Lógica en la toma de decisiones de inversión en el sector de extracción de petróleo y gas natural

Este documento se centra en la presentación de información y análisis de la misma a la hora de establecer la manera en que empresas del sector de extracción de gas natural y generación de energía a base de dicho recurso, toman decisiones en cuanto a inversión, centrándose en la lógica que usan a la hora de emprender este proceso. Esto debido a la constante necesidad de establecer procesos que permitan tomar decisiones más acertadas, incluyendo todas las herramientas posibles para tal fin. La lógica es una de estas herramientas, pues permite encadenar factores con el fin de obtener resultados positivos. Por tal razón, se hace importante conocer el uso de esta herramienta, teniendo en cuentas de qué manera y en que contextos es usada. Con el fin de tener una mayor orientación, este estudio estará centrado en un sector específico, el cual es el de la extracción de petróleo y gas natural. Lo anterior entendiendo la necesidad existente de fundamentación teórica que permita establecer de manera clara la forma apropiada de tomar decisiones en un sector tan diverso y complejo como lo es el mencionado. El contexto empresarial actual exige una visión global, no basada en la lógica lineal causal que hoy se tiene como referencia. El sector de extracción de petróleo y gas natural es un ejemplo particular en cuanto a la manera en cuanto se toman decisiones en inversión, puesto que en su mayoría son empresas de capital intensivo, las cuales mantienen un flujo elevado de recursos monetarios.

Integration and segmentation in European investment services markets: assessing the implications for international real estate investment

This paper analyses developments in the growth and configuration of the institutional savings markets within the European Union. The paper discusses the changing socio-economic context in which investment services within the EU are being delivered. The is followed by an examination of drivers of market integration such as the growth and consolidation of the fund management industry, the demographic and fiscal pressures for reform of pensions markets and the process and effects of the deregulation of investment services markets. There is a review of outstanding sources of market segmentation. The projections for future growth in pensions are outlined and implications for real estate investment assessed. It is concluded that, although numerous imponderables render reliable quantitative projections problematic, growth and restructuring of the institutional savings market is likely to increase cross-border capital flows to real estate markets.

Commercial real estate return distributions: a review of literature and empirical evidence

This paper review the literature on the distribution of commercial real estate returns. There is growing evidence that the assumption of normality in returns is not safe. Distributions are found to be peaked, fat-tailed and, tentatively, skewed. There is some evidence of compound distributions and non-linearity. Public traded real estate assets (such as property company or REIT shares) behave in a fashion more similar to other common stocks. However, as in equity markets, it would be unwise to assume normality uncritically. Empirical evidence for UK real estate markets is obtained by applying distribution fitting routines to IPD Monthly Index data for the aggregate index and selected sub-sectors. It is clear that normality is rejected in most cases. It is often argued that observed differences in real estate returns are a measurement issue resulting from appraiser behaviour. However, unsmoothing the series does not assist in modelling returns. A large proportion of returns are close to zero. This would be characteristic of a thinly-traded market where new information arrives infrequently. Analysis of quarterly data suggests that, over longer trading periods, return distributions may conform more closely to those found in other asset markets. These results have implications for the formulation and implementation of a multi-asset portfolio allocation strategy.

Nonlocal office investors: anchored by their markets and impaired by their distance

Nonlocal investors purchase and sell investment property in a distant metropolitan area. In this study, we identify capital value underperformance for nonlocal investors on both sides of the transaction, when they purchase and when they sell. The commercial real estate transactions data include a national sample of office property occurring in more than 100 U.S. markets. Using propensity-score matched sample to control for selection bias, we find that nonlocal investors overpay on the purchase by an estimated 13.8 % and sell at an estimated 7 % discount. These disadvantages relative to local investors expand with the geographic distance separating investor and asset. Nonlocal investors fundamentally overvalue similar assets sold to each other relative to assets transacted between locals, and are less patient as sellers. The positive bias in overpayment is directly tied to office rent differentials between the asset and investor markets.

