Thesis (M.S. in food science)--Washington State University, May 2009. / Title from PDF title page (viewed on July 28, 2009). "School of Food Science." Includes bibliographical references.

Thesis (M. Sc.)--Memorial University of Newfoundland, 1997. / Bibliography: leaves 122-128.

En este artículo son comparadas dos metodologías para estimar el Valor en Riesgo (VaR) del Índice General de la Bolsa de Valores de Lima (IGBVL) durante el periodo 2000-2006. Específicamente son utilizados el método RiskmetricsTM y el método de regresión cuantílica CAViaR, propuesto por Engle y Manganelli (2004). Los resultados obtenidos muestran que, en periodos de baja volatilidad o para VaR 95%, los VaR estimados por los dos métodos son próximos, pero se observan diferencias importantes en periodos de alta volatilidad, especialmente para VaR 99%.

Esse trabalho apresenta um estudo da causalidade de Granger em Risco bivariado aplicado a séries temporais financeiras. Os eventos de risco, no caso de séries financeiras, estão relacionados com a avaliação do Valor em Risco das posições em ativos. Para isso, os modelos CaViaR, que fazem parte do grupo de modelos de Regressão Quantílica, foram utilizado para identificação desses eventos. Foram expostos os conceitos principais envolvidos da modelagem, assim como as definições necessárias para entendê-las. Através da análise da causalide de Granger em risco entre duas séries, podemos investigar se uma delas é capaz de prever a ocorrência de um valor extremo da outra. Foi realizada a análise de causalidade de Granger usual somente para como comparativo. / Quantile Regression, Value at Risk, CAViaR Model, Granger Causality, Granger Causality in Risk

Esse trabalho apresenta um estudo da causalidade de Granger em Risco bivariado aplicado a séries temporais financeiras. Os eventos de risco, no caso de séries financeiras, estão relacionados com a avaliação do Valor em Risco das posições em ativos. Para isso, os modelos CaViaR, que fazem parte do grupo de modelos de Regressão Quantílica, foram utilizado para identificação desses eventos. Foram expostos os conceitos principais envolvidos da modelagem, assim como as definições necessárias para entendê-las. Através da análise da causalide de Granger em risco entre duas séries, podemos investigar se uma delas é capaz de prever a ocorrência de um valor extremo da outra. Foi realizada a análise de causalidade de Granger usual somente para como comparativo. / Quantile Regression, Value at Risk, CAViaR Model, Granger Causality, Granger Causality in Risk

La majorité des méthodes d’analyse et de contrôle actuelles dans l’agroalimentaire sont basées sur une approche ciblée, c'est-à-dire avec une définition en amont des contaminants recherchés, et présentent ainsi le risque de ne pas détecter certaines fraudes ou sources potentielles de contaminations de produits authentiques. C’est autour de cette problématique que le projet Agrifood GPS (Global Protection System), dont fait partie cette thèse, a été initié. Celui-ci a pour objectif principal la mise en place de nouvelles méthodes analytiques de criblage (non-ciblées) afin de garantir l’intégrité des produits analysés. Cette thèse regroupe ainsi les différents résultats obtenus sur des matrices semi-solides (poisson principalement), par RMN Haute Résolution en Rotation à l’Angle Magique (HR-MAS), et sur des extraits de caviar par RMN liquide haute résolution (HR). Ce manuscrit présente, tout d’abord, une méthode de détermination rapide de la fraîcheur et la qualité du poisson basée sur la mesure de deux indicateurs chimiques (le TMA-N et la valeur K) ainsi que les résultats portant sur l’évaluation de la texture du poisson à travers l’étude du temps de relaxation transversale (T2) de l’eau contenue dans les tissus musculaires, ces deux études étant réalisées par RMN HR-MAS. Puis, les résultats des travaux réalisés sur la détermination de l’origine géographique du caviar à l’aide du profil métabolique enregistré par spectroscopie RMN liquide haute résolution et d’analyses statistiques multivariées, dans le cadre de la démarche d’obtention d’une IGP (Indication Géographique Protégée) des producteurs de l’Aquitaine, et sur l’étude de la dégradation du caviar au cours de sa conservation au réfrigérateur sont alors présentés. / Most of the current analytical and quality control methods in the food industry are based on a targeted approach, with an upstream definition of the intended contaminants, and may fail to detect some frauds or contaminations of genuine products. It is around this issue that the Agrifood GPS (Global Protection System), of which this thesis is part of, has been initiated. This project aims at developing new holistic analytical methods (untargeted) in order to ensure the integrity of the foodstuff analyzed. This thesis manuscript gathers the results obtained on semi-solid foodstuffs (mainly fish), by High Resolution Magic Angle Spinning NMR, and on caviar extracts by high resolution liquid-state NMR (HR NMR). First of all, it presents a rapid method to evaluate fish freshness and quality based on the determination of two chemical indicators (the TMA-N and the K-value) and the results of a fish texture study through the measurement of the transverse relaxation time (T2) of water in muscle tissues, both by HR-MAS NMR spectroscopy. Thereafter, are presented the results of the work carried out on the determination of the geographical origin of caviar using the metabolic profile acquired by liquid-state NMR spectroscopy and multivariate statistical analysis in the context of the PGI (Protected Geographical Indication) status for the Aquitaine producers, and on the degradation study of caviar during its storage in a fridge.

