Modelando a volatilidade dos retornos de Petrobrás usando dados de alta frequência

O objetivo do presente trabalho é analisar as características empíricas de uma série de retornos de dados em alta freqüência para um dos ativos mais negociados na Bolsa de Valores de São Paulo. Estamos interessados em modelar a volatilidade condicional destes retornos, testando em particular a presença de memória longa, entre outros fenômenos que caracterizam este tipo de dados. Nossa investigação revela que além da memória longa, existe forte sazonalidade intradiária, mas não encontramos evidências de um fato estilizado de retornos de ações, o efeito alavancagem. Utilizamos modelos capazes de captar a memória longa na variância condicional dos retornos dessazonalizados, com resultados superiores a modelos tradicionais de memória curta, com implicações importantes para precificação de opções e de risco de mercado

Modelo de volatilidade estocástica com saltos aplicado a commodities agrícolas

Mensalmente são publicados relatórios pelo Departamento de Agricultura dos Estados Unidos (USDA) onde são divulgados dados de condições das safras, oferta e demanda globais, nível dos estoques, que servem como referência para todos os participantes do mercado de commodities agrícolas. Esse mercado apresenta uma volatilidade acentuada no período de divulgação dos relatórios. Um modelo de volatilidade estocástica com saltos é utilizado para a dinâmica de preços de milho e de soja. Não existe um modelo ‘ideal’ para tal fim, cada um dos existentes têm suas vantagens e desvantagens. O modelo escolhido foi o de Oztukel e Wilmott (1998), que é um modelo de volatilidade estocástica empírica, incrementado com saltos determinísticos. Empiricamente foi demonstrado que um modelo de volatilidade estocástica pode ser bem ajustado ao mercado de commodities, e o processo de jump-diffusion pode representar bem os saltos que o mercado apresenta durante a divulgação dos relatórios. As opções de commodities agrícolas que são negociadas em bolsa são do tipo americanas, então alguns métodos disponíveis poderiam ser utilizados para precificar opções seguindo a dinâmica do modelo proposto. Dado que o modelo escolhido é um modelo multi-fatores, então o método apropriado para a precificação é o proposto por Longstaff e Schwartz (2001) chamado de Monte Carlo por mínimos quadrados (LSM). As opções precificadas pelo modelo são utilizadas em uma estratégia de hedge de uma posição física de milho e de soja, e a eficiência dessa estratégia é comparada com estratégias utilizando-se instrumentos disponíveis no mercado.

Eficiência e razão de hedge: uma análise dos mercados futuro brasileiros de boi, café, etanol, milho e soja

This research aims to investigate the Hedge Efficiency and Optimal Hedge Ratio for the future market of cattle, coffee, ethanol, corn and soybean. This paper uses the Optimal Hedge Ratio and Hedge Effectiveness through multivariate GARCH models with error correction, attempting to the possible phenomenon of Optimal Hedge Ratio differential during the crop and intercrop period. The Optimal Hedge Ratio must be bigger in the intercrop period due to the uncertainty related to a possible supply shock (LAZZARINI, 2010). Among the future contracts studied in this research, the coffee, ethanol and soybean contracts were not object of this phenomenon investigation, yet. Furthermore, the corn and ethanol contracts were not object of researches which deal with Dynamic Hedging Strategy. This paper distinguishes itself for including the GARCH model with error correction, which it was never considered when the possible Optimal Hedge Ratio differential during the crop and intercrop period were investigated. The commodities quotation were used as future price in the market future of BM&FBOVESPA and as spot market, the CEPEA index, in the period from May 2010 to June 2013 to cattle, coffee, ethanol and corn, and to August 2012 to soybean, with daily frequency. Similar results were achieved for all the commodities. There is a long term relationship among the spot market and future market, bicausality and the spot market and future market of cattle, coffee, ethanol and corn, and unicausality of the future price of soybean on spot price. The Optimal Hedge Ratio was estimated from three different strategies: linear regression by MQO, BEKK-GARCH diagonal model, and BEKK-GARCH diagonal with intercrop dummy. The MQO regression model, pointed out the Hedge inefficiency, taking into consideration that the Optimal Hedge presented was too low. The second model represents the strategy of dynamic hedge, which collected time variations in the Optimal Hedge. The last Hedge strategy did not detect Optimal Hedge Ratio differential between the crop and intercrop period, therefore, unlikely what they expected, the investor do not need increase his/her investment in the future market during the intercrop

