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Forecasting Volatility for commodity futures using fat-tailed modelKe, Pei-ru 08 July 2011 (has links)
This paper considers the high-moments and uses the skew generalized error distribution (SGED) to explain the financial market data which have leptokurtic, fat-tailed and skewness. And we compare performance with the commonly used symmetrical distribution model such as normal distribution, student¡¦s t distribution and generalized error distribution (GED). To research when returns of asset have leptokurtic and fat-tailed phenomena, what model has better predictive power for volatility forecasting?
The empirical procedure is as follows: First step, make the descriptive statistics of raw data, and know that the GARCH effect should be considered, followed by selecting the optimal order of ARMA-GARCH. The second steps, make the parameter estimations of full-sample, and pick up the best model. Finally, forecast out-of-sample volatility for 1-day, 2-day, 5-day, 10-day and 20-day respectively, not only use different loss function to measure the performance, but also use DM test to compare the relative predictive power of the models under the different error distribution.
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Analyzing value at risk and expected shortfall methods: the use of parametric, non-parametric, and semi-parametric modelsHuang, Xinxin 25 August 2014 (has links)
Value at Risk (VaR) and Expected Shortfall (ES) are methods often used to measure market risk. Inaccurate and unreliable Value at Risk and Expected Shortfall models can lead to underestimation of the market risk that a firm or financial institution is exposed to, and therefore may jeopardize the well-being or survival of the firm or financial institution during adverse markets. The objective of this study is therefore to examine various Value at Risk and Expected Shortfall models, including fatter tail models, in order to analyze the accuracy and reliability of these models.
Thirteen VaR and ES models under three main approaches (Parametric, Non-Parametric and Semi-Parametric) are examined in this study. The results of this study show that the proposed model (ARMA(1,1)-GJR-GARCH(1,1)-SGED) gives the most balanced Value at Risk results. The semi-parametric model (Extreme Value Theory, EVT) is the most accurate Value at Risk model in this study for S&P 500. / October 2014
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Smartcom: uma arquitetura inteligente de gerenciamento de consumo de energia para smart homeOLIVEIRA, Edvar da Luz 25 August 2017 (has links)
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Previous issue date: 2017-08-25 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / CNPq - Conselho Nacional de Desenvolvimento Científico e Tecnológico / Com os avanços na tecnologia da informação para diversas áreas, como saúe bem-estar, as soluções baseadas em Smart Home que usam as tecnologidaes Internet of Things (IoT) vem ganhando maior repercussão, inclusive como alternativas de economia de energia com base em Sistemas de Gerenciamento de Energia Domiciliar (SGED). Esta tese define uma arquitetura inovadora, denominada de SmartCoM, que é implementada para monitorar e gerenciar habitações residenciais usando tecnologias IoT. Tal estratégia envolve a definição dos parâmetros que podem efetivar a interoperabilidade entre medição, gerenciamento e as camadas de comunicação de dados, que são os recursos necessários para que os dispositivos de hardware possam realizar o monitoramento e medição pretendidos. Além disso, uma interface é definida por uma camada de middleware para integrar o gerenciamento de instalações externas e a visualização de dados por meio de um serviço em nuvem. A arquitetura SmartCoM é definida de maneira fim-a-fim, em detalhes do ponto de vista do consumidor e as estratégias de otimização s.o empregadas tanto para o cliente final quanto para a concessionária de energia. A fim de avaliar a arquitetura proposta, foi elaborado um estudo de caso, a partir do qual, observa-se a viabilidade do desenvolvimento de soluções para Smart Home de acordo com os requisitos descritos na SmartCoM. / With advances in information technology for a variety of areas, such as health and wellness, Smart Home based solutions using Internet of Things (IoT) technologies are gaining in popularity, including energy-saving alternatives based on Home Energy Management Systems (HEMS). This thesis defines an innovative architecture, called SmartCoM, which is implemented to monitor and manage residences using IoT technologies. Such a strategy involves defining the parameters that can affect the interoperability between measurement, management and the layers of data communication, which are the resources necessary for the hardware devices to perform the intended monitoring and measurement. In addition, an interface is defined by a middleware layer to integrate the management of external installations and the visualization of data through cloud services. The SmartCoM architecture is defined end-to-end in detail from the consumer's point of view and optimization strategies are employed for both the end customer and the utility. In order to evaluate the proposed architecture, a case study was elaborated, from which the viability of developing solutions for Smart Home according to the requirements described in SmartCoM is observed.
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