This thesis investigates forecasting performance of Quantile Regression Neural Networks in forecasting multiperiod quantiles of realized volatility and quantiles of returns. It relies on model-free measures of realized variance and its components (realized variance, median realized variance, integrated variance, jump variation and positive and negative semivariances). The data used are S&P 500 futures and WTI Crude Oil futures contracts. Resulting models of returns and volatility have good absolute performance and relative performance in comparison to the linear quantile regression models. In the case of in- sample the models estimated by Quantile Regression Neural Networks provide better estimates than linear quantile regression models and in the case of out-of-sample they are equally good.

There has been an on-going debate about choices of the most suitable model amongst a variety of model specifications and parameterizations. The first dissertation essay investigates whether asymmetric leptokurtic return distributions such as Hansen's (1994) skewed tdistribution combined with GARCH specifications can outperform mixed GARCH-jump models such as Maheu and McCurdy's (2004) GARJI model incorporating the autoregressive conditional jump intensity parameterization in the discrete-time framework. I find that the more parsimonious GJR-HT model is superior to mixed GARCH-jump models. Likelihood-ratio (LR) tests, information criteria such as AIC, SC, and HQ and Value-at-Risk (VaR) analysis confirm that GJR-HT is one of the most suitable model specifications which gives us both better fit to the data and parsimony of parameterization. The benefits of estimating GARCH models using asymmetric leptokurtic distributions are more substantial for highly volatile series such as emerging stock markets, which have a higher degree of non-normality. Furthermore, Hansen's skewed t-distribution also provides us with an excellent risk management tool evidenced by VaR analysis. The second dissertation essay provides a variety of empirical evidences to support redundancy of stochastic volatility for SP500 index returns when stochastic volatility is taken into account with infinite activity pure Lévy jumps models and the importance of stochastic volatility to reduce pricing errors for SP500 index options without regard to jumps specifications. This finding is important because recent studies have shown that stochastic volatility in a continuous-time framework provides an excellent fit for financial asset returns when combined with finite-activity Merton's type compound Poisson jump-diffusion models. The second essay also shows that stochastic volatility with jumps (SVJ) and extended variance-gamma with stochastic volatility (EVGSV) models perform almost equally well for option pricing, which strongly imply that the type of Lévy jumps specifications is not important factors to enhance model performances once stochastic volatility is incorporated. In the second essay, I compute option prices via improved Fast Fourier Transform (FFT) algorithm using characteristic functions to match arbitrary log-strike grids with equal intervals with each moneyness and maturity of actual market option prices.

Este trabalho apresenta um estudo através da abordagem Bayesiana em modelos de volatilidade estocástica, para modelagem de séries temporais financeiras, com o uso do método de Monte Carlo Hamiltoniano (HMC). Propomos o uso de outras distribuições para os erros da equação de observações do modelos de volatilidade estocástica, além da distribuição Gaussiana, para tratar problemas como caudas pesadas e assimetria nos retornos. Além disso, utilizamos critérios de informações, recentemente desenvolvidos, WAIC e LOO que aproximam a metodologia de validação cruzada, para realizar a seleção de modelos. No decorrer do trabalho, estudamos a qualidade do método HMC através de exemplos, estudo de simulação e aplicação a conjunto de dados. Adicionalmente, avaliamos a performance dos modelos e métodos propostos através do cálculo de estimativas para o Valor em Risco (VaR) para múltiplos horizontes de tempo. / This paper presents a study using Bayesian approach in stochastic volatility models for modeling financial time series, using Hamiltonian Monte Carlo methods (HMC). We propose the use of other distributions for the errors of the equation at stochastic volatiliy model, besides the Gaussian distribution, to treat the problem as heavy tails and asymmetry in the returns. Moreover, we use recently developed information criteria WAIC and LOO that approximate the crossvalidation methodology, to perform the selection of models. Throughout this work, we study the quality of the HMC methods through examples, simulation study and application to dataset. In addition, we evaluated the performance of the proposed models and methods by calculating estimates for Value at Risk (VaR) for multiple time horizons.

This thesis performs portfolio optimization using three allocation methods, Certainty Equivalence Tangency (CET), Global Minimum Variance (GMV) and Minimum Conditional Value-at-Risk (MinCVaR). We estimate expected returns and covariance matrices based on 7 stock market indices with a DCC-GARCH model including an ARMA (1.1) process and an external regressor of an implied volatility index (VIX). We then simulate returns using a rolling window of 500 daily observations and construct portfolios based on the allocation methods. The results suggest that the model can sufficiently estimate expected returns and covariance matrices and we can outperform benchmarks in form of equally weighted and historical portfolios in terms of higher returns and lower risk. Over the whole out-of-sample period the CET portfolio yields the highest mean returns and GMV and MinCVaR can significantly lower the variance. The inclusion of VIX has marginal effects on the forecasting accuracy and it seems to impair the estimation of risk.