Genetic associations between carcass traits measured by real-time ultrasound and scrotal circumference and growth traits in Nelore cattle

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Associações genéticas entre pesos e características reprodutivas em rebanhos da raça Nelore

Dados de 14.918 animais da raça Nelore nascidos entre 1991 e 2000, provenientes de rebanhos localizados nas regiões Sul e Sudeste do País, foram utilizados para estimar componentes de co-variância, herdabilidade e correlações genéticas de peso ao desmame (PD), peso a 1 ano de idade (PA), peso ao sobreano (PS), peso ao primeiro parto (PPP), idade ao primeiro parto (IPP) e dias para o primeiro parto (DP). As estimativas dos componentes de co-variância e dos parâmetros genéticos foram obtidas pelo método de máxima verossimilhança restrita, em análises multivariadas. As herdabilidades estimadas para PD, PA, PS, PPP, IPP e DP foram de 0,26; 0,30; 0,34; 0,35; 0,14 e 0,07, respectivamente. Correlações genéticas negativas foram estimadas entre pesos medidos em diferentes idades e IPP, as quais variaram de -0,31 a -0,16. do mesmo modo, as estimativas de correlação genética entre PD × DP (-0,09); PA × DP (-0,13); PS × DP (-0,17) e PPP × DP (-0,16) foram negativas, embora de menor magnitude. As correlações genéticas estimadas entre as características de crescimento e a IPP foram favoráveis. Assim, a seleção para aumento de peso deve promover redução da IPP. A alta correlação genética estimada entre IPP e DP (0,73) indica que o uso de DP na seleção de bovinos de corte pode promover resposta correlacionada favorável na idade ao primeiro parto.

Determinação do teor de diclofenaco sódico em comprimidos por espectroscopia no infravermelho próximo NIR com calibração multivariada PLS

This work is combined with the potential of the technique of near infrared spectroscopy - NIR and chemometrics order to determine the content of diclofenac tablets, without destruction of the sample, to which was used as the reference method, ultraviolet spectroscopy, which is one of the official methods. In the construction of multivariate calibration models has been studied several types of pre-processing of NIR spectral data, such as scatter correction, first derivative. The regression method used in the construction of calibration models is the PLS (partial least squares) using NIR spectroscopic data of a set of 90 tablets were divided into two sets (calibration and prediction). 54 were used in the calibration samples and the prediction was used 36, since the calibration method used was crossvalidation method (full cross-validation) that eliminates the need for a validation set. The evaluation of the models was done by observing the values of correlation coefficient R 2 and RMSEC mean square error (calibration error) and RMSEP (forecast error). As the forecast values estimated for the remaining 36 samples, which the results were consistent with the values obtained by UV spectroscopy

Espectroscopia no infravermelho próximo e métodos de calibração multivariada aplicados à determinação simultânea de parâmetros bioquímicos em plasma sanguíneo

In this work, the quantitative analysis of glucose, triglycerides and cholesterol (total and HDL) in both rat and human blood plasma was performed without any kind of pretreatment of samples, by using near infrared spectroscopy (NIR) combined with multivariate methods. For this purpose, different techniques and algorithms used to pre-process data, to select variables and to build multivariate regression models were compared between each other, such as partial least squares regression (PLS), non linear regression by artificial neural networks, interval partial least squares regression (iPLS), genetic algorithm (GA), successive projections algorithm (SPA), amongst others. Related to the determinations of rat blood plasma samples, the variables selection algorithms showed satisfactory results both for the correlation coefficients (R²) and for the values of root mean square error of prediction (RMSEP) for the three analytes, especially for triglycerides and cholesterol-HDL. The RMSEP values for glucose, triglycerides and cholesterol-HDL obtained through the best PLS model were 6.08, 16.07 e 2.03 mg dL-1, respectively. In the other case, for the determinations in human blood plasma, the predictions obtained by the PLS models provided unsatisfactory results with non linear tendency and presence of bias. Then, the ANN regression was applied as an alternative to PLS, considering its ability of modeling data from non linear systems. The root mean square error of monitoring (RMSEM) for glucose, triglycerides and total cholesterol, for the best ANN models, were 13.20, 10.31 e 12.35 mg dL-1, respectively. Statistical tests (F and t) suggest that NIR spectroscopy combined with multivariate regression methods (PLS and ANN) are capable to quantify the analytes (glucose, triglycerides and cholesterol) even when they are present in highly complex biological fluids, such as blood plasma

Thermophysical properties of mango pulp (Mangifera indica L. cv. Tommy Atkins)

The density, heat capacity and thermal conductivity of mango pulp (Mangifera indica L. cv. Tommy Atkins) were determined at moisture contents of between 0.9 and 0.52 kg kg(-1) (w.b.) and temperatures of between 20 and 80 degrees C. The experimental data were satisfactorily fitted (explained variation values >99.1%) as functions of the moisture content and temperature by using multivariate linear models. In the range of conditions considered, the moisture content exhibits a greater influence on the studied properties than temperature. (C) 2009 Elsevier Ltd. All rights reserved.

