Financial Instruments and Promotional Banking in the EU are Growing — But is this Justified?

Institutions and the European Investment Bank are at the forefront of EU investment policy. Their role is expanding and it is now a widely-held position that an improvement in the economy will not eliminate the need for their intervention. The proposals for the 2021–2027 MFF launch more financial instruments and present ‘InvestEU’ as a larger more powerful version of the European Fund for Strategic Investments (EFSI). However, promotional banking is often misunderstood and so is the actual size of promotional banking in Europe in which InvestEU will operate.     

Characterization of the Row Geometric Mean Ranking with a Group Consensus Axiom

An axiomatic approach is applied to the problem of extracting a ranking of the alternatives from a pairwise comparison ratio matrix. The ordering induced by row geometric mean method is proved to be uniquely determined by three independent axioms, anonymity (independence of the labelling of alternatives), responsiveness (a kind of monotonicity property) and aggregation invariance, which requires the preservation of group consensus, that is, the pairwise ranking between two alternatives should remain unchanged if unanimous individual preferences are combined by geometric mean.     

Fixed-Term Contracts and Labor Market Duality in France

The French labor market is segmented between permanent and temporary workers. The second category has difficulty in getting an open-ended contract. This paper aims at depicting workers on short-term contracts and shows the consequences on their professional career are negative and significant. A large part of the wage gap between permanent and temporary workers remains unexplained by observable characteristics. They receive less on-the-job training and their likelihood of obtaining a stable job is lower than 30% after 1 year and a half. They have also a higher probability of being the adjustment variable in case of an economic negative shock. These different findings show the importance of using public policies to encourage transitions from fixed-term to permanent employment and reducing labor-market duality.     

The Engineer and The Plumber: Mediating Mumbai's Conflicting Infrastructural Imaginaries

Two decades ago, the rules governing the provision of piped municipal water supply in Mumbai became linked to the policy frameworks governing eligibility for a property titling scheme. This article outlines the ideological basis and practical implications of the shift, as well as the contradictions of the new regulatory regime. The article demonstrates how these contradictions have been mediated by the material and practical knowledge, embodied expertise, local authority and wide‐ranging socio‐political work of two sets of actors: municipal water engineers and a cast of characters known locally as ‘plumbers’. The social, political and hydraulic imaginaries animating the work of ‘plumbing’ are bound up with a temporal and spatial imaginary distinctly at odds with the network‐flow conception of hydraulic engineering within which the work of water supply planning and distribution in Mumbai is conceptualized, materialized and institutionalized. The hydraulic and legal contradictions of these clashing infrastructural idioms––of flow and event––have rendered the regulatory framework highly unstable. These contradictions eventually erupted in Mumbai's waterscape, leaving the city's water infrastructures suspended in a highly politicized state of limbo between dueling infrastructural imaginaries.     

A New Regulatory Paradigm for the Digital Sector in Europe

The new regulatory framework in the digital sector in Europe is likely to fall short of expectations. A fragmented digital market with insuffi cient incentives for investment is the probable outcome. European regulators should abandon their approach in favour of a new and more flexible set of ideas discussed in this paper.     

Integrated bio-economic models as tools to support land-use decision making: a review of potential and limitations

Bio-economic modelling has become a useful tool for anticipating the outcomes of policies and technologies before their implementation. Advances in mathematical programming have made it possible to build more comprehensive models. In an overview of recent studies about bio-economic models applied to land-use problems in agriculture and forestry, we evaluated how aspects such as uncertainty, multiple objective functions, system dynamics and time have been incorporated into models. We found that single objective models were more frequently applied at the farm level, while multiple objective modelling has been applied to meet concerns at the landscape level. Among the objectives, social aspects are seldom represented in all models, when being compared to economic and environmental aspects. The integration of uncertainty is occasionally a topic, while stochastic approaches are more frequently applied than non-stochastic robust methods. Most multiple-objective models do not integrate uncertainty or sequential decision making. Static approaches continue to be more recurrent than truly dynamic models. Even though integrating multiple aspects may enhance our understanding of a system; it involves a tradeoff between complexity and robustness of the results obtained. Land-use models have to address this balance between complexity and robustness in order to evolve towards robust multiple-objective spatial optimization as a prerequisite to achieve sustainability goals.     

Stock market volatility forecasting: Do we need high-frequency data?

The general consensus in the volatility forecasting literature is that high-frequency volatility models outperform low-frequency volatility models. However, such a conclusion is reached when low-frequency volatility models are estimated from daily returns. Instead, we study this question considering daily, low-frequency volatility estimators based on open, high, low, and close daily prices. Our data sample consists of 18 stock market indices. We find that high-frequency volatility models tend to outperform low-frequency volatility models only for short-term forecasts. As the forecast horizon increases (up to one month), the difference in forecast accuracy becomes statistically indistinguishable for most market indices. To evaluate the practical implications of our results, we study a simple asset allocation problem. The results reveal that asset allocation based on high-frequency volatility model forecasts does not outperform asset allocation based on low-frequency volatility model forecasts.     

Predicting the equity market with option-implied variables

We comprehensively analyze the predictive power of several option-implied variables for monthly S&P 500 excess returns and realized variance. The correlation risk premium (CRP) and the variance risk premium (VRP) emerge as strong predictors of both excess returns and realized variance. This is true both in- and out-of-sample. Our results also reveal that statistical evidence of predictability does not necessarily lead to economic gains. However, a timing strategy based on the CRP leads to utility gains of more than 5.03% per annum. Forecast combinations provide stable forecasts for both excess returns and realized variance, and add economic value.     

A multivariate distance nonlinear causality test based on partial distance correlation: a machine learning application to energy futures

This paper proposes a multivariate distance nonlinear causality test (MDNC) using the partial distance correlation in a time series framework. Partial distance correlation as an extension of the Brownian distance correlation calculates the distance correlation between random vectors X and Y controlling for a random vector Z. Our test can detect nonlinear lagged relationships between time series, and when integrated with machine learning methods it can improve the forecasting power. We apply our method as a feature selection procedure and combine it with the support vector machine and random forests algorithms to study the forecast of the main energy financial time series (oil, coal, and natural gas futures). It shows substantial improvement in forecasting the fuel energy time series in comparison to the classical Granger causality method in time series.     

Firm age and performance

Amid increasing interest in firm age and its effects on firm performance, this special issue offers an exhaustive review of the literature and a novel collection of evidence on the effects of firm age on performance, including a special focus of interest on innovation performance, financial performance, exports, survival and growth. This editorial positions the theme in the extant literature, and provides key definitions and challenges ahead in the field of evolutionary economics. It introduces the collection of articles composing the special issue. The papers offer a diversity of country contexts, as well as analytical approaches and methods. They include an exhaustive review of the literature on age and firms’ performance, and present original empirical studies focusing on the effects of age on firms’ economic outcomes on the one hand, and on innovation outcomes on the other hand. While most of the papers use econometric analysis, the level of analysis ranges from firm to individual.