Cette thèse étudie le risque de crédit souverain et son impact sur les banques et les entreprises. Le premier essai montre que le risque de crédit bancaire est lié au risque de crédit souverain via l’exposition commune au risque systémique au lieu du sauvetage implicite ou de l’exposition excessive aux obligations émises par le pays d’origine. Dans le deuxième essai, je construis un modèle de structure du capital qui prédit une corrélation négative entre le niveau d’endettement des grands entreprises et le risque de crédit souverain à cause du sauvetage implicite. Cette prédiction est confirmée en suite par des preuves empiriques des entreprises dans la zone euro. Le troisième essai donne un modèle joint de CDS et d’obligation pour identifier les composantes de défaut et de liquidité dans les spreads de CDS et les rendements obligataires. Je trouve une composante de liquidité importante dans les spreads de CDS des pays périphériques de la zone euros et conclus que le fait de ne pas prendre en compte de l’illiquidité des CDS conduit à surestimer la composante de défaut dans le rendement obligataire. / This thesis studies sovereign credit risk and its impact on banks and industrial firms. The first essay shows that bank credit risk is linked to sovereign credit risk through common exposure to systemic risk instead of implicit bailout or excessive holding of home country bonds. In the second essay, I build a trade-off model of capital structure which predicts negative correlation between optimal leverage of big firms and sovereign credit risk due to implicit bailout. The model prediction is confirmed by empirical evidence from firms in the euro area. The third essay provides a joint pricing model of CDS and bond to disentangle the default and liquidity component in CDS spread and bond yield spread. I find a remarkable liquidity component in the CDS spreads of peripheral euro area countries and conclude that ignoring CDS illiquidity leads to overestimation of default component in bond yield.

[pt] A análise de agrupamento não supervisionado, o processo que consistem em agrupar conjuntos de pontos de acordo com um ou mais critérios de similaridade, tem desempenhado um papel essencial em vários campos. O dois algoritmos mais populares para esse processão são o k-means e o Gaussian Mixture Models (GMM). O primeiro atribui cada ponto a um único cluster e usa a distância Euclidiana como similaridade. O último determina uma matriz de probabilidade de pontos pertencentes a clusters, e usa distância de Mahalanobis como similaridade. Além da diferença no método de atribuição - a chamada atribuição hard para o primeiro e a atribuição soft para o último - os algoritmos também diferem em relação à estrutura do cluster, ou forma: o k-means considera estruturas esféricas no dados; enquanto o GMM considera elipsoidais através da estimação de matrizes de covariância. Neste trabalho, um problema de otimização matemática que combina a atribuição hard com a estrutura do cluster elipsoidal é detalhado e formulado. Uma vez que a estimativa da covariância desempenha um papel importante no comportamento de estruturas agrupamentos elipsoidais, técnicas de regularizações são exploradas. Neste contexto, dois métodos de meta-heurística, uma perturbação Random Swap e um algoritmo híbrido genético, são adaptados, e seu impacto na melhoria do desempenho dos métodos é estudado. O objetivo central dividido em três: compreender as condições em que as estruturas de agrupamento elipsoidais são mais benéficas que as esféricas; determinar o impacto da estimativa de covariância com os métodos de regularização; e analisar o efeito das meta-heurísticas de otimização global na análise de agrupamento não supervisionado. Finalmente, a fim de fornecer bases para a comparação das presentes descobertas com futuros trabalhos relacionados, foi gerada uma base de dados com um extenso benchmark contendo análise das variações de diferentes tamanhos, formas, número de grupos e separabilidade, e seu impacto nos resultados de diferentes algoritmos de agrupamento. Além disso, pacotes escritos na linguagem Julia foram disponibilizados com os algoritmos estudados ao longo deste trabalho. / [en] Unsupervised cluster analysis, the process of grouping sets of points according to one or more similarity criteria, plays an essential role in various fields. The two most popular algorithms for this process are the k-means and the Gaussian Mixture Models (GMM). The former assigns each point to a single cluster and uses Euclidean distance as similarity. The latter determines a probability matrix of points to belong to clusters, and the Mahalanobis distance is the underlying similarity. Apart from the difference in the assignment method - the so-called hard assignment for the former and soft assignment for the latter - the algorithms also differ concerning the cluster structure, or shape: the k-means considers spherical structures in the data; while the GMM considers ellipsoidal ones through the estimation of covariance matrices. In this work, a mathematical optimization problem that combines the hard assignment with the ellipsoidal cluster structure is detailed and formulated. Since the estimation of the covariance plays a major role in the behavior of ellipsoidal cluster structures, regularization techniques are explored. In this context, two meta-heuristic methods, a Random Swap perturbation and a hybrid genetic algorithm, are adapted, and their impact on the improvement of the performance of the methods is studied. The central objective is three-fold: to gain an understanding of the conditions in which ellipsoidal clustering structures are more beneficial than spherical ones; to determine the impact of covariance estimation with regularization methods; and to analyze the effect of global optimization meta-heuristics on unsupervised cluster analysis. Finally, in order to provide grounds for comparison of the present findings to future related works, a database was generated together with an extensive benchmark containing an analysis of the variations of different sizes, shapes, number of clusters, and separability and their impact on the results of different clustering algorithms. Furthermore, packages written in the Julia language have been made available with the algorithms studied throughout this work.