In the financial industry, it has been increasingly popular to measure risk. One of the most common quantitative measures for assessing risk is Value-at-Risk (VaR). VaR helps to measure extreme risks that an investor is exposed to. In addition to acquiring information of the expected loss, VaR was introduced in the regulatory frameworks of Basel I and II as a standardized measure of market risk. Due to necessity of measuring VaR accurately, this thesis aims to be a contribution to the research field of applying GARCH-models to financial time series in order to forecast the conditional variance and find accurate VaR-estimations. The findings in this thesis is that GARCH-models which incorporate the asymmetric effect of positive and negative returns perform better than a standard GARCH. Further on, leptokurtic distributions have been found to outperform normal distribution. In addition to various models and distributions, various rolling windows have been used to examine how the forecasts differ given window lengths.

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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

Cette thèse se concentre sur des mesures du risque financier et la modélisation de la volatilité. L’objectifgénéral est : (i) de proposer de nouvelles techniques pour mesurer à la fois le risque systémique et lerisque à haute fréquence, et (ii) d’appliquer et d’améliorer les outils économétriques de modélisation etde prévision de la volatilité. Ce travail comporte quatre chapitres (papiers de recherche).La première partie de la thèse traite des questions liées à la modélisation et la prévision des mesuresdu risque à haute fréquence et du risque systémique. Plus précisément, le deuxième chapitre proposeune nouvelle mesure du risque systémique utilisée pour identifier les institutions financières d’importancesystémique (SIFIs). Basée sur une approche spécifique, cette mesure originale permet de décomposer lerisque global du système financier tout en tenant compte des caractéristiques de l’entreprise. Le troisièmechapitre propose des mesures du risque de marché intra-journalier dans le contexte particulier des donnéesà haute fréquence irrégulièrement espacées dans le temps (tick-by-tick).La deuxième partie de la thèse est consacrée aux méthodes d’estimation et de prévision de la volatilitéincluant directement des données à haute fréquence ou des mesures réalisées de volatilité. Ainsi, dans lequatrième chapitre, nous cherchons à déterminer, dans le contexte des modèles de mélange des fréquencesd’échantillonnage (MIDAS), si des regresseurs à haute fréquence améliorent les prévisions de la volatilitéà basse fréquence. Une question liée est de savoir s’il existe une fréquence d’échantillonnage optimaleen termes de prévision, et non de mesure de la volatilité. Le cinquième chapitre propose une versionrobuste aux jumps du modèle Realized GARCH. L’application porte sur la crise / This thesis focuses on financial risk measures and volatility modeling. The broad goal of this dissertationis: (i) to propose new techniques to measure both systemic risk and high-frequency risk, and (ii) toapply and improve advanced econometric tools to model and forecast time-varying volatility. This workhas been concretized in four chapters (articles).The first part addresses issues related to econometric modeling and forecasting procedures on bothsystemic risk and high-frequency risk measures. More precisely, Chapter 2 proposes a new systemic riskmeasure used to identify systemically important financial institutions (SIFIs). Based on a componentapproach, this original measure allows to decompose the risk of the aggregate financial system whileaccounting for the firm characteristics. Chapter 3 studies the importance and certifies the validity ofintraday High Frequency Risk (HFR) measures for market risk in the special context of irregularly spacedhigh-frequency data.The second part of this thesis tackles the need to improve the estimation/prediction of volatility bydirectly including high-frequency data or realized measures of volatility. Therefore, in Chapter 4 weexamine whether high-frequency data improve the volatility forecasts accuracy, and if so, whether thereexists an optimal sampling frequency in terms of prediction. Chapter 5 studies the financial volatilityduring the global financial crisis. To this aim, we use the largest volatility shocks, as provided by therobust version of the Realized GARCH model, to identify and analyze the events having induced theseshocks during the crisis.

