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Σκοπός της παρούσας μελέτης είναι να εξετάσουμε την σχέση που υπάρχει μεταξύ της χρηματιστηριακής αγοράς και αυτής των αποδόσεων των ομολόγων σε τέσσερις χώρες της Ευρωπαϊκής Ένωσης (Γερμανίας, Ιταλίας, Ισπανίας και Γαλλίας) για την περίοδο από τον Δεκέμβριο 1999 έως τον Δεκέμβριο του 2012. Προσπαθήσαμε να εξετάσουμε το κατά πόσο υπάρχουν συσχετίσεις μεταξύ των δύο περιουσιακών στοιχείων σε μεγάλο χρονικό διάστημα χρησιμοποιώντας πολυμεταβλητά μοντέλα. Τα δεδομένα που πήραμε είναι οι ημερήσιες αποδόσεις των 10ετών ομολόγων και τα κλεισίματα των χρηματιστηριακών αγορών των χωρών αυτών για κάθε μία ξεχωριστά. Ξεκινάμε την ερευνά μας χρησιμοποιώντας το μοντέλο του GARCH του Bollerslev (1990). Τέλος μέσω της συνολοκλήρωσης με την διαδικασία του Johansen test θα εξετάσουμε το κατά πόσο οι σειρές μας ολοκληρώνονται μακροχρόνια επηρεάζοντας η μία την άλλη καθώς και την μεταξύ τους εξάρτηση και την αιτιότητα των εν λόγω σχέσεων. Η εργασίας μας έχει ως στόχο να μας δείξει το κατά πόσο υπάρχει μακροχρόνια συσχέτιση μεταξύ των δύο αυτών αγορών, ώστε να βοηθά τους διαχειριστές και οικονομικούς αναλυτές να δημιουργούν το χαρτοφυλάκιο με το μικρότερο κίνδυνο και την μεγαλύτερη απόδοση. Τα αποτελέσματα μας δείχνουν μία μακροχρόνια συσχέτιση μεταξύ αυτών των δύο αγορών και ότι η μία αγορά επηρεάζει την άλλη σε βάθος χρόνου, οπότε είναι χρήσιμο σε ένα χαρτοφυλάκιο να υπάρχουν και τα δύο περιουσιακά στοιχεία. / The purpose of this study is to look at the relationship between stock market and bond market in four European Countries (Germany, France, Spain and Italy) for the period of December 1999 to December 2012. We attempt to examine whether the correlations between two classes of assets are time varying by using multivariate conditional volatility models. The data we are daily yields on 10-year bonds and the closures of the stock markets of these countries for each one individually. We start our investigation by applying GARCH model of Bollerslev (1990). Finally, through co integration with the process of Johansen test will look at whether our series completed long influencing each other and their mutual dependence and causality of these relations. Our paper aims to show us whether there is a long correlation between these two markets in order to help managers and financial analysts to create a portfolio with less risk and greater efficiency. Our results show a long-term correlation between these two markets and one market affects the other in the long run, so it is useful to have a portfolio of both assets.

[pt] Esta dissertação examina os impactos das boas práticas de governança corporativa na volatilidade dos retornos das ações dentro e fora de momentos de crise. Dados de freqüência diária foram utilizados para estimar modelos Autoregressivos Generalizados de Heterocedasticidade Condicional (GARCH) para quarenta e nove papéis negociados na Bolsa de Valores de São Paulo (BOVESPA). As evidências indicam um efeito negativo na maioria das séries analisadas. Para algumas ações, a redução da volatilidade é ainda maior em períodos de choques negativos. Foi encontrado, ainda, o resultado de que o risco mitigado é o idiossincrático e, desta forma, governança incentiva a manutenção da concentração de propriedade. / [en] This dissertation examines impacts of good practices of corporate governance on the volatility of returns in and out crisis periods. Daily data are used to estimate Generalized Autoregressive Conditional Heteroskedastic (GARCH) models for forty nine stocks traded on the São Paulo Stock Exchange (BOVESPA. It is found evidence of a negative impact on the majority of the analyzed series. For some stocks, the reduction of the volatility is even greater in crisis periods. It was also found that the risk mitigated is the idiosyncratic one and, thus, governance incentives the maintenance of ownership concentration.

