Oil price shocks and stock market behavior : empirical evidence for the U.S. and European Countries

The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file.Title from title screen of research.pdf file (viewed on September 28, 2007)Vita.Thesis (Ph. D.) University of Missouri-Columbia 2007.This dissertation analyze the relationship between oil price shocks and stock market for the US and 13 European countries with monthly data from 1986.1-2005.12. Three countries (Denmark, Norway and the UK) among 13 European countries are oil exporting countries. Unrestricted multivariate Vector Autoregression (VAR) with 4 variables (interest rates, real oil price changes, industrial production and real stock returns) is estimated as well as impulse response function and variance decomposition. With regard to impact of oil price shocks on the stock market, in most oil importing countries oil price shocks have significantly negative effect on the stock market in the same month or in one month, while among oil exporting countries only Norway shows a significantly positive response of real stock returns to oil price shocks. Comparing the impacts of oil price shocks and interest rate (monetary) shocks on the stock market, in most oil importing countries oil price shocks have a greater impact than interest rate shocks, except for a few countries where monetary policy responds systemically to oil price shocks by raising interest rates, which leads to a decline in real stock returns. Therefore, taking into account the response of monetary policy to oil price shocks, oil prices play a crucial role in the stock market of oil importing countries. On the contrary, in oil exporting countries oil price shocks have a smaller impact on the stock market than interest rate shocks, and monetary policy does not respond to the oil price shocks. According to the literature, oil price shocks have an asymmetric effect on economic activity and the stock market in that oil price increases have a greater impact than oil price decreases. However, in this dissertation, the asymmetric pattern is a little different. In the sub-sample period (1996.5-2005.12) when oil price increases more frequently than oil price decreases and the average magnitude of oil price increases is smaller than that of oil price decreases, stock markets in most countries are more influenced by oil price decreases than oil price increases in the variance decomposition analysis. In particular, statistically significant evidence at the 5% level is found that oil price decreases have a greater impact on real stock returns than oil price increases after the mid 1990's in the US.Includes bibliographical reference

Compute-proximal Energy Harvesting for Mobile Environments: Fundamentals, Applications, and Tools

Over the past two decades, we have witnessed remarkable achievements in computing, sensing, actuating, and communications capabilities of ubiquitous computing applications. However, due to the limitations in stable energy supply, it is difficult to make the applications ubiquitous. Batteries have been considered a promising technology for this problem, but their low energy density and sluggish innovation have constrained the utility and expansion of ubiquitous computing. Two key techniques—energy harvesting and power management—have been studied as alternatives to overcome the battery limitations. Compared to static environments such as homes or buildings, there are more energy harvesting opportunities in mobile environments since ubiquitous systems can generate various forms of energy as they move. Most of the previous studies in this regard have been focused on human movements for wearable computing, while other mobile environments (e.g., cars, motorcycles, and bikes) have received limited attention. In this thesis, I present a class of energy harvesting approaches called compute-proximal energy harvesting, which allows us to develop energy harvesting technology where computing, sensing, and actuating are needed in vehicles. Computing includes sensing phenomena, executing instructions, actuating components, storing information, and communication. Proximal considers the harvesting of energy available around the specific location where computation is needed, reducing the need for excessive wiring. A primary goal of this new approach is to mitigate the effort associated with the installation and field deployment of self-sustained computing and lower the entry barriers to developing self-sustainable systems for vehicles. In this thesis, I first select an automobile as a promising case study and discuss the opportunities, challenges, and design guidelines of compute-proximal energy harvesting with practical yet advanced examples in the automotive domain. Second, I present research in the design of small-scale wind energy harvesters and the implementation and evaluation of two advanced safety sensing systems—a blind spot monitoring system and a lane detection system—with the harvested power from wind. Finally, I conduct a study to democratize the lessons learned from the automotive case studies for makers and people with no prior experience in energy harvesting technology. In this study, I seek to understand what problems they have encountered and what possible solutions they have considered while dealing with energy harvesting technology. Based on the findings, I develop a comprehensive energy harvesting toolkit and examine its utility, usability, and creativity through a series of workshops.Ph.D

New Power Quality Index in a Distribution Power System by Using RMP Model

In this paper, a new power quality index (PQI), which is directly related to the generation of distortion power from nonlinear harmonic loads, is introduced to determine their harmonic pollution ranking in a distribution power system. The electric load composition rate (LCR) and the total harmonic distortion (THD) for the estimated currents on each harmonic load are used to define the proposed PQI. The reduced multivariate polynomial (RMP) model with one-shot training property is applied to realize the PQI. Then, the ranking of distortion power for each nonlinear load, which have adverse effect on the entire system, is determined. It is proved that the relative ranking based on the PQI matches that on the distortion power computed directly from each harmonic load

MLP/RBF Neural-Networks-Based Online Global Model Identification of Synchronous Generator

This paper compares the performances of a multilayer perceptron neural network (MLPN) and a radial basis function neural network (RBFN) for online identification of the nonlinear dynamics of a synchronous generator in a power system. The computational requirement to process the data during the online training, local convergence, and online global convergence properties are investigated by time-domain simulations. The performances of the identifiers as a global model, which are trained at different stable operating conditions, are compared using the actual signals as well as the deviation signals for the inputs of the identifiers. Such an online-trained identifier with fixed optimal weights after the global convergence test is needed to provide information about the plant to a neurocontroller. The use of the fixed weights is to provide against a sensor failure in which case the training of the identifiers would be automatically stopped, and their weights frozen, but the control action, which uses the identifier, would be able to continue

Adaptive Critic Designs and Their Implementations on Different Neural Network Architectures

The design of nonlinear optimal neurocontrollers based on the Adaptive Critic Designs (ACDs) family of algorithms has recently attracted interest. This paper presents a summary of these algorithms, and compares their performance when implemented on two different types of artificial neural networks, namely the multilayer perceptron neural network (MLPNN) and the radial basis function neural network (RBFNN). As an example for the application of the ACDs, the control of synchronous generator on an electric power grid is considered and results are presented to compare the different ACD family members and their implementations on different neural network architectures

Indirect Adaptive Control for Synchronous Generator: Comparison of MLP/RBF Neural Networks Approach with Lyapunov Stability Analysis

This paper compares two indirect adaptive neurocontrollers, namely a multilayer perceptron neurocontroller (MLPNC) and a radial basis function neurocontroller (RBFNC) to control a synchronous generator. The different damping and transient performances of two neurocontrollers are compared with those of conventional linear controllers, and analyzed based on the Lyapunov direct method

A Novel Dual Heuristic Programming Based Optimal Control of a Series Compensator in the Electric Power Transmission System

In this paper, the dual heuristic programming (DHP) optimization algorithm is used for the design of a nonlinear optimal neurocontroller that replaces the proportional-integral (PI) based conventional linear controller (CONVC) in the internal control of a power electronic converter based series compensator in the electric power transmission system. The performance of the proposed DHP based neurocontroller is compared with that of the CONVC with respect to damping low frequency oscillations. Simulation results using the PSCAD/EMTDC software package are presented