Pricing and competition in the Swedish retail market for electricity

Sweden, together with Norway, Finland and Denmark, have created a multi-national electricity market called NordPool. In this market, producers and retailers of electricity can buy and sell electricity, and the retailers then offers this electricity to end consumers such as households and industries. Previous studies have shown that pricing at the NordPool market is functioning quite well, but no other study has to my knowledge studied if pricing in the retail market to consumers in Sweden is well functioning. If the market is well functioning, with competition and low transaction costs when changing electricity retailer, we would expect that a homogeneous good such as electricity would be sold at the approximately same price, and that price changes would be highly correlated, in this market. Thus, the aim of this study is to test whether the price of Vattenfall, the largest energy firm in the Swedish market, is highly correlated to the price of other firms in the Swedish retail market for electricity. Descriptive statistics indicate that the price offered by Vattenfall is quite similar to the price of other firms in the market. In addition, regression analysis show that the correlation between the price of Vattenfall and other firms is as high as 0.98.

Modelos condicionais de apreçamento de ativos com prêmios de risco macroeconômicos variantes no tempo

Analisamos a previsibilidade dos retornos mensais de ativos no mercado brasileiro em um período de 10 anos desde o início do plano Real. Para analisarmos a variação cross-section dos retornos e explicarmos estes retornos em função de prêmios de risco variantes no tempo, condicionados a variáveis de estado macroeconômicas, utilizamos um novo modelo de apreçamento de ativos, combinando dois diferentes tipos de modelos econômicos, um modelo de finanças - condicional e multifatorial, e um modelo estritamente macroeconômico do tipo Vector Auto Regressive. Verificamos que o modelo com betas condicionais não explica adequadamente os retornos dos ativos, porém o modelo com os prêmios de risco (e não os betas) condicionais, produz resultados com interpretação econômica e estatisticamente satis fatórios

O Impacto do Risco Inflacionário sobre o Juros no Brasil

Esta dissertação analisa a conexão existente entre o mercado de dívida pública e a política monetária no Brasil. Com base em um Vetor Auto-Regressivo (VAR), foram utilizadas duas proxies alternativas de risco inflacionário para mostrar que choques positivos no risco inflacionário elevam tanto as expectativas de inflação do mercado quanto os juros futuros do Swap Pré x DI. Em seguida, com base em modelo de inconsistência dinâmica de Blanchard e Missale (1994) e utilizando a metodologia de Johansen, constatou-se que um aumento nos juros futuros diminui a maturidade da dívida pública, no longo prazo. Os resultados levam a duas conclusões: o risco inflacionário 1) dificulta a colocação de títulos nominais (não-indexados) no mercado pelo governo, gerando um perfil de dívida menos longo do que o ideal e 2) torna a política monetária mais custosa.

Determinantes do nível de caixa das empresas: análise de amostra de países da América Latina

Neste estudo são analisados, através de técnicas de dados em painel, os fatores determinantes dos níveis de ativos líquidos de empresas abertas do Brasil, Argentina, Chile, México e Peru no período de 1995 a 2009. O índice utilizado nas modelagens é denominado de ativo líquido (ou simplesmente caixa), o qual inclui os recursos disponíveis em caixa e as aplicações financeiras de curto prazo, divididos pelo total de ativos da firma. É possível identificar uma tendência crescente de acúmulo de ativos líquidos como proporção do total de ativos ao longo dos anos em praticamente todos os países. São encontradas evidências de que empresas com maiores oportunidades de crescimento, maior tamanho (medido pelo total de ativos), maior nível de pagamento de dividendos e maior nível de lucratividade, acumulam mais caixa na maior parte dos países analisados. Da mesma forma, empresas com maiores níveis de investimento em ativo imobilizado, maior geração de caixa, maior volatilidade do fluxo de caixa, maior alavancagem e maior nível de capital de giro, apresentam menor nível de acúmulo de ativos líquidos. São identificadas semelhanças de fatores determinantes de liquidez em relação a estudos empíricos com empresas de países desenvolvidos, bem como diferenças devido a fenômenos particulares de países emergentes, como por exemplo elevadas taxas de juros internas, diferentes graus de acessibilidade ao mercado de crédito internacional e a linhas de crédito de agências de fomento, equity kicking, entre outros. Em teste para a base de dados das maiores firmas do Brasil, é identificada a presença de níveis-alvo de caixa através de modelo auto-regressivo de primeira ordem (AR1). Variáveis presentes em estudos mais recentes com empresas de países desenvolvidos como aquisições, abertura recente de capital e nível de governança corporativa também são testadas para a base de dados do Brasil.

