A dynamic approach for aircraft assignment and maintenance scheduling by airlines

The problems of assigning planes to flights and of fleet maintenance operations scheduling are considered in this paper. While recent approaches make use of artificial intelligence techniques running on main frame computers to solve combinatorial optimization problems for nominal operations, a dynamic approach is proposed here to face on-line operation conditions. The proposed solution mixes a Dynamic Programming approach (to cope with the fleet assignment problem) and a heuristic technique (to solve the embedded maintenance schedule problem). When applied to a medium charter airline, this approach shows acceptability characteristics for operational staffs, while providing efficient solutions. The proposed solution scheme can be considered as the basis for the development of an on-line decision support system for fleet operations management within airlines.     

The Behaviour of the Real Exchange Rate: Evidence from an Alternative Price Index

Although the long–run purchasing power parity (PPP) hypothesis is expected to hold across tradable goods, all price indices available to researchers for testing the validity of PPP contain some proportion of non–tradable goods prices, which may generate substantial persistence in the real exchange rate. We construct time series for quarterly price indices that minimize the presence of non–tradable goods for six major economies. Applying recently developed nonlinear econometric techniques to the resulting five US dollar real exchange rate series for the recent floating exchange rate regime, we provide evidence that the nonlinear mean reverting properties of these real exchange rate series are stronger than the mean reverting properties of real exchange rate time series constructed using the consumer price index (CPI). In turn, these results have a natural economic interpretation.
(J.E.L.: F31).     

Inflation and the Income Share of the Rich: Evidence for 14 OECD Countries

This paper examines the distributional implications of inflation on top income shares in 14 advanced economies using data over the period 1920–2016. We use local projections to analyze how top income shares respond to an inflation shock, and panel regressions in which all variables are defined as 5-year averages to examine the impact of inflation on the position of the top-one-percent in the long run. Our findings suggest that inflation reduces the share of national income held by the top 1 percent. Furthermore, we find that inflation shocks and long-run inflation have similar effects on top income shares.     

The Impact of Inequality and Redistribution on Growth

In this paper, we reassess the impact of inequality on growth. The majority of previous papers have employed (system) GMM estimation. However, recent simulation studies indicate that the problems of GMM when using non‐stationary data such as GDP have been grossly underestimated in applied research. Concerning predetermined regressors such as inequality, GMM is outperformed by a simple least‐squares dummy variable estimator. Additionally, new data have recently become available that not only double the sample size compared to most previous studies, but also address the substantial measurement issues that have plagued past research. Using these new data and an LSDV estimator, we provide an analysis that both accounts for the conditions where inequality is beneficial or detrimental to growth and distinguishes between market‐driven inequality and redistribution. We show that there are situations where market inequality affects growth positively while redistribution is simultaneously beneficial.     

Financial Development,Foreign Banks and Economic Growth in Africa

This study aims to examine and analyze the impact of financial development and foreign bank penetration on African economies. An empirical study for the period 1995–2015 is conducted using the system GMM estimator. Our empirical results indicate that foreign bank entry has a positive and significant impact on economic growth in the countries of North and Southern Africa, while in the other two regions considered in this study (West and Central Africa, East Africa) the impact is negative and rarely significant. In addition, our results show that the development of financial markets has a positive and significant effect on economic growth only in the Southern African region. The paper concludes that policymakers should focus on long‐term policies to strengthen the financial sector to truly meet the needs of African people.     

Two-step methods in VaR prediction and the importance of fat tails

This paper proposes a two-step methodology for Value-at-Risk prediction. The first step involves estimation of a GARCH model using quasi-maximum likelihood estimation and the second step uses model filtered returns with the skewed t distribution of Azzalini and Capitanio [J. R. Stat. Soc. B, 2003, 65, 367–389]. The predictive performance of this method is compared to the single-step joint estimation of the same data generating process, to the well-known GARCH-Evt model and to a comprehensive set of other market risk models. Backtesting results show that the proposed two-step method outperforms most benchmarks including the classical joint estimation method of same data generating process and it performs competitively with respect to the GARCH-Evt model. This paper recommends two robust models to risk managers of emerging market stock portfolios. Both models are estimated in two steps: the GJR-GARCH-Evt model and the two-step GARCH-St model proposed in this study.     

