Cette thèse se propose d’analyser la structure et la dynamique de dépendance de crédit des institutions financières aux Etats-Unis et en Europe durant la crise financière de 2008. Un premier chapitre présente une revue de la littérature des modèles multi-dimensionnels de crédit et des modèles économétriques de contagion financière. Ce chapitre a pour vocation de guider notre réflexion à la fois conceptuelle et méthodologique sur les hypothèses analytiques de la contagion ainsi que ses méthodes de mesure. Nous montrons que si la contagion est devenue une hypothèse centrale des modèles multivariés de risque de crédit, il n’en reste néanmoins que sa définition et sa quantification ne font pas l’objet de consensus dans la littérature. Un deuxième chapitre propose une analyse empirique des co-movements des rendements de CDS de banques et sociétés d’assurance américaines et européennes. La dissociation de leur structure de dépendance entre association linéaire et dépendances extrêmes nous permet de mettre en évidence des phénomènes d'interconnexions entre institutions financières apparues au courant de la crise et véhiculant ainsi sous l'effet de la contagion, un risque systémique croissant. Un dernier chapitre présente une interprétation économique des résultats obtenus dans notre deuxième chapitre. En particulier, nous cherchons à quantifier l'influence jouée par la contagion et les facteurs de risques communs sur la dynamique de dépendance extrême des institutions financières. Nous démontrons ainsi le rôle du risque de contrepartie, du risque de liquidité et du risque de défaut des institutions financières dans la transmission de la contagion sur le marché de CDS. / This thesis examines the credit dependence structure and dynamics of financial institutions in the U.S. and Europe amid the recent financial crisis. A first chapter presents a survey of multi-name models of credit risk and econometric models of financial contagion with the purpose of guiding both the analytical and conceptual assumptions and econometric modelling techniques we use in the subsequent chapters. We show that if contagion has become a central cornerstone of multi-name models of credit risk, there is nonetheless a lack of consensus on the way to both define and measure it. A second chapter presents the results of an empirical analysis of U.S. and European banks and insurance companies’ CDS return extreme co-movements. By uncovering financial institutions' linear as well as extremal dependence structures, we provide evidence that their credit dependence has strengthened during the crisis, thereby effectively conveying, in the face of extreme tail events, potential systemic risks. A third and last chapter provides an economic rationale of the results presented in our second chapter. In particular, we examine the impact of common risk factors and contagion on the dynamics of financial institutions' extremal credit dependence. We demonstrate the role of counterparty risk and liquidity risk, as well the repricing by market participants since July 2007 of their jump-to-default premia as additional channels driving financial institutions' increased dependence and amplifying contagion on the CDS market.

Cette thèse a pour ambition d’analyser si la performance environnementale, sociale et de gouvernance (ESG) est intégrée par les marchés de la dette d'entreprise et souveraine. Le premier chapitre se concentre sur les informations ESG publiés à contenu négatif et leur impact négatif sur le coût de la dette. Plus exactement, dans les secteurs industriels et utilitaires les événements négatifs sociaux et de gouvernance font augmenter le coût de la dette. Également, un bon niveau général de performance ESG agit comme un mécanisme d'assurance contre ces événements négatifs. Dans un deuxième chapitre seront présentés les résultats d’une simulation de portefeuille intégrant la performance ESG d'entreprise. Un gérant de portefeuille peut améliorer le niveau agrégé de la performance ESG du portefeuille de 1,5 écart-type sans faire baisser la performance financière. Ainsi, le gérant peut combiner cette intégration avec des stratégies d'allocation d'actif financiers ou des stratégies de rendement absolu. Dans un troisième chapitre les résultats sur la réduction du coût de la dette dû à une bonne performance environnementale et sociale de souverains émergents seront analysés. Enfin dans le quatrième chapitre je décris comment la performance de gouvernance des souverains influence la différence entre le yield émis en devise étrangère et celui émis en devise locale. Dans les pays développés cette différence augmente avec le risque politique, i.e. le yield étranger augmente plus rapidement que le yield domestique. Dans les pays émergents, c'est l’effet inverse qui est observé. Cette différence entre les deux yields varie plus fortement avec un taux croissant de la dette domestique détenue par des investisseurs étrangers. / This thesis analyzes if and to what extent debt markets value the environmental, social and governance (ESG) performance of firms and sovereigns. The first chapter shows that negative ESG news has a negative impact on the cost of debt of firms. The news relates to environmental and social events within the industrial/utilities sector. In this sector, a sound corporate social performance acts as an insurance against the adverse impact of negative environmental events on bond prices. The second chapter reveals that ESG scores integrated into portfolios do not change the financial performance ex post. A portfolio manager can increase the average ESG rating of her portfolio by 1.5 standard deviations without incurring cost. This leaves substantial room and opportunity for ESG ratings to be combined with asset allocation or absolute return strategies. The third chapter shows how ESG performance is linked to a lower cost of debt of emerging sovereigns. Research indicates that an emerging country’s average cost of capital decreases with its positive environmental and social performance. The fourth chapter discusses how governance performance may influence the spread of debt denominated in local and foreign currency. In developed countries, the spread between a foreign currency yield and a hedged local currency yield increases with our political risk indicator, i.e. the foreign yield increases faster than the domestic one. For emerging countries, the reverse trend is true. Interestingly, the foreign currency and local currency yield spreads move significantly stronger in absolute terms with increasing foreign investment participation in both emerging countries and developed countries’ debt markets.