This paper presents a comprehensive review and comparison of five computational methods for Bayesian model selection, based on MCMC simulations from posterior model parameter distributions. We apply these methods to a well-known and important class of models in financial time series analysis, namely GARCH and GARCH-t models for conditional return distributions (assuming normal and t-distributions). We compare their performance vis--vis the more common maximum likelihood-based model selection on both simulated and real market data. All five MCMC methods proved feasible in both cases, although differing in their computational demands. Results on simulated data show that for large degrees of freedom (where the t-distribution becomes more similar to a normal one), Bayesian model selection results in better decisions in favour of the true model than maximum likelihood. Results on market data show the feasibility of all model selection methods, mainly because the distributions appear to be decisively non-Gaussian. / Series: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science"

O estudo da volatilidade dos retornos dos ativos ocupa um lugar de destaque dentro da moderna teoria de finanças. Tradicionalmente, os modelos empregados para a modelagem da volatilidade são estimados a partir de dados diários. No entanto, a recente disponibilidade de dados intradiários tem permitido a modelagem e a previsão da volatilidade dos ativos por meio da chamada variância realizada. Dessa forma, o objetivo principal da presente dissertação foi analisar como os modelos que incorporam dados intradiários se comportam, em termos de acurácia de previsão de volatilidade diária, em relação àqueles que utilizam apenas dados diários. Foram observados os comportamentos dos índices Ibovespa e S&P 500 durante o período de janeiro de 2006 a junho de 2009. Os resultados revelaram que o desempenho de previsão dos modelos estimados a partir de dados diários foi superior ao dos modelos de variância realizada para os dois índices. Buscou-se ainda comparar o comportamento dos modelos durante o período da crise de 2008. Novamente os resultados apontaram para uma melhor acurácia de previsão dos modelos que utilizaram apenas dados diários. / The study of volatility in asset returns is relevant within the modern theory of finance. Modeling volatility has been frequently based on daily data. Recent availability of intraday data has allowed volatility modeling and forecasting through the so called realized variance. The main objective of this master’s thesis was, therefore, to compare the accuracy of daily volatility forecasting between models that use either daily or intraday data. Returns during the period January 2006 to June 2009 on two indexes, the Ibovespa and the S&P 500, were used. Results showed that, for both indexes, forecasting based on daily data was superior to forecasting that used intraday returns. Comparison between models was also tested during the 2008 crisis. Similarly, results showed a better forecasting performance of daily data models.

As séries temporais financeiras são marcadas por comportamentos complexos e não-lineares. No mercado financeiro, além da trajetória das cotações, a sua variabilidade, representada pela volatilidade, consiste em importante informação para o mercado. Redes neurais são modelos não lineares flexíveis com capacidade de descrever funções de distintas classes, possuindo a propriedade de aproximadores universais. Este trabalho busca empregar redes neurais, especificamente Perceptron de múltiplas camadas com uma única camada escondida alimentada para frente (Feedforward Multilayer Perceptron), para a previsão da volatilidade. Mais ainda, é proposto um modelo híbrido que combina o modelo GARCH e redes neurais. Os modelos GARCH e redes neurais são estimados para duas séries financeiras: Índice S&P500 e cotações do petróleo tipo Brent. Os resultados indicam que a volatilidade aproximada por redes neurais é muito semelhante as estimativas dos tradicionais modelos GARCH. Suas diferenças são mais qualitativas, na forma de resposta da volatilidade estimada a choques de maior magnitude e sua suavidade, do que quantitativas, apresentando critérios de erros de previsão em relação a uma medida de volatilidade benchmark muito próximos. / The financial time series are characterized by complex and non-linear behaviors. In addition to the financial market trend in prices their variability or volatility, a risk estimate, is important information for the market players. Neural networks are flexible nonlinear models capable of describing functions of different classes, having the property of universal approximators. This paper employs neural networks, specifically one hidden layer feedforward Multilayer Perceptron, for volatility forecasting. Moreover, we propose a hybrid model that combines the GARCH model with neural networks. The GARCH and neural network models are estimated over two financial series: the S&P500 composite index and prices of Brent oil. The results indicate that the volatility approximated by neural networks is very similar to that estimated by the traditional GARCH models, while their differences are more qualitative than quantitative, with information content that differs from and complements each other for different market environments.

