Risk sharing with Multilateral Financial Institutions in infrastructure and energy project financing : effect on capital relief for commercial banks

(Matsukawa and Habeck, 2007) analyse the main instruments for risk mitigation in infrastructure financing with Multilateral Financial Institutions (MFIs). Their review coincided with the global financial crisis of 2007-08, and is highly relevant in current times considering the sovereign debt crisis, the lack of available capital and the increases in bank regulation in Western economies. The current macroeconomic environment has seen a slowdown in the level of finance for infrastructure projects, as they pose a higher credit risk given their requirements for long term investments. The rationale for this work is to look for innovative solutions that are focused on the credit risk mitigation of infrastructure and energy projects whilst optimizing the economic capital allocation for commercial banks. This objective is achieved through risk-sharing with MFIs and looking for capital relief in project finance transactions. This research finds out the answer to the main question: "What is the impact of risk-sharing with MFIs on project finance transactions to increase their efficiency and viability?", and is developed from the perspective of a commercial bank assessing the economic capital used and analysing the relevant variables for it: Probability of Default, Loss Given Default and Recovery Rates, (Altman, 2010). An overview of project finance for the infrastructure and energy sectors in terms of the volume of transactions worldwide is outlined, along with a summary of risk-sharing financing with MFIs. A review of the current regulatory framework beneath risk-sharing in structured finance with MFIs is also analysed. From here, the impact of risk-sharing and the diversification effect in infrastructure and energy projects is assessed, from the perspective of economic capital allocation for a commercial bank. CreditMetrics (J. P. Morgan, 1997) is applied over an existing well diversified portfolio of project finance infrastructure and energy investments, working with the main risk capital measures: economic capital, RAROC, and EVA. The conclusions of this research show that economic capital allocation on a portfolio of project finance along with risk-sharing with MFIs have a huge impact on capital relief whilst increasing performance profitability for commercial banks. There is an outstanding diversification effect due to the portfolio, which is combined with risk mitigation and an improvement in recovery rates through Partial Credit Guarantees issued by MFIs. A stress test scenario analysis is applied to the current assumptions and credit risk model, considering a downgrade in the rating for the commercial bank (lender) and an increase of default in emerging countries, presenting a direct impact on economic capital, through an increase in expected loss and a decrease in performance profitability. Getting capital relief through risk-sharing makes it more viable for commercial banks to finance infrastructure and energy projects, with the beneficial effect of a direct impact of these investments on GDP growth and employment. The main contribution of this work is to promote a strategic economic capital allocation in infrastructure and energy financing through innovative risk-sharing with MFIs and economic pricing to create economic value added for banks, and to allow the financing of more infrastructure and energy projects. This work suggests several topics for further research in relation to issues analysed. (Matsukawa and Habeck, 2007) analizan los principales instrumentos de mitigación de riesgos en las Instituciones Financieras Multilaterales (IFMs) para la financiación de infraestructuras. Su presentación coincidió con el inicio de la crisis financiera en Agosto de 2007, y sus consecuencias persisten en la actualidad, destacando la deuda soberana en economías desarrolladas y los problemas capitalización de los bancos. Este entorno macroeconómico ha ralentizado la financiación de proyectos de infraestructuras. El actual trabajo de investigación tiene su motivación en la búsqueda de soluciones para la financiación de proyectos de infraestructuras y de energía, mitigando los riesgos inherentes, con el objeto de reducir el consumo de capital económico en los bancos financiadores. Este objetivo se alcanza compartiendo el riesgo de la financiación con IFMs, a través de estructuras de risk-sharing. La investigación responde la pregunta: "Cuál es el impacto de risk-sharing con IFMs, en la financiación de proyectos para aumentar su eficiencia y viabilidad?". El trabajo se desarrolla desde el enfoque de un banco comercial, estimando el consumo de capital económico en la financiación de proyectos y analizando las principales variables del riesgo de crédito, Probability of Default, Loss Given Default and Recovery Rates, (Altman, 2010). La investigación presenta las cifras globales de Project Finance en los sectores de infraestructuras y de energía, y analiza el marco regulatorio internacional en relación al consumo de capital económico en la financiación de proyectos en los que participan IFMs. A continuación, el trabajo modeliza una cartera real, bien diversificada, de Project Finance de infraestructuras y de energía, aplicando la metodología CreditMet- rics (J. P. Morgan, 1997). Su objeto es estimar el consumo de capital económico y la rentabilidad de la cartera de proyectos a través del RAROC y EVA. La modelización permite estimar el efecto diversificación y la liberación de capital económico consecuencia del risk-sharing. Los resultados muestran el enorme impacto del efecto diversificación de la cartera, así como de las garantías parciales de las IFMs que mitigan riesgos, mejoran el recovery rate de los proyectos y reducen el consumo de capital económico para el banco comercial, mientras aumentan la rentabilidad, RAROC, y crean valor económico, EVA. En escenarios económicos de inestabilidad, empeoramiento del rating de los bancos, aumentos de default en los proyectos y de correlación en las carteras, hay un impacto directo en el capital económico y en la pérdida de rentabilidad. La liberación de capital económico, como se plantea en la presente investigación, permitirá financiar más proyectos de infraestructuras y de energía, lo que repercutirá en un mayor crecimiento económico y creación de empleo. La principal contribución de este trabajo es promover la gestión activa del capital económico en la financiación de infraestructuras y de proyectos energéticos, a través de estructuras innovadoras de risk-sharing con IFMs y de creación de valor económico en los bancos comerciales, lo que mejoraría su eficiencia y capitalización. La aportación metodológica del trabajo se convierte por su originalidad en una contribución, que sugiere y facilita nuevas líneas de investigación académica en las principales variables del riesgo de crédito que afectan al capital económico en la financiación de proyectos.