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台灣早期有關系統風險（β）的研究皆指出β不能解釋台灣股票報酬的變異，故控尋更能解釋股票報酬的風險因素為本文的主要目的之一。 本研究分析民國71年7月至85年5月股票上市公司資料（排除金融、保險、及變更交易方式的公司）。因民國79年股價指數從2月的最高點12,495急遽下滑至10月的2,560，故分析上將79年度予以排除。在71年7月至78年12月的時段中，整體市場因素（RM-RF）不能解釋股票報酬的變異。此點發現與台灣早期研究的結論一致。其他變數顯著者僅有與規模有關的因素（SZSMB），或與負債比率有關的因素（DEHML），其中以 SZSMB的解釋能力最強。在民國80年1月至85年5月的時段中，所有模式中整體市場因素（ RM-RF）的係數皆顯著，並且是所有因素中最顯著者。這點發現與前時段（71年7月至78年12月）的結果有很大的不同。其他的變數顯著者，有代表成長機會的BMHML（與淨值市價比有關的因素）、EPHML（與益本比有關的因素）、或CPHML（與營運現金市價比有關的因素），及代表利率結構有關的風險因素TERM（與利率期間結構有關的風險溢酬）、或DFT（與利率違約風險有關的風險溢酬）。其中以（RM-RF）、EPHML、CPHML及TERM的風險組合最能解釋股票報酬的變異。 應用更完整的股票報酬解釋變數，探討股票報酬與盈餘間的關係，亦為本文主要目的之一。經分析以(1)各時段最能解釋股票報酬的因素組合為基礎，計算異常報酬；(2)單獨的以整體市場因素（RM-RF）為基礎計算異常報酬，然後再分別估出盈餘反應比較係數（ERC）比較之。結果顯示，以各時段最能顯著解釋股票報酬的因素組合為基礎的ERC為正的顯著，且其ERC大於只以整體市場因素（RM-RF）為基礎所算出的ERC。 另外，關於盈餘品質假說之測試，經以公司規模大小為虛擬變數放入迴歸式中，結果顯示，代表大公司的虛擬變數之係數時而為正，時而為負，且都不顯著，故盈餘品質假說未獲得支持。 再者，關於成長機會與ERC關係之測試，經以公司成長機會大小為虛擬變數放迴歸式中，結果顯示，代表成長機會的虛擬變數之系數時而為正，時而為負，且大都不顯著，故成長機會大的公司之ERC大於成長機會小的公司之ERC的假說，未獲得實證的支持。 / Earlier studies (Chen 1990; Chiu 1990; and Wang 1992) found that systematic risk (β) could not explain the variance of stock returns in Taiwan. The findings were inconsistent with the Capital Asset Pricing Model (CAPM). One of the major purposes of this paper is to examine the factors that have higher explanatory power of stock returns. To test the hypotheses, this study uses the data of Taiwanese listed companies covering the period from July 1982 to may 1996. The 1990 data are excluded because the stock market index climbed to a record high of 12,495 in February 1990 and then fell sharply to allow level of 2,560 in October 1990. The "crash" might cause structural changes in stock market, so the analyses are conducted separately for the periods before and after the crash, namely the prior-crash period (from July 1982 to December 1989) and the post-crash period (from January 1991 to May 1996). The empirical results show that for the prior-crash period the overall market factor (market returns minus risk free rate, RM-RF) can not explain the variance of stock returns. The findings are consistent with those of previous studies. However, we find that the factor-related to size (SZSMB) and the factor related to debt/equity ratio (DEHML) have significant association with stock returns. Furthermore, SZSMB has higher explanatory power. In contrast, the overall market factor is the most significant factor for the post-crash period. Other factors that are significant consisted of (1) proxies for growth opportunities, including book-to-market equity (BMHML), earnings/price ratio (EPHML), and cash flow/price ratio (CPHML), and (2) the factors related to interest structure, including term structure (TERM) and default risk (DFT). Among these factors, the set of RM-RF, EPHML, CPHML, and TERM explains the variance of stock returns most. Another purpose of this paper is to use the aforementioned findings to study the relationship between stock returns and earnings. The results show that the earnings response coefficients based on the most explanatory factor portfolio of each period are positive and significant, and are greater than those based on the traditional systematic risk (β). The tests for earnings quality hypothesis indicate that the coefficients of the dummy variable proxies for big companies are insignificant. The earnings quality hypothesis is not supported. The tests regarding the relationship between growth opportunities and earnings response coefficients show that the coefficients of the dummy variable proxies for high growth companies are unstable. The hypothesis that the earnings response coefficients of high growth companies are greater than those of low growth companies is not supported by empirical evidence.