Ensaios em econometria aplicada

Este tese é composta por quatro ensaios sobre aplicações econométricas em tópicos econômicos relevantes. Os estudos versam sobre consumo de bens não-duráveis e preços de imóveis, capital humano e crescimento econômico, demanda residencial de energia elétrica e, por fim, periodicidade de variáveis fiscais de Estados e Municípios brasileiros. No primeiro artigo, "Non-Durable Consumption and Real-Estate Prices in Brazil: Panel-Data Analysis at the State Level", é investigada a relação entre variação do preço de imóveis e variação no consumo de bens não-duráveis. Os dados coletados permitem a formação de um painel com sete estados brasileiros observados entre 2008- 2012. Os resultados são obtidos a partir da estimação de uma forma reduzida obtida em Campbell e Cocco (2007) que aproxima um modelo estrutural. As estimativas para o caso brasileiro são inferiores as de Campbell e Cocco (2007), que, por sua vez, utilizaram microdados britânicos. O segundo artigo, "Uma medida alternativa de capital humano para o estudo empírico do crescimento", propõe uma forma de mensuração do estoque de capital humano que reflita diretamente preços de mercado, através do valor presente do fluxo de renda real futura. Os impactos dessa medida alternativa são avaliados a partir da estimação da função de produção tradicional dos modelos de crescimento neoclássico. Os dados compõem um painel de 25 países observados entre 1970 e 2010. Um exercício de robustez é realizado para avaliar a estabilidade dos coeficientes estimados diante de variações em variáveis exógenas do modelo. Por sua vez, o terceiro artigo "Household Electricity Demand in Brazil: a microdata approach", parte de dados da Pesquisa de Orçamento Familiar (POF) para mensurar a elasticidade preço da demanda residencial brasileira por energia elétrica. O uso de microdados permite adotar abordagens que levem em consideração a seleção amostral. Seu efeito sobre a demanda de eletricidade é relevante, uma vez que esta demanda é derivada da demanda por estoque de bens duráveis. Nesse contexto, a escolha prévia do estoque de bens duráveis (e consequentemente, a escolha pela intensidade de energia desse estoque) condiciona a demanda por eletricidade dos domicílios. Finalmente, o quarto trabalho, "Interpolação de Variáveis Fiscais Brasileiras usando Representação de Espaço de Estados" procurou sanar o problema de baixa periodicidade da divulgação de séries fiscais de Estados e Municípios brasileiros. Através de técnica de interpolação baseada no Filtro de Kalman, as séries mensais não observadas são projetadas a partir de séries bimestrais parcialmente observadas e covariáveis mensais selecionadas.

Market coupling in the power markets

The thesis analyses the European Unions’ effort to create an integrated pan-European electricity market based on “market coupling” as the proposed allocation mechanism for interconnector transfer capacity. Thus, the thesis’ main focus is if market coupling leads to a price convergence in interlinked markets and how it affects the behavior of electricity price data. The applied research methods are a qualitative, structured literature review and a quantitative analysis of electricity price data. The quantitative analysis relies on descriptive statistics of absolute price differentials and on a Cointegration analysis according to Engle & Granger (1987)’s two step approach. Main findings are that implicit auction mechanisms such as market coupling are more efficient than explicit auctions. Especially the method of price coupling leads to a price convergence in involved markets, to social welfare gains and reduces market power of producers, as shown on the example of the TLC market coupling. The market coupling initiative between Germany and Denmark, on the other hand, is evaluated as less successful and illustrates the complexity and difficulties of implementing market coupling initiatives. The cointegration analysis shows that the time series were already before the coupling date cointegrated, but the statistical significance increased. The thesis suggests that market coupling leads to a price convergence of involved markets and thus functions as method to create a single, integrated European electricity market.

Using baseline methods to identify non-technical losses in the context of smart grids

Demand response has gained increasing importance in the context of competitive electricity markets and smart grid environments. In addition to the importance that has been given to the development of business models for integrating demand response, several methods have been developed to evaluate the consumers' performance after the participation in a demand response event. The present paper uses those performance evaluation methods, namely customer baseline load calculation methods, to determine the expected consumption in each period of the consumer historic data. In the cases in which there is a certain difference between the actual consumption and the estimated consumption, the consumer is identified as a potential cause of non-technical losses. A case study demonstrates the application of the proposed method to real consumption data. © 2013 IEEE.

VIRTOPSY--scientific documentation, reconstruction and animation in forensic: individual and real 3D data based geo-metric approach including optical body/object surface and radiological CT/MRI scanning.

