might be unwilling to share their data. Interest in collaborative privacy-preserving forecasting is thus increasing. This paper analyzes the state-of-the-art and unveils several shortcomings of existing methods in guaranteeing data privacy when employing vector autoregressive models. The methods are divided into three groups: data transformation, secure multi-party computations, and decomposition methods. The analysis shows that state-of-the-art techniques have limitations in preserving data privacy, such as (i) the necessary trade-off between privacy and forecasting accuracy, empirically evaluated through simulations and real-world experiments based on solar data; and (ii) iterative model fitting processes, which reveal data after a number of iterations.     

An Exploratory Study on Nonlinear Causality Among the MILA Markets

According to conventional portfolio theory, an increase in the interconnectedness of international financial markets may reduce the potential for constructing diversified portfolios. This article explores the implications of the creation of the Latin American Integrated Market (MILA)¹ over the dependence structure of its members using correlation and cointegration analysis as well as linear and nonlinear Granger causality tests. The creation of MILA aimed to enhance the integration process that Latin American financial markets “naturally” present while still providing diversification opportunities to investors. The results of our empirical analysis suggest that such objective is being achieved. Evidence of a rise in cross-country linear correlations and their linear causal relationship supports the idea of an increasing financial integration process in the region, while the absence of cointegration and the weakening of the nonlinear causal relationship favors the creation of diversified regional portfolios. These findings provide valuable insights for investment portfolio designers, regulators, and supervisors.