Neste trabalho analisaremos a utilização dos modelos de mudança de regime markoviano para a variância condicional. Estes modelos podem estimar de maneira fácil e inteligente a variância condicional não observada em função da variância anterior e do regime. Isso porque, é razoável ter coeficientes variando no tempo dependendo do regime correspondentes à persistência da variância (variância anterior) e às inovações. A noção de que uma série econômica possa ter alguma variação na sua estrutura é antiga para os economistas. Marcucci (2005) comparou diferentes modelos com e sem mudança de regime em termos de sua capacidade para descrever e predizer a volatilidade do mercado de valores dos EUA. O trabalho de Hamilton (1989) foi uns dos mais importantes para o desenvolvimento de modelos com mudança de regime. Inicialmente mostrou que a série do PIB dos EUA pode ser modelada como um processo que tem duas formas diferentes, uma na qual a economia encontra-se em crescimento e a outra durante a recessão. O câmbio de uma fase para outra da economia pode seguir uma cadeia de Markov de primeira ordem. Utilizamos as séries de índice Bovespa e S&P500 entre janeiro de 2003 e abril de 2012 e ajustamos o modelo GARCH(1,1) com mudança de regime seguindo uma cadeia de Markov de primeira ordem, considerando dois regimes. Foram consideradas as distribuições gaussiana, t de Student e generalizada do erro (GED) para modelar as inovações. A distribuição t de Student com mesmo grau de liberdade para ambos os regimes e graus distintos se mostrou superior à distribuição normal para caracterizar a distribuição dos retornos em relação ao modelo GARCH com mudança de regime. Além disso, verificou-se um ganho no percentual de cobertura dos intervalos de confiança para a distribuição normal, bem como para a distribuição t de Student com mesmo grau de liberdade para ambos os regimes e graus distintos, em relação ao modelo GARCH com mudança de regime quando comparado ao modelo GARCH usual. / In this work we analyze heterocedastic financial data using Markov regime switching models for conditional variance. These models can estimate easily the unobserved conditional variance as function of the previous variance and the regime. It is reasonable to have time-varying coefficients corresponding to the persistence of variance (previous variance) and innovations. The economic series notion may have some variation in their structure is usual for economists. Marcucci (2005) compared different models with and without regime switching in terms of their ability to describe and predict the volatility of the U.S. market. The Hamiltons (1989) work was the most important one in the regime switching models development. Initially showed that the series of U.S. GDP can be modeled as a process that has two different forms one in which the economy is growing and the other during the recession. The change from one phase to another economy can follow a Markov first order chain. We use the Bovespa series index and S&P500 between January 2003 and April 2012 and fitted the GARCH (1,1) models with regime switching following a Markov first order chain, considering two regimes. We considered Gaussian distribution, Student-t and generalized error (GED) to model innovations. The t-Student distribution with the same freedom degree for both regimes and distinct degrees showed higher than normal distribution for characterizing the distribution of returns relative to the GARCH model with regime switching. In addition, there was a gain in the percentage of coverage of the confidence intervals for the normal distribution, as well as the t-Student distribution with the same freedom degree for both regimes and distinct degrees related to GARCH model with regime switching when compared to the usual GARCH model.

A estimação e previsão da volatilidade de ativos são de suma importância para os mercados financeiros. Temas como risco e incerteza na teoria econômica incentivaram a procura por métodos capazes de modelar a variância condicional que evolui ao longo do tempo. O objetivo central desta dissertação foi modelar via modelos ARCH – GARCH e modelos aditivos o índice do IBOVESPA e o ativo PETR4 para analisar a existência de correlação entre as volatilidades estimadas. A estimação da volatilidade dos ativos no método paramétrico foi realizada via modelos EGARCH; já para o método não paramétrico, utilizouse os modelos aditivos com 5 defasagens. / Volatility estimation and forecasting are very important matters for the financial markets. Themes like risk and uncertainty in modern economic theory have encouraged the search for methods that allow for modeling of time varying variances. The main objective of this dissertation was estimate through GARCH models and additive models of IBOVESPA and PETR4 assets; and analyzes the existence of correlation between volatilities estimated. We use EGARCH models to estimate through parametric methods and use additive models 5 to estimate non parametric methods.

Uma das informações mais importantes no mercado financeiro é a variabilidade de um ativo. Diversos modelos foram propostos na literatura com o intuito de avaliar este fenômeno. Dentre eles podemos destacar os modelos GARCH. Este trabalho propõe o uso do método Monte Carlo Hamiltoniano (HMC) para a estimação dos parâmetros do modelo GARCH univariado e multivariado. Estudos de simulação são realizados e as estimativas comparadas com o método de estimação Metropolis-Hastings presente no pacote BayesDccGarch. Além disso, compara-se os resultados do método HMC com a metodologia adotada no pacote rstan. Por fim, é realizado uma aplicação a dados reais utilizando o DCC-GARCH bivariado e os métodos de estimação HMC e Metropolis-Hastings. / One of the most important informations in financial market is variability of an asset. Several models have been proposed in literature with a view of to evaluate this phenomenon. Among them we have the GARCH models. This paper use Hamiltonian Monte Carlo (HMC) methods for estimation of parameters univariate and multivariate GARCH models. Simulation studies are performed and the estimatives compared with Metropolis-Hastings methods of the BayesDcc- Garch package. Also, we compared the results of HMC method with the methodology present in rstan package. Finally, a application with real data is performed using bivariate DCC-GARCH and the methods of estimation HMC and Metropolis-Hastings.

<p>The objective for this master thesis is to investigate the possibility to predict the risk of stocks in financial markets. The data used for model estimation has been gathered from different branches and different European countries. The four data series that are used in the estimation are price series from: Münchner Rück, Suez-Lyonnaise des Eaux, Volkswagen and OMX, a Swedish stock index. The risk prediction is done with univariate GARCH models. GARCH models are estimated and validated for these four data series. </p><p>Conclusions are drawn regarding different GARCH models, their numbers of lags and distributions. The model that performs best, out-of-sample, is the APARCH model but the standard GARCH is also a good choice. The use of non-normal distributions is not clearly supported. The result from this master thesis could be used in option pricing, hedging strategies and portfolio selection.</p>