Until today, most of the documentation of forensic relevant medical findings is limited to traditional 2D photography, 2D conventional radiographs, sketches and verbal description. There are still some limitations of the classic documentation in forensic science especially if a 3D documentation is necessary. The goal of this paper is to demonstrate new 3D real data based geo-metric technology approaches. This paper present approaches to a 3D geo-metric documentation of injuries on the body surface and internal injuries in the living and deceased cases. Using modern imaging methods such as photogrammetry, optical surface and radiological CT/MRI scanning in combination it could be demonstrated that a real, full 3D data based individual documentation of the body surface and internal structures is possible in a non-invasive and non-destructive manner. Using the data merging/fusing and animation possibilities, it is possible to answer reconstructive questions of the dynamic development of patterned injuries (morphologic imprints) and to evaluate the possibility, that they are matchable or linkable to suspected injury-causing instruments. For the first time, to our knowledge, the method of optical and radiological 3D scanning was used to document the forensic relevant injuries of human body in combination with vehicle damages. By this complementary documentation approach, individual forensic real data based analysis and animation were possible linking body injuries to vehicle deformations or damages. These data allow conclusions to be drawn for automobile accident research, optimization of vehicle safety (pedestrian and passenger) and for further development of crash dummies. Real 3D data based documentation opens a new horizon for scientific reconstruction and animation by bringing added value and a real quality improvement in forensic science.

Unit Commitment and Electricity Prices Forecasting for Market Strategy Preparation in Interconnected Systems

In this paper we present a solution for building a better strategy to take part in external electricity markets. For an optimal strategy development, both the internal system costs as well as the future values of the series of electricity prices in external markets need to be known. But in practice, the real problems that must be faced are that both future electricity prices and costs are unknown. Thus, the first ones must be modeled and forecasted and the costs must be calculated. Our methodology for building an optimal strategy consists of three steps: The first step is modeling and forecasting market prices in external systems. The second step is the cost calculation on internal system taking into account the expected prices in the first step. The third step is based on the results of the previous steps, and consists of preparing the bids for external markets. The main goal is to reduce consumers' costs unlike many others that are oriented to increase GenCo's profits.

Electricity market price spike forecast with data mining techniques

Electricity market price forecast is a changeling yet very important task for electricity market managers and participants. Due to the complexity and uncertainties in the power grid, electricity prices are highly volatile and normally carry with spikes. which may be (ens or even hundreds of times higher than the normal price. Such electricity spikes are very difficult to be predicted. So far. most of the research on electricity price forecast is based on the normal range electricity prices. This paper proposes a data mining based electricity price forecast framework, which can predict the normal price as well as the price spikes. The normal price can be, predicted by a previously proposed wavelet and neural network based forecast model, while the spikes are forecasted based on a data mining approach. This paper focuses on the spike prediction and explores the reasons for price spikes based on the measurement of a proposed composite supply-demand balance index (SDI) and relative demand index (RDI). These indices are able to reflect the relationship among electricity demand, electricity supply and electricity reserve capacity. The proposed model is based on a mining database including market clearing price, trading hour. electricity), demand, electricity supply and reserve. Bayesian classification and similarity searching techniques are used to mine the database to find out the internal relationships between electricity price spikes and these proposed. The mining results are used to form the price spike forecast model. This proposed model is able to generate forecasted price spike, level of spike and associated forecast confidence level. The model is tested with the Queensland electricity market data with promising results. Crown Copyright (C) 2004 Published by Elsevier B.V. All rights reserved.

A power system control scheme based on security visualisation in parameter space

Power system real time security assessment is one of the fundamental modules of the electricity markets. Typically, when a contingency occurs, it is required that security assessment and enhancement module shall be ready for action within about 20 minutes’ time to meet the real time requirement. The recent California black out again highlighted the importance of system security. This paper proposed an approach for power system security assessment and enhancement based on the information provided from the pre-defined system parameter space. The proposed scheme opens up an efficient way for real time security assessment and enhancement in a competitive electricity market for single contingency case

A gestão do risco associado à negociação de energia eléctrica através de contratos bilaterais e mercado spot