O impacto do risco inflacionário sobre os juros no Brasil

Esta dissertação analisa a conexão existente entre o mercado de dívida pública e a política monetária no Brasil. Com base em um Vetor Auto-Regressivo (VAR), foram utilizadas duas proxies alternativas de risco inflacionário para mostrar que choques positivos no risco inflacionário elevam tanto as expectativas de inflação do mercado quanto os juros futuros do Swap Pré x DI. Em seguida, com base em modelo de inconsistência dinâmica de Blanchard e Missale (1994) e utilizando a metodologia de Johansen, constatou-se que um aumento nos juros futuros diminui a maturidade da dívida pública, no longo prazo. Os resultados levam a duas conclusões: o risco inflacionário 1) dificulta a colocação de títulos nominais (não-indexados) no mercado pelo governo, gerando um perfil de dívida menos longo do que o ideal e 2) torna a política monetária mais custosa.

Uncertainty of aid inflows and the growth relationship

This paper contributes to the literature on aid and economic growth. We posit that it is not the levei of aid flows per se but the stability of such flows that determines the impact of aid on economic growth. Three measures of aid instability are employed. One is a simple deviation from trend, and measures overall instability. The other measures are based on auto-regressive estimates to capture deviations from an expected trend. These measures are intended to proxy for uncertainty in aid receipts. We posit that such uncertainty will influence the relationship between aid and investment and how recipient governments respond to aid, and will therefore affect how aid impacts on growth. We estimate a standard cross-country growth regression including the leveI of aid, and find aid to be insignificant (in line with other results in the literature). We then introduce measures of instability. Aid remains insignificant when we account for overall instability. However, when we account for uncertainty (which is negative and significant), we find that aid has a significant positive effect on growth. We conduct stability tests that show that the significance of aid is largely due to its effect on the volume of investment. The finding that uncertainty of aid receipts reduces the effectiveness of aid is robust. When we control for this, aid appears to have a significant positive influence on growth. When the regression is estimated for the sub-sample of African countries these findings hold, although the effectiveness of aid appears weaker than for the full sample.

Forecast comparison with nonlinear methods for Brazilian industrial production

This work assesses the forecasts of three nonlinear methods | Markov Switching Autoregressive Model, Logistic Smooth Transition Auto-regressive Model, and Auto-metrics with Dummy Saturation | for the Brazilian monthly industrial production and tests if they are more accurate than those of naive predictors such as the autoregressive model of order p and the double di erencing device. The results show that the step dummy saturation and the logistic smooth transition autoregressive can be superior to the double di erencing device, but the linear autoregressive model is more accurate than all the other methods analyzed.

Structural damage detection by fuzzy clustering

The development of strategies for structural health monitoring (SHM) has become increasingly important because of the necessity of preventing undesirable damage. This paper describes an approach to this problem using vibration data. It involves a three-stage process: reduction of the time-series data using principle component analysis (PCA), the development of a data-based model using an auto-regressive moving average (ARMA) model using data from an undamaged structure, and the classification of whether or not the structure is damaged using a fuzzy clustering approach. The approach is applied to data from a benchmark structure from Los Alamos National Laboratory, USA. Two fuzzy clustering algorithms are compared: fuzzy c-means (FCM) and Gustafson-Kessel (GK) algorithms. It is shown that while both fuzzy clustering algorithms are effective, the GK algorithm marginally outperforms the FCM algorithm. (C) 2008 Elsevier Ltd. All rights reserved.

Structural Health Monitoring in Smart Structures Through Time Series Analysis

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Identificação e comparação entre controle preditivo com modelo não linear e PI sintonizados com PSO em sistema de separação gravitacional de águia-óleo

The separation methods are reduced applications as a result of the operational costs, the low output and the long time to separate the uids. But, these treatment methods are important because of the need for extraction of unwanted contaminants in the oil production. The water and the concentration of oil in water should be minimal (around 40 to 20 ppm) in order to take it to the sea. Because of the need of primary treatment, the objective of this project is to study and implement algorithms for identification of polynomial NARX (Nonlinear Auto-Regressive with Exogenous Input) models in closed loop, implement a structural identification, and compare strategies using PI control and updated on-line NARX predictive models on a combination of three-phase separator in series with three hydro cyclones batteries. The main goal of this project is to: obtain an optimized process of phase separation that will regulate the system, even in the presence of oil gushes; Show that it is possible to get optimized tunings for controllers analyzing the mesh as a whole, and evaluate and compare the strategies of PI and predictive control applied to the process. To accomplish these goals a simulator was used to represent the three phase separator and hydro cyclones. Algorithms were developed for system identification (NARX) using RLS(Recursive Least Square), along with methods for structure models detection. Predictive Control Algorithms were also implemented with NARX model updated on-line, and optimization algorithms using PSO (Particle Swarm Optimization). This project ends with a comparison of results obtained from the use of PI and predictive controllers (both with optimal state through the algorithm of cloud particles) in the simulated system. Thus, concluding that the performed optimizations make the system less sensitive to external perturbations and when optimized, the two controllers show similar results with the assessment of predictive control somewhat less sensitive to disturbances