Microscopic origin of non-Gaussian distributions of financial returns

In this paper we study the possible microscopic origin of heavy-tailed probability density distributions for the price variation of financial instruments. We extend the standard log-normal process to include another random component in the so-called stochastic volatility models. We study these models under an assumption, akin to the Born-Oppenheimer approximation, in which the volatility has already relaxed to its equilibrium distribution and acts as a background to the evolution of the price process. In this approximation, we show that all models of stochastic volatility should exhibit a scaling relation in the time lag of zero-drift modified log-returns. We verify that the Dow-Jones Industrial Average index indeed follows this scaling. We then focus on two popular stochastic volatility models, the Heston and Hull-White models. In particular, we show that in the Hull-White model the resulting probability distribution of log-returns in this approximation corresponds to the Tsallis (t-Student) distribution. The Tsallis parameters are given in terms of the microscopic stochastic volatility model. Finally, we show that the log-returns for 30 years Dow Jones index data is well fitted by a Tsallis distribution, obtaining the relevant parameters. (c) 2007 Elsevier B.V. All rights reserved.

Reductions in the Prevalence and Incidence of Geohelminth Infections following a City-wide Sanitation Program in a Brazilian Urban Centre

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Clinical patterns and epidemiological characteristics of facial melasma in Brazilian women

Background Melasma is a common acquired chronic hypermelanosis of sun-exposed areas which significantly impacts quality of life. There are few epidemiological studies in medical literature concerning these patients. Objective Characterize clinical and epidemiological data on Brazilian female patients with melasma. Methods A semi-structured questionnaire was administered to melasma patients treated at a dermatology clinic between 2005 and 2010. Association between variables was performed by multivariate regression models. Results We assessed 302 patients; intermediate skin phototypes III (34.4%) and IV (38.4%) were prevalent. Mean disease onset age was 27.5 ± 7.8 years and familiar occurrence of melasma was identified in 56.3%. The most commonly reported trigger factors were pregnancy (36.4%), contraceptive pills (16.2%) and intense sun exposure (27.2%). Preferred facial topographies were zygomatic (83.8%), labial superior (51.3%) and frontal (49.7%). Pregnancy induced melasma has been associated to early disease (OR = 0.86) and number of pregnancies (OR = 1.39). Childbearing was correlated to melasma extension. Older disease onset age was associated to darker skin phototypes. Co-occurrence of facial topographies supported clinical classification as centrofacial and peripheral melasma. Conclusion This population was characterized by: a high prevalence in adult females, intermediate skin phototypes, disease precipitation by hormonal stimulus and familiar genetic influence. © 2012 The Authors. Journal of the European Academy of Dermatology and Venereology © 2012 European Academy of Dermatology and Venereology.

Análise multivariada no monitoramento do processo de produção de óleo diesel numa refinaria de petróleo

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Análise bayesiana do modelo fatorial dinâmico para um vetor de séries temporais utilizando a distribuição t multivariada

The multivariate t models are symmetric and with heavier tail than the normal distribution, important feature in financial data. In this theses is presented the Bayesian estimation of a dynamic factor model, where the factors follow a multivariate autoregressive model, using multivariate t distribution. Since the multivariate t distribution is complex, it was represented in this work as a mix between a multivariate normal distribution and a square root of a chi-square distribution. This method allowed to define the posteriors. The inference on the parameters was made taking a sample of the posterior distribution, through the Gibbs Sampler. The convergence was verified through graphical analysis and the convergence tests Geweke (1992) and Raftery & Lewis (1992a). The method was applied in simulated data and in the indexes of the major stock exchanges in the world.

Estudo comparativo morfofuncional e expressão de receptor de melanocortina do tipo 1 em lesões de melasma

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

Uso do modelo linear multivariado de crescimento no estudo da densidade básica da madeira de eucaliptos

Pós-graduação em Agronomia (Energia na Agricultura) - FCA