Exploring Factors that Influence Social Retail Investors’ Decisions: Evidence from Desjardins Fund

Most studies on the choices, motivations and behavior of investors consist of segmentations focused on socio-demographic characteristics such as age, income, education level, etc. Such approaches seem to simplify, even mutilate, reality by aggregating data about observable variables and considering investors as homogeneous groups. These perspectives are inspired by a scientific approach that consists of separating in order to better understand the observed phenomena. By considering individual as a “homo economicus”, that is to say, a rational and autonomous individual who makes decisions motivated by material gains, these studies fail to recognize all the complexity that shapes human behavior. This paper argues that to understand the behavior and choices of investors in regards to socially responsible investing (SRI), we must consider social investors as complex individuals. In addition, we should take into account influence that the institution may exercise throughout the role of advisors and SRI promotion strategies. Our research builds on a multidimensional approach that explores to what extent demographic, environmental, social and governance (ESG) issues, the trade-offs between financial return and social values, the attitudes and the role of the institution (throughout the role of the advisor and SRI promotion strategies) influence the decisions of individual social investors. Moreover, it adopts a more open approach by exploring the characteristics and behaviors of individual social investors in relation to those of conventional investors. Our research provides evidence from the Desjardins Fund. Qualitative and quantitative data gathered by Desjardins from online surveys are subjected to bivariate and multivariate analyses and are complimented by ten semi-structured interviews with managers, analysts, and advisors who provided further insight into SRI investment behavior and choice. The results show that while demographic characteristics still remain important in understanding the behavior and attitudes of social investors, it is their social values, ESG issues, financial return considerations and the role played by the institution in mediating investment decisions that are significantly associated with SRI portfolios. Our research highlights the complexity surrounding the phenomenon of SRI and has several implications both in terms of theory and practice.     

Connecting the STEM dots: measuring the effect of an integrated engineering design intervention

Recent publications have elevated the priority of increasing the integration of Science, Technology, Engineering, and Mathematics (STEM) content for K-12 education. The STEM education community must invest in the development of valid and reliable to scales to measure STEM content, knowledge fusion, and perceptions of the nature of STEM. This brief report discusses the development of an instrument to measure student perceptions of the interdependent nature of STEM content knowledge in the context of a complex classroom intervention implemented in five Colorado high schools (N = 275). Specifically, cross-functional science, technology, engineering, and mathematics teams of high school students were formed to complete engineering design problems. Exploratory (pretest) and confirmatory (posttest) factor analyses indicated that a newly adapted scale to measure student perceptions of the interdependent nature of STEM content knowledge had possessed adequate model fit. Furthermore, analysis revealed a novel pattern of results for the intervention. Specifically, students with initially high perceptions of the interdependent nature of STEM sustained their high perceptions at posttest; however, students with initially low perceptions exhibited statistically significantly positive gains from pretest to posttest. Therefore, this intervention may work best with students who are at risk of losing interest in STEM disciplines. The implications of these research findings are discussed.     

CASH SUBADDITIVE RISK MEASURES AND INTEREST RATE AMBIGUITY

A new class of risk measures called cash subadditive risk measures is introduced to assess the risk of future financial, nonfinancial, and insurance positions. The debated cash additive axiom is relaxed into the cash subadditive axiom to preserve the original difference between the numéraire of the current reserve amounts and future positions. Consequently, cash subadditive risk measures can model stochastic and/or ambiguous interest rates or defaultable contingent claims. Practical examples are presented, and in such contexts cash additive risk measures cannot be used. Several representations of the cash subadditive risk measures are provided. The new risk measures are characterized by penalty functions defined on a set of sublinear probability measures and can be represented using penalty functions associated with cash additive risk measures defined on some extended spaces. The issue of the optimal risk transfer is studied in the new framework using inf-convolution techniques. Examples of dynamic cash subadditive risk measures are provided via BSDEs where the generator can locally depend on the level of the cash subadditive risk measure.