L’implémentation de Solvabilité II constitue une révolution pour les sociétés d’assurance car elle entraine d’importants changements dans leurs pratiques de gestion. Plusieurs inquiétudes ont été soulevées quant aux impacts de la calibration de la formule standard sur les investissements des assureurs et l’économie en général. A cela s’ajoutent les exigences sur la gouvernance et la gestion des risques qui vont modifier la culture de risques des entreprises et qui vont encourager l’adoption d’une approche ERM. Cette thèse a donc pour objectif d’analyser les impacts des exigences de Solvabilité II et de l’ERM sur la gestion d’actifs et la gestion des risques des assureurs.Une première partie traite des effets potentiels de Solvabilité II sur les investissements obligataires. Nous analysons la pertinence du SCR obligataire et le comportement du couple rendement-SCR. Les résultats montrent que la mesure de risque réglementaire est globalement satisfaisante pour les obligations à faible risque mais qu’elle est surestimée (sous-estimée) en période d’absence de crise (en période de crise) pour les obligations à risque élevé. Nous montrons également que la calibration de Solvabilité II favorise les obligations à faible duration et notamment, les obligations high yield. Une deuxième partie porte sur les facteurs déterminants de l’état d’avancement de l’ERM et ses bénéfices. Notre principale contribution est la construction d’un indice continu sur l’ERM qui permet de déterminer l’état d’avancement de l’ERM pour les assureurs. Nos résultats montrent que le statut de mutuelle, la taille de l’assureur et sa localisation géographique influencent l’état d’avancement de l’ERM, et qu’une relation positive et significative existe entre l’état d’avancement de l’ERM et la rentabilité des assureurs. / Solvency II implementation constitutes a revolution for insurance companies because it leads to major changes their management practices. Solvency II propositions have thus become a major concern for the insurance sector, especially regarding the potential impact of the standard formula on insurers’ investments and on the economy. Aditionnally, Solvency II governance and risk management principles will modify insurers’ risk management culture and will encourage them to adopt ERM practices. This thesis analyses the impact of Solvency II requirements and ERM on insurer’s management practices.A first analysis focuses on the effects of Solvency II calibration on insurance companies’ bond investments. We analyse the adequacy of bond SCR and the behaviour of the return-SCR couple. The results show that the regulatory risk measure for low risk bonds is overall adequate, but that it is overestimated (underestimated) for high risk bonds in non-crisis periods (in crisis periods). We also show that Solvency II calibration encourages investments in low duration bonds and especially high yield bonds. A second analysis focuses on understanding the determinants of ERM adoption and its benefits. Our main contribution is the creation of a continuous ERM index that measures the level of development of insurance companies’ ERM program. We conclude that insurers’ mutual status, size, and geographical location are determinants of ERM state of progress and that there is a positive and significant relationship between ERM state of progress and companies’ profitability.

Made available in DSpace on 2016-04-25T16:45:31Z (GMT). No. of bitstreams: 1 Jose Augusto da Silva Rezende.pdf: 1815562 bytes, checksum: c40bcc71ae45d2b7fd4c4ec054a04ca9 (MD5) Previous issue date: 2010-07-28 / This dissertation brings a review on two management and risk assessment tools: VaR and Tracking Error and their applicability in pension funds. It describes the calculation methodologies and their employment in the management of a pension fund. It also reviews the literature on the employment of VaR and Tracking Error for risk assessment of pension funds. Consequently, make VaR and Tracking Error calculations for a theoretical pension funds portfolio, created from active principles and participations determined by the law. From this point on, it performs an exploratory study which seeks to analyze the tenure to a theoretical pension fund portfolio to a benchmark (inflation index composing the actuarial goal). For this purpose, six classes of financial assets were used such as stocks, interests and inflation risks. From these results, the work finds evidences that tenure assessment (Tracking Error), complemented by total risk Assessment, are useful tools for the management of pension funds and the findings on deviations of actuarial goals / Esta dissertação faz uma revisão de duas ferramentas de gestão de riscos: o VaR e o Tracking Error, e suas aplicabilidades aos fundos de pensão. Descreve as metodologias de cálculo e as suas utilizações na gestão de um fundo de pensão. Revisa a literatura acerca do uso do VaR e do Tracking Error como medidas de risco dos fundos de pensão. A seguir faz o cálculo do VaR e Tracking Error para uma carteira teórica de fundo de pensão, criada a partir dos principais ativos e participações determinados pela Legislação. A partir disso, realiza um estudo exploratório, que busca analisar a aderência da carteira teórica de um fundo de pensão a um benchmark (índice inflacionário componente da meta atuarial). Para isso, foram utilizados 6 classes de ativos financeiros com riscos de bolsa, juros e inflação. A partir dos resultados obtidos, o trabalho encontra evidências que medidas de aderência (Tracking Error), complementadas por medidas de risco absoluto (VaR), são ferramentas úteis para a gestão de fundos de pensão e, na apuração de desvios da meta atuarial