Building a Sustainable Bank: The Case of GTBank of Nigeria

GTBank of Nigeria has, in just 20 years, developed into one of the most profitable and sustainable financial institutions in Africa. The case identifies the choices that helped the bank emerge successful from several shocks to the Nigerian financial sector and has implications for other financial institutions trying to balance sustainability and profitability. On the basis of eight years of research on the bank, we illustrate six factors that have characterized its success: (1) effective leadership, (2) a focus on people rather than strategy, (3) adopting a simple and focused strategy, (4) a culture of sustainable banking, (5) confronting effectively the external environment, and (6) exploitation of technology to accelerate the business. GTBank’s story raises questions about what makes a company sustainable and analyzes the decisions that executives need to make, often in challenging external circumstances, in order to preserve the long‐term sustainability of an organization. © 2014 Wiley Periodicals, Inc.     

Theory and Practice of Contagion in Monetary Unions: Domino Effects in EMU Mediterranean Countries

This paper analyzes strategic interactions and contagion effects in the peripheral countries of a monetary union. Using game theory and cost-benefit analysis, the paper determines the set of equilibrium solutions under which country-specific shocks are transmitted to other member countries giving rise to contagion. Numerical simulations, obtained by a simple calibration of the model on some key Mediterranean countries of the Euro Zone, show the probabilities of contagion from Greece, Spain and Italy.     

A multiple linear regression model for imprecise information

In standard regression analysis the relationship between the (response) variable and a set of (explanatory) variables is investigated. In the classical framework the response is affected by probabilistic uncertainty (randomness) and, thus, treated as a random variable. However, the data can also be subjected to other kinds of uncertainty such as imprecision. A possible way to manage all of these uncertainties is represented by the concept of fuzzy random variable (FRV). The most common class of FRVs is the LR family (LR FRV), which allows us to express every FRV in terms of three random variables, namely, the center, the left spread and the right spread. In this work, limiting our attention to the LR FRV class, we consider the linear regression problem in the presence of one or more imprecise random elements. The procedure for estimating the model parameters and the determination coefficient are discussed and the hypothesis testing problem is addressed following a bootstrap approach. Furthermore, in order to illustrate how the proposed model works in practice, the results of a real-life example are given together with a comparison with those obtained by applying classical regression analysis.     

The roots of inequality: estimating inequality of opportunity from regression trees and forests*

We propose the use of machine learning methods to estimate inequality of opportunity and to illustrate that regression trees and forests represent a substantial improvement over existing approaches: they reduce the risk of ad hoc model selection and trade off upward and downward bias in inequality of opportunity estimates. The advantages of regression trees and forests are illustrated by an empirical application for a cross-section of 31 European countries. We show that arbitrary model selection might lead to significant biases in inequality of opportunity estimates relative to our preferred method. These biases are reflected in both point estimates and country rankings.     

Sustainable Power Generation in Europe: A Panel Data Analysis of the Effects of Market and Environmental Regulations

Environmental and Resource Economics - Climate change and greenhouse gas emissions have become increasingly more pressing environmental concerns in European policy agenda. Environmental energy...     

The European Monetary Policy Responses During the Pandemic Crisis

This paper uses an event-based analysis to describe how the European Central Bank’s (ECB’s) policy responses to the pandemic crisis have affected the European financial and economic system. The result of our exercise, which is based on the examination of the main measures taken by the ECB during 2020, is that these responses have positively affected the European economic system by improving banks’ lending activity and by indirectly creating room for expansionary fiscal policies in the euro area’s high-debt countries that do not have fiscal capacity.

     

Panel data and descriptor for energy econometrics – an efficiency,resilience and innovation analysis

Quality & Quantity - The work at hand presents a new extensive panel dataset for energy economics, econometrics and policy. The referred dataset is made of 5000 observations circa, including 6...     

Community structure in the World Trade Network based on communicability distances

In this paper, we investigate the mesoscale structure of the World Trade Network. In this framework, a specific role is assumed by short- and long-range interactions, and hence by any suitably defined network-based distance between countries. Therefore, we identify clusters through a new procedure that exploits Estrada communicability distance and the vibrational communicability distance, which turn out to be particularly suitable for catching the inner structure of the economic network. The proposed methodology aims at finding the distance threshold that maximizes a specific quality function defined for general metric spaces. Main advantages regard the computational efficiency of the procedure as well as the possibility to inspect intercluster and intracluster properties of the resulting communities. The numerical analysis highlights peculiar relationships between countries and provides a rich set of information that can hardly be achieved within alternative clustering approaches.

     

Local volatility under rough volatility

Several asymptotic results for the implied volatility generated by a rough volatility model have been obtained in recent years (notably in the small-maturity regime), providing a better understanding of the shapes of the volatility surface induced by rough volatility models, supporting their calibration power to SP500 option data. Rough volatility models also generate a local volatility surface, via the so-called Markovian projection of the stochastic volatility. We complement the existing results on implied volatility by studying the asymptotic behavior of the local volatility surface generated by a class of rough stochastic volatility models, encompassing the rough Bergomi model. Notably, we observe that the celebrated “1/2 skew rule” linking the short-term at-the-money skew of the implied volatility to the short-term at-the-money skew of the local volatility, a consequence of the celebrated “harmonic mean formula” of [Berestycki et al. (2002). Quantitative Finance, 2, 61–69], is replaced by a new rule: the ratio of the at-the-money implied and local volatility skews tends to the constant $1/(H+3/2)$ (as opposed to the constant 1/2), where H is the regularity index of the underlying instantaneous volatility process.     

The probability of multidimensional poverty: A new approach and an empirical application to EU-SILC data

This paper proposes a novel method to analyze multidimensional poverty by using a large set of feasible weights to summarize the information about the poor, which enables remaining agnostic about the relative importance given to different poverty dimensions. This method allows for the calculation of the individual probability of being poor in a multidimensional perspective. The distribution of individual probabilities can then be combined with Generalized Lorenz dominance techniques to derive unanimous consent for a wide class of social welfare functions with a minimum load of value judgments. The innovations proposed here allow to move from a dual definition of poverty, where poor and non-poor individuals are classified in a mutually exclusive context, to a continuous measure of deprivation capturing both the extensive and intensive margin of multidimensional poverty. The empirical application of the method consists of measuring multidimensional poverty in ten selected countries using four waves of EU-SILC data (2008–2014).