Num mercado de electricidade competitivo onde existe um ambiente de incerteza, as empresas de geração adoptam estratégias que visam a maximização do lucro, e a minimização do risco. Neste contexto, é de extrema importância para desenvolver uma estratégia adequada de gestão de risco ter em conta as diferentes opções de negociação de energia num mercado liberalizado, de forma a suportar a tomada de decisões na gestão de risco. O presente trabalho apresenta um modelo que avalia a melhor estratégia de um produtor de energia eléctrica que comercializa num mercado competitivo, onde existem dois mercados possíveis para a transacção de energia: o mercado organizado (bolsa) e o mercado de contratos bilaterais. O produtor tenta maximizar seus lucros e minimizar os riscos correspondentes, seleccionando o melhor equilíbrio entre os dois mercados possíveis (bolsa e bilateral). O mercado de contratos bilaterais visa gerir adequadamente os riscos inerentes à operação de mercados no curto prazo (mercado organizado) e dar o vendedor / comprador uma capacidade real de escolher o fornecedor com que quer negociar. O modelo apresentado neste trabalho faz uma caracterização explícita do risco no que diz respeito ao agente de mercado na questão da sua atitude face ao risco, medido pelo Value at Risk (VaR), descrito neste trabalho por Lucro-em-Risco (PAR). O preço e os factores de risco de volume são caracterizados por um valor médio e um desvio padrão, e são modelizados por distribuições normais. Os resultados numéricos são obtidos utilizando a simulação de Monte Carlo implementado em Matlab, e que é aplicado a um produtor que mantém uma carteira diversificada de tecnologias de geração, para um horizonte temporal de um ano. Esta dissertação está organizada da seguinte forma: o capítulo 1, 2 e 3 descrevem o estado-da-arte relacionado com a gestão de risco na comercialização de energia eléctrica. O capítulo 4 descreve o modelo desenvolvido e implementado, onde é também apresentado um estudo de caso com uma aplicação do modelo para avaliar o risco de negociação de um produtor. No capítulo 5 são apresentadas as principais conclusões.

Definition of MV Load Diagrams via Weighted Evidence Accumulation Clustering using Subsampling

A definition of medium voltage (MV) load diagrams was made, based on the data base knowledge discovery process. Clustering techniques were used as support for the agents of the electric power retail markets to obtain specific knowledge of their customers’ consumption habits. Each customer class resulting from the clustering operation is represented by its load diagram. The Two-step clustering algorithm and the WEACS approach based on evidence accumulation (EAC) were applied to an electricity consumption data from a utility client’s database in order to form the customer’s classes and to find a set of representative consumption patterns. The WEACS approach is a clustering ensemble combination approach that uses subsampling and that weights differently the partitions in the co-association matrix. As a complementary step to the WEACS approach, all the final data partitions produced by the different variations of the method are combined and the Ward Link algorithm is used to obtain the final data partition. Experiment results showed that WEACS approach led to better accuracy than many other clustering approaches. In this paper the WEACS approach separates better the customer’s population than Two-step clustering algorithm.

Long-term price range forecast applied to risk management using regression models

Long-term contractual decisions are the basis of an efficient risk management. However those types of decisions have to be supported with a robust price forecast methodology. This paper reports a different approach for long-term price forecast which tries to give answers to that need. Making use of regression models, the proposed methodology has as main objective to find the maximum and a minimum Market Clearing Price (MCP) for a specific programming period, and with a desired confidence level α. Due to the problem complexity, the meta-heuristic Particle Swarm Optimization (PSO) was used to find the best regression parameters and the results compared with the obtained by using a Genetic Algorithm (GA). To validate these models, results from realistic data are presented and discussed in detail.

MASGriP – a Multi-Agent Smart Grid Simulation Platform

The increasing number of players that operate in power systems leads to a more complex management. In this paper a new multi-agent platform is proposed, which simulates the real operation of power system players. MASGriP – A Multi-Agent Smart Grid Simulation Platform is presented. Several consumer and producer agents are implemented and simulated, considering real characteristics and different goals and actuation strategies. Aggregator entities, such as Virtual Power Players and Curtailment Service Providers are also included. The integration of MASGriP agents in MASCEM (Multi-Agent System for Competitive Electricity Markets) simulator allows the simulation of technical and economical activities of several players. An energy resources management architecture used in microgrids is also explained.