Most of the literature on Value at Risk concentrates on the unconditional nonparametric or parametric approach to VaR estimation and much less on the direct modeling of conditional quantiles. This thesis focuses on the direct conditional VaR modeling, using the flexible quantile regression and hence imposing no restrictions on the return distribution. We apply semiparamet- ric Conditional Autoregressive Value at Risk (CAViaR) models that allow time-variation of the conditional distribution of returns and also different time-variation for different quantiles on four stock price indices: Czech PX, Hungarian BUX, German DAX and U.S. S&P 500. The objective is to inves- tigate how the introduction of dynamics impacts VaR accuracy. The main contribution lies firstly in the primary application of this approach on Cen- tral European stock market and secondly in the fact that we investigate the impact on VaR accuracy during the pre-crisis period and also the period covering the global financial crisis. Our results show that CAViaR models perform very well in describing the evolution of the quantiles, both in abso- lute terms and relative to the benchmark parametric models. Not only do they provide generally a better fit, they are also able to produce accurate forecasts. CAViaR models may be therefore used as a...

Tez (Yüksek Lisans) - Süleyman Demirel Üniversitesi, Fen Bilimleri Enstitüsü, Su Ürünleri Yetiştiriciliği Anabilim Dalı, 2008. / Kaynakça var.

The aim of this thesis is to measure changes in dependencies among returns on equity indices for European countries in tranquil periods against crisis periods and to investigate their asymmetries in the lower and upper tail of their distributions. The approach is based on a conditional probability that a random variable is lower than a given quantile while other random variables are also lower than their corresponding quantiles. Time-varying conditional quantiles are modeled by the Conditional Autoregressive Value at Risk via Regression Quantiles (CAViaR) method. In addition to the univariate conditional autoregressive models, the vector autoregressive extension is considered. In the second step, the conditional probability is estimated through the OLS regression. Moreover, the model which allows the distribution of returns in one country to lead or to lag the distribution of returns in another country, is defined and applied on European equity returns. Finally, the model measuring dependencies among more than two return series is derived and the relating dimensionality problems are discussed. The results document a significant increase in European equity return comovements in bear markets during the crisis in 1990s and 2000s. The explicit controlling for the high volatility days does not appear to have an impact on the main findings. For the comparison purposes, the results for Latin American countries are reported as well.

In this thesis the performance of the quantile based CAV iaR models is evaluated and compared with GARCH models for predicting the Value at Risk. This is done by one step ahead out of sample prediction. The one step ahead out of sample prediction is done for the 500 observations at the end of the sample. To calculate the predictions a rolling forecast is used. This means that the sample that is used to do the one step ahead predictions is equally sized for all 500 predictions. Then tests are performed to evaluate the predictive power of the forecasts. The tests that are used to evaluate the predictions are: the dynamic quantile test, the Kupiec test and the Christoffersens test. The data that is used in the analysis are two stock indexes and one exchange rate index. What is concluded from the thesis is that the models perform good in general for the Stockholmsb ̈orsen data. For the First north data the 1% V aR produced too high risk predictions so the exceedance rate became too low. For the 5% V aR the predictions were more accurate. For the exchange rate data the predictions from the models were generally good as well.