B-LOG: A branch and bound methodology for the parallel execution of logic programs

We propose a computational methodology -"B-LOG"-, which offers the potential for an effective implementation of Logic Programming in a parallel computer. We also propose a weighting scheme to guide the search process through the graph and we apply the concepts of parallel "branch and bound" algorithms in order to perform a "best-first" search using an information theoretic bound. The concept of "session" is used to speed up the search process in a succession of similar queries. Within a session, we strongly modify the bounds in a local database, while bounds kept in a global database are weakly modified to provide a better initial condition for other sessions. We also propose an implementation scheme based on a database machine using "semantic paging", and the "B-LOG processor" based on a scoreboard driven controller.

Infrastructure Finance in Developing Countries by State and Multilateral Development Banks

Infrastructure concession is an alternative widely used by governments to increase investment. In the case of the road sector, the main characteristics of the concessions are: long-term projects, high investments in the early years of the contract and high risks. A viability analysis must be carried out for each concession and consider the characteristics of the project. When the infrastructure is located in a developing country, political and market growth uncertainties should be add in the concession project analysis, as well as economic instability, because they present greater risks. This paper is an analysis of state bank participation in road infrastructure finance in developing countries. For this purpose, we studied road infrastructure financing and its associated risks, and also the features of developing countries. Furthermore, we considered the issue of state banks and multilateral development banks that perform an important role by offering better credit lines than the private banks, in terms of cost, interest and grace period. Based on this study, we analyzed the Brazilian Development Bank - BNDES – and their credit supply to road infrastructure concessions. The results show that BNDES is the main financing agent for long-term investment in the sector, offering loans with low interest rates in Brazilian currency. From this research we argue that a single state bank should not alone support the increasing demand for finance in Brazil. Therefore, we conclude that there is a need to expand the supply of credit in Brazil, by strengthening private banks in the long-term lending market.

Comparing the effectiveness of equivalence partitioning, branch testing and code reading by stepwise abstraction applied by subjects

Some verification and validation techniques have been evaluated both theoretically and empirically. Most empirical studies have been conducted without subjects, passing over any effect testers have when they apply the techniques. We have run an experiment with students to evaluate the effectiveness of three verification and validation techniques (equivalence partitioning, branch testing and code reading by stepwise abstraction). We have studied how well able the techniques are to reveal defects in three programs. We have replicated the experiment eight times at different sites. Our results show that equivalence partitioning and branch testing are equally effective and better than code reading by stepwise abstraction. The effectiveness of code reading by stepwise abstraction varies significantly from program to program. Finally, we have identified project contextual variables that should be considered when applying any verification and validation technique or to choose one particular technique.