Nesta dissertação, o modelo de seleção de carteiras de Markowitz será estendido com uma análise em tempo discreto e hipóteses mais realísticas. Um produto tensorial finito de densidades Erlang será usado para aproximar a densidade de probabilidade multivariada dos retornos discretos uniperiódicos de ativos dependentes. A Erlang é um caso particular da distribuição Gama. Uma mistura finita pode gerar densidades multimodais não-simétricas e o produto tensorial generaliza este conceito para dimensões maiores. Assumindo que a densidade multivariada foi independente e identicamente distribuída (i.i.d.) no passado, a aproximação pode ser calibrada com dados históricos usando o critério da máxima verossimilhança. Este é um problema de otimização em larga escala, mas com uma estrutura especial. Assumindo que esta densidade multivariada será i.i.d. no futuro, então a densidade dos retornos discretos de uma carteira de ativos com pesos não-negativos será uma mistura finita de densidades Erlang. O risco será calculado com a medida Downside Risk, que é convexa para determinados parâmetros, não é baseada em quantis, não causa a subestimação do risco e torna os problemas de otimização uni e multiperiódico convexos. O retorno discreto é uma variável aleatória multiplicativa ao longo do tempo. A distribuição multiperiódica dos retornos discretos de uma seqüência de T carteiras será uma mistura finita de distribuições Meijer G. Após uma mudança na medida de probabilidade para a composta média, é possível calcular o risco e o retorno, que levará à fronteira eficiente multiperiódica, na qual cada ponto representa uma ou mais seqüências ordenadas de T carteiras. As carteiras de cada seqüência devem ser calculadas do futuro para o presente, mantendo o retorno esperado no nível desejado, o qual pode ser função do tempo. Uma estratégia de alocação dinâmica de ativos é refazer os cálculos a cada período, usando as novas informações disponíveis. Se o horizonte de tempo tender a infinito, então a fronteira eficiente, na medida de probabilidade composta média, tenderá a um único ponto, dado pela carteira de Kelly, qualquer que seja a medida de risco. Para selecionar um dentre vários modelos de otimização de carteira, é necessário comparar seus desempenhos relativos. A fronteira eficiente de cada modelo deve ser traçada em seu respectivo gráfico. Como os pesos dos ativos das carteiras sobre estas curvas são conhecidos, é possível traçar todas as curvas em um mesmo gráfico. Para um dado retorno esperado, as carteiras eficientes dos modelos podem ser calculadas, e os retornos realizados e suas diferenças ao longo de um backtest podem ser comparados. / In this thesis, Markowitz’s portfolio selection model will be extended by means of a discrete time analysis and more realistic hypotheses. A finite tensor product of Erlang densities will be used to approximate the multivariate probability density function of the single-period discrete returns of dependent assets. The Erlang is a particular case of the Gamma distribution. A finite mixture can generate multimodal asymmetric densities and the tensor product generalizes this concept to higher dimensions. Assuming that the multivariate density was independent and identically distributed (i.i.d.) in the past, the approximation can be calibrated with historical data using the maximum likelihood criterion. This is a large-scale optimization problem, but with a special structure. Assuming that this multivariate density will be i.i.d. in the future, then the density of the discrete returns of a portfolio of assets with nonnegative weights will be a finite mixture of Erlang densities. The risk will be calculated with the Downside Risk measure, which is convex for certain parameters, is not based on quantiles, does not cause risk underestimation and makes the single and multiperiod optimization problems convex. The discrete return is a multiplicative random variable along the time. The multiperiod distribution of the discrete returns of a sequence of T portfolios will be a finite mixture of Meijer G distributions. After a change of the distribution to the average compound, it is possible to calculate the risk and the return, which will lead to the multiperiod efficient frontier, where each point represents one or more ordered sequences of T portfolios. The portfolios of each sequence must be calculated from the future to the present, keeping the expected return at the desired level, which can be a function of time. A dynamic asset allocation strategy is to redo the calculations at each period, using new available information. If the time horizon tends to infinite, then the efficient frontier, in the average compound probability measure, will tend to only one point, given by the Kelly’s portfolio, whatever the risk measure is. To select one among several portfolio optimization models, it is necessary to compare their relative performances. The efficient frontier of each model must be plotted in its respective graph. As the weights of the assets of the portfolios on these curves are known, it is possible to plot all curves in the same graph. For a given expected return, the efficient portfolios of the models can be calculated, and the realized returns and their differences along a backtest can be compared.

