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This paper aims to analyse the volatility of the fixed income market from 11 countries (Brazil, Russia, India, China, South Africa, Argentina, Chile, Mexico, USA, Germany and Japan) from January 2000 to December 2011 by examining the interbank interest rates from each market.

To the volatility of interest rates returns, the study used models of auto-regressive conditional heteroscedasticity, autoregressive conditional heteroscedasticity (ARCH), generalized autoregressive conditional heteroscedasticity (GARCH), exponential generalized autoregressive conditional heteroscedasticity (EGARCH), threshold generalized autoregressive conditional heteroscedasticity (TGARCH) and periodic generalized autoregressive conditional heteroscedasticity (PGARCH), and a combination of these with autoregressive integrated moving average (ARIMA) models, checking which of these processes were more efficient in capturing volatility of interest rates of each of the sample countries.

The results suggest that for most markets, studied volatility is best modelled by asymmetric GARCH processes – in this case the EGARCH – demonstrating that bad news leads to a higher increase in the volatility of these markets than good news. In addition, the causes of increased volatility seem to be more associated with events occurring internally in each country, as changes in macroeconomic policies, than the overall external events.

It is expected that this study has contributed to a better understanding of the volatility of interest rates and the main factors affecting this market.

Este estudio analiza la volatilidad del mercado de renta fija de once países (Brasil, Rusia, India, China, Sudáfrica, Argentina, Chile, México, Estados Unidos, Alemania y Japón) de enero de 2000 a diciembre de 2011, mediante el examen de las tasas de interés interbancarias de cada mercado.

Para la volatilidad de los retornos de las tasas de interés, se utilizaron modelos de heteroscedasticidad condicional autorregresiva: ARCH, GARCH, EGARCH, TGARCH y PGARCH, y una combinación de estos con modelos ARIMA, comprobando cuáles de los procesos eran más eficientes para capturar la volatilidad de interés de cada uno de los países de la muestra.

Los resultados sugieren que para la mayoría de los mercados estudiados la volatilidad es mejor modelada por procesos GARCH asimétricos —en este caso el EGARCH— demostrando que las malas noticias conducen a un mayor incremento en la volatilidad de estos mercados que las buenas noticias. Además, las causas de una mayor volatilidad parecen estar más asociadas a eventos que ocurren internamente en cada país, como cambios en las políticas macroeconómicas, que los eventos externos generales.

Se espera que este estudio contribuya a un mejor entendimiento de la volatilidad de las tasas de interés y de los principales factores que afectan a este mercado.

Ingreso fijo, Volatilidad, Países emergentes, Modelos ARCH-GARCH

Artículo de investigación

The purpose of this paper is to investigate how certain characteristics of the university–industry–government collaboration facilitate knowledge creation and management, hence innovation focusing on particularities of the Brazilian scenario.

As a conceptual basis, there are correlations between theories of knowledge management and the Triple Helix, a model referenced to university–industry–government cooperation. The research was conducted through a multiple case study at two National Institutes of Science and Technology (INCTs in Portuguese).

The main results show the importance of participation in the INCT program, as it enables the creation of an organizational structure with the coordinator’s leadership, who directs the flow of knowledge among organizations and stimulates innovation.

The choice of the topic is justified by the lack of studies on the identification and analyses of the main aspects of this type of collaboration in an integrated way.

Effective planning requires the participation of different functions and may be hampered by lack of integration and information quality (IQ). This paper aims to investigate the relationships among integration, uncertainty, IQ and performance, in the context of the production planning and control function. The literature lacks in-depth studies that consider these factors altogether, showing how they interact and how they contribute to improve business performance.

The authors introduce the variable of planning performance, which represents the quality of the production plans/planning process and is related to the frequency and causes of modifications to these plans. The relationships among the mentioned constructs are investigated by means of multiple case studies.

The results illustrate that integration is positively related to planning performance, and this relationship is mediated by IQ and moderated by uncertainty.

The presented analysis may help practitioners to foster interfunctional integration, better cope with uncertainty and improve information management, aiming to achieve better planning performance. The managers can choose integration and IQ improvement mechanisms that better fit to their environment/reality, using the four different cases as a benchmark. Moreover, this research contributes to the literature exploring this contingency perspective by means of in-depth case studies, considering that most of the existing research adopting this perspective is survey-based.

This paper aims to present a literature review of the most recent optimisation methods applied to Credit Scoring Models (CSMs).

The research methodology employed technical procedures based on bibliographic and exploratory analyses. A traditional investigation was carried out using the Scopus, ScienceDirect and Web of Science databases. The papers selection and classification took place in three steps considering only studies in English language and published in electronic journals (from 2008 to 2022). The investigation led up to the selection of 46 publications (10 presenting literature reviews and 36 proposing CSMs).

The findings showed that CSMs are usually formulated using Financial Analysis, Machine Learning, Statistical Techniques, Operational Research and Data Mining Algorithms. The main databases used by the researchers were banks and the University of California, Irvine. The analyses identified 48 methods used by CSMs, the main ones being: Logistic Regression (13%), Naive Bayes (10%) and Artificial Neural Networks (7%). The authors conclude that advances in credit score studies will require new hybrid approaches capable of integrating Big Data and Deep Learning algorithms into CSMs. These algorithms should have practical issues considered consider practical issues for improving the level of adaptation and performance demanded for the CSMs.

The results of this study might provide considerable practical implications for the application of CSMs. As it was aimed to demonstrate the application of optimisation methods, it is highly considerable that legal and ethical issues should be better adapted to CSMs. It is also suggested improvement of studies focused on micro and small companies for sales in instalment plans and commercial credit through the improvement or new CSMs.

The economic reality surrounding credit granting has made risk management a complex decision-making issue increasingly supported by CSMs. Therefore, this paper satisfies an important gap in the literature to present an analysis of recent advances in optimisation methods applied to CSMs. The main contribution of this paper consists of presenting the evolution of the state of the art and future trends in studies aimed at proposing better CSMs.

This paper aims to exemplify the use of project management tools in the scheduling of aircraft maintenance activities. This process is known as maintenance, repair and overhaul and it has gained importance within the aeronautical sector due to its expected growth in the coming years; however, it also faces increasing competitiveness in its market. This fact gives rise to the need of acting in maintenance management and seeking lower costs while maintaining the quality of the service provided. The purpose of this paper is to propose the structuring of a procedure that aims to reduce the total maintenance time (downtime) and guarantee the delivery of the aircraft on time.

The paper, through a case study at a Brazilian aircraft maintenance center, used critical path method and critical chain project management, the latter being derived from the theory of constraints, with the purpose of analyzing resources systematically and synchronizing the activities in the precedence network.

As a result, it is shown that downtime can be reduced from 11 to 5 days and improvements are proposed to achieve greater market competitiveness.

This paper demonstrates the competitive advantage that resulted from the application of project management tools in the aircraft maintenance planning and execution.

The purpose of this paper is to determine the factors for an academic research project in electrical engineering to become relevant.

As a conceptual basis, a few theories of entrepreneurial university and triple helix were correlated, seeking to determine the main critical factors and the successful criteria of an academic research project. The research was conducted through four cases of electric engineering that succeed in generating social and economic impact.

When analyzing the available bibliography, it is clear that the connection among the companies, the market and the research that happens at the university is very important. Not only that, but also, according to the results, this is the key to generating revenue and impact on society. In addition, operational policies and competent leaders promoting this impact inside the universities are extremely important.

The topic was selected because of the reduced number of articles related to the identification and analyses of the main aspects that make an academic research project relevant to the society. Furthermore, the paper is significant because it analyzes the main factors that help develop a better society and country through academic research.