Application of ARX neural networks to model the rate of penetration of petroleum wells drilling

Bit performance prediction has been a challenging problem for the petroleum industry. It is essential in cost reduction associated with well planning and drilling performance prediction, especially when rigs leasing rates tend to follow the projects-demand and barrel-price rises. A methodology to model and predict one of the drilling bit performance evaluator, the Rate of Penetration (ROP), is presented herein. As the parameters affecting the ROP are complex and their relationship not easily modeled, the application of a Neural Network is suggested. In the present work, a dynamic neural network, based on the Auto-Regressive with Extra Input Signals model, or ARX model, is used to approach the ROP modeling problem. The network was applied to a real oil offshore field data set, consisted of information from seven wells drilled with an equal-diameter bit.

Damage detection in a benchmark structure using AR-ARX models and statistical pattern recognition

Structural health monitoring (SHM) is related to the ability of monitoring the state and deciding the level of damage or deterioration within aerospace, civil and mechanical systems. In this sense, this paper deals with the application of a two-step auto-regressive and auto-regressive with exogenous inputs (AR-ARX) model for linear prediction of damage diagnosis in structural systems. This damage detection algorithm is based on the. monitoring of residual error as damage-sensitive indexes, obtained through vibration response measurements. In complex structures there are. many positions under observation and a large amount of data to be handed, making difficult the visualization of the signals. This paper also investigates data compression by using principal component analysis. In order to establish a threshold value, a fuzzy c-means clustering is taken to quantify the damage-sensitive index in an unsupervised learning mode. Tests are made in a benchmark problem, as proposed by IASC-ASCE with different damage patterns. The diagnosis that was obtained showed high correlation with the actual integrity state of the structure. Copyright © 2007 by ABCM.

Applying a genetic neuro-model reference adaptive controller in drilling optimization

Motivated by rising drilling operation costs, the oil industry has shown a trend toward real-time measurements and control. In this scenario, drilling control becomes a challenging problem for the industry, especially due to the difficulty associated with parameters modeling. One of the drillbit performance evaluators, the Rate Of Penetration (ROP), has been used as a drilling control parameter. However, relationships between operational variables affecting the ROP are complex and not easily modeled. This work presents a neuro-genetic adaptive controller to treat this problem. It is based on an auto-regressive with extra input signals, or ARX model and on a Genetic Algorithm (GA) to control the ROP. © [2006] IEEE.

A genetic neuro-model reference adaptive controller for petroleum wells drilling operations

Motivated by rising drilling operation costs, the oil industry has shown a trend towards real-time measurements and control. In this scenario, drilling control becomes a challenging problem for the industry, especially due to the difficulty associated to parameters modeling. One of the drill-bit performance evaluators, the Rate of Penetration (ROP), has been used in the literature as a drilling control parameter. However, the relationships between the operational variables affecting the ROP are complex and not easily modeled. This work presents a neuro-genetic adaptive controller to treat this problem. It is based on the Auto-Regressive with Extra Input Signals model, or ARX model, to accomplish the system identification and on a Genetic Algorithm (GA) to provide a robust control for the ROP. Results of simulations run over a real offshore oil field data, consisted of seven wells drilled with equal diameter bits, are provided. © 2006 IEEE.

Adaptive filter feature identification for structural health monitoring in aeronautical panel

This paper presents an approach for structural health monitoring (SHM) by using adaptive filters. The experimental signals from different structural conditions provided by piezoelectric actuators/sensors bonded in the test structure are modeled by a discrete-time recursive least square (RLS) filter. The biggest advantage to use a RLS filter is the clear possibility to perform an online SHM procedure since that the identification is also valid for non-stationary linear systems. An online damage-sensitive index feature is computed based on autoregressive (AR) portion of coefficients normalized by the square root of the sum of the square of them. The proposed method is then utilized in a laboratory test involving an aeronautical panel coupled with piezoelectric sensors/actuators (PZTs) in different positions. A hypothesis test employing the t-test is used to obtain the damage decision. The proposed algorithm was able to identify and localize the damages simulated in the structure. The results have shown the applicability and drawbacks the method and the paper concludes with suggestions to improve it. ©2010 Society for Experimental Mechanics Inc.