Nesta dissertação, o modelo de seleção de carteiras de Markowitz será estendido com uma análise em tempo discreto e hipóteses mais realísticas. Um produto tensorial finito de densidades Erlang será usado para aproximar a densidade de probabilidade multivariada dos retornos discretos uniperiódicos de ativos dependentes. A Erlang é um caso particular da distribuição Gama. Uma mistura finita pode gerar densidades multimodais não-simétricas e o produto tensorial generaliza este conceito para dimensões maiores. Assumindo que a densidade multivariada foi independente e identicamente distribuída (i.i.d.) no passado, a aproximação pode ser calibrada com dados históricos usando o critério da máxima verossimilhança. Este é um problema de otimização em larga escala, mas com uma estrutura especial. Assumindo que esta densidade multivariada será i.i.d. no futuro, então a densidade dos retornos discretos de uma carteira de ativos com pesos não-negativos será uma mistura finita de densidades Erlang. O risco será calculado com a medida Downside Risk, que é convexa para determinados parâmetros, não é baseada em quantis, não causa a subestimação do risco e torna os problemas de otimização uni e multiperiódico convexos. O retorno discreto é uma variável aleatória multiplicativa ao longo do tempo. A distribuição multiperiódica dos retornos discretos de uma seqüência de T carteiras será uma mistura finita de distribuições Meijer G. Após uma mudança na medida de probabilidade para a composta média, é possível calcular o risco e o retorno, que levará à fronteira eficiente multiperiódica, na qual cada ponto representa uma ou mais seqüências ordenadas de T carteiras. As carteiras de cada seqüência devem ser calculadas do futuro para o presente, mantendo o retorno esperado no nível desejado, o qual pode ser função do tempo. Uma estratégia de alocação dinâmica de ativos é refazer os cálculos a cada período, usando as novas informações disponíveis. Se o horizonte de tempo tender a infinito, então a fronteira eficiente, na medida de probabilidade composta média, tenderá a um único ponto, dado pela carteira de Kelly, qualquer que seja a medida de risco. Para selecionar um dentre vários modelos de otimização de carteira, é necessário comparar seus desempenhos relativos. A fronteira eficiente de cada modelo deve ser traçada em seu respectivo gráfico. Como os pesos dos ativos das carteiras sobre estas curvas são conhecidos, é possível traçar todas as curvas em um mesmo gráfico. Para um dado retorno esperado, as carteiras eficientes dos modelos podem ser calculadas, e os retornos realizados e suas diferenças ao longo de um backtest podem ser comparados. / In this thesis, Markowitz’s portfolio selection model will be extended by means of a discrete time analysis and more realistic hypotheses. A finite tensor product of Erlang densities will be used to approximate the multivariate probability density function of the single-period discrete returns of dependent assets. The Erlang is a particular case of the Gamma distribution. A finite mixture can generate multimodal asymmetric densities and the tensor product generalizes this concept to higher dimensions. Assuming that the multivariate density was independent and identically distributed (i.i.d.) in the past, the approximation can be calibrated with historical data using the maximum likelihood criterion. This is a large-scale optimization problem, but with a special structure. Assuming that this multivariate density will be i.i.d. in the future, then the density of the discrete returns of a portfolio of assets with nonnegative weights will be a finite mixture of Erlang densities. The risk will be calculated with the Downside Risk measure, which is convex for certain parameters, is not based on quantiles, does not cause risk underestimation and makes the single and multiperiod optimization problems convex. The discrete return is a multiplicative random variable along the time. The multiperiod distribution of the discrete returns of a sequence of T portfolios will be a finite mixture of Meijer G distributions. After a change of the distribution to the average compound, it is possible to calculate the risk and the return, which will lead to the multiperiod efficient frontier, where each point represents one or more ordered sequences of T portfolios. The portfolios of each sequence must be calculated from the future to the present, keeping the expected return at the desired level, which can be a function of time. A dynamic asset allocation strategy is to redo the calculations at each period, using new available information. If the time horizon tends to infinite, then the efficient frontier, in the average compound probability measure, will tend to only one point, given by the Kelly’s portfolio, whatever the risk measure is. To select one among several portfolio optimization models, it is necessary to compare their relative performances. The efficient frontier of each model must be plotted in its respective graph. As the weights of the assets of the portfolios on these curves are known, it is possible to plot all curves in the same graph. For a given expected return, the efficient portfolios of the models can be calculated, and the realized returns and their differences along a backtest can be compared.