Plataforma de evaluación de algoritmos para la identificación de usuarios

La minería de datos es un campo de las ciencias de la computación referido al proceso que intenta descubrir patrones en grandes volúmenes de datos. La minería de datos busca generar información similar a la que podría producir un experto humano. Además es el proceso de descubrir conocimientos interesantes, como patrones, asociaciones, cambios, anomalías y estructuras significativas a partir de grandes cantidades de datos almacenadas en bases de datos, data warehouses o cualquier otro medio de almacenamiento de información. El aprendizaje automático o aprendizaje de máquinas es una rama de la Inteligencia artificial cuyo objetivo es desarrollar técnicas que permitan a las computadoras aprender. De forma más concreta, se trata de crear programas capaces de generalizar comportamientos a partir de una información no estructurada suministrada en forma de ejemplos. La minería de datos utiliza métodos de aprendizaje automático para descubrir y enumerar patrones presentes en los datos. En los últimos años se han aplicado las técnicas de clasificación y aprendizaje automático en un número elevado de ámbitos como el sanitario, comercial o de seguridad. Un ejemplo muy actual es la detección de comportamientos y transacciones fraudulentas en bancos. Una aplicación de interés es el uso de las técnicas desarrolladas para la detección de comportamientos fraudulentos en la identificación de usuarios existentes en el interior de entornos inteligentes sin necesidad de realizar un proceso de autenticación. Para comprobar que estas técnicas son efectivas durante la fase de análisis de una determinada solución, es necesario crear una plataforma que de soporte al desarrollo, validación y evaluación de algoritmos de aprendizaje y clasificación en los entornos de aplicación bajo estudio. El proyecto planteado está definido para la creación de una plataforma que permita evaluar algoritmos de aprendizaje automático como mecanismos de identificación en espacios inteligentes. Se estudiarán tanto los algoritmos propios de este tipo de técnicas como las plataformas actuales existentes para definir un conjunto de requisitos específicos de la plataforma a desarrollar. Tras el análisis se desarrollará parcialmente la plataforma. Tras el desarrollo se validará con pruebas de concepto y finalmente se verificará en un entorno de investigación a definir. ABSTRACT. The data mining is a field of the sciences of the computation referred to the process that it tries to discover patterns in big volumes of information. The data mining seeks to generate information similar to the one that a human expert might produce. In addition it is the process of discovering interesting knowledge, as patterns, associations, changes, abnormalities and significant structures from big quantities of information stored in databases, data warehouses or any other way of storage of information. The machine learning is a branch of the artificial Intelligence which aim is to develop technologies that they allow the computers to learn. More specifically, it is a question of creating programs capable of generalizing behaviors from not structured information supplied in the form of examples. The data mining uses methods of machine learning to discover and to enumerate present patterns in the information. In the last years there have been applied classification and machine learning techniques in a high number of areas such as healthcare, commercial or security. A very current example is the detection of behaviors and fraudulent transactions in banks. An application of interest is the use of the techniques developed for the detection of fraudulent behaviors in the identification of existing Users inside intelligent environments without need to realize a process of authentication. To verify these techniques are effective during the phase of analysis of a certain solution, it is necessary to create a platform that support the development, validation and evaluation of algorithms of learning and classification in the environments of application under study. The project proposed is defined for the creation of a platform that allows evaluating algorithms of machine learning as mechanisms of identification in intelligent spaces. There will be studied both the own algorithms of this type of technologies and the current existing platforms to define a set of specific requirements of the platform to develop. After the analysis the platform will develop partially. After the development it will be validated by prove of concept and finally verified in an environment of investigation that would be define.

The chair of food Banks UPM as a tool of raising awareness and promoting a culture of rational food comsumption

The Chair of Food Banks UPM arises from a cooperation agreement between the Spanish Federation of Food Banks (FESBAL) and the Technical University of Madrid (UPM), with the aim of raising awareness and promoting rational food consumption to avoid food waste, through activities of training, transfer of knowledge and promotion of I+D+i. The aim of this paper is to reflect on the activities carried out during the first year in order to obtain learning lessons and improve the management of activities and resources.