[pt] Este trabalho analisa a potencial formação do fenômeno da “curva do pato” sobre os preços horários de energia no Brasil. Tal fenômeno se dá pela introdução massiva de geração solar na matriz energética e produz um afundamento nos preços de energia nos horários diurnos onde a produção de energia é abundante e uma rápida elevação dos preços no início da noite que carece de tal fonte de energia e apresenta picos de demanda. Este efeito tem produzido custos elevados aos operadores sistemas elétricos em diversos mercados internacionais como California, Austrália, Alemanha e, principalmente, reduzido a rentabilidade das usinas de geração solar que produzem energia em horários de preço baixo e precisam comprar energia em horários de preços altos. Este estudo recorreu a mais de cinquenta artigos e literaturas além de analisar minuciosamente o Plano Decenal de Energia (EPE, 2022b) como fonte principal para simulações de preço. Também contou com a contribuição de quatro especialistas do setor elétrico Brasileiro que avaliaram o entorno macro e aspectos da atratividade da indústria. Por fim, o trabalho conclui que a curva do pato no Brasil começará a se formar em 2026 tendendo a chegar a uma formação mais evidente no ano de 2031. Contudo, estima-se que o prêmio de risco de mercado para um agente de geração solar mitigar esse risco através de um contrato de swap seria menor que 10 por cento do preço de face de um contrato de energia de longo prazo. / [en] This thesis analyzes the potential formation of the duck curve phenomena on the hourly energy prices in Brazil. Such occurs by the massive introduction of solar generation into the energy matrix and produces a reduction in energy prices during the daytime hours where energy production is abundant and a steep increase in prices in the evening which lacks solar energy source and peaks on load demand. This effect has produced high costs for electrical system operators in several international markets such as California, Australia, Germany and, mainly, reduced the profitability of solar generation plants that produce energy at low price period and need to buy energy at high price hours. This study used more than fifty articles and literatures in addition to thoroughly analyzing the Ten-year Energy Plan (EPE, 2022b) as the main premise for price simulations. It also had the contribution of four specialists from the Brazilian electricity sector who evaluated the macro environment and the industry attractivity. Finally, the work concludes that the duck curve in Brazil will begin to form in 2026 tending to reach a more evident formation in the year 2031. However, it is estimated that the market risk premium for a solar generation agent to mitigate this risk through a swap contract would be less than 10 per cent of the face price of a long-term energy contract.

Cancer metastasis accounts for approximately 90% of all cancer-related deaths; however, the underlying mechanisms remain largely unknown. It has been known proteins that control F-actin dynamics are crucial for cancer metastasis. In this study, we revealed how an F-actin binding protein, Switch-associated protein 70 (SWAP-70), contributes to breast cancer metastasis. Moreover, immunotherapy is a promising approach to treat metastatic cancer cells by enhancing the function of the host immune system against cancer. Our lab has conducted extensive studies on how SWAP-70 regulates the function of several immune cell types, including dendritic cells (DCs), B cells, and mast cells. These cells have been reported to contribute to tumor immunity. Thus, we hypothesized that SWAP-70 plays a role in tumor immunity. To characterize the function of SWAP-70 in metastasis, we generated 4T1, mouse breast cancer, SWAP-70 knockout (KO) cells using Crispr/Cas9 technology. A syngeneic orthotopic model was used to recapitulate clinical disease progression, and the results showed that SWAP-70 led to significant metastasis to the lungs and bones in immunocompetent mice. Several functional assays have revealed that SWAP-70 promotes anchorage-independent growth, cell migration, invasion, and adhesion in 4T1 cells. Biophysical measurements showed that SWAP-70 contributes to cellular mechanics. To investigate how SWAP-70 in host cells affects tumor immunity, SWAP-70 deficient mice were injected with E0771 mouse breast cancer cells to study tumorigenicity. SWAP-70 deficient mice showed delayed primary tumor growth and less distant metastasis. Isolated SWAP-70−/− DCs were impaired in generating CD8 T cell responses pulsed with soluble OVA protein, but not with OVA peptide, suggesting that the antigen uptake, processing, and presentation process in SWAP-70−/− DCs may be diminished. Taken together, our findings describe the potential mechanisms by which the loss of SWAP-70 hinders cancer metastasis and provide several insights into how targeting SWAP-70 could be a potential therapeutic approach to target cancer.

研究動機 交換交易為新興財務處理工具，屬於衍生性金融商品一種，可使企業降低資金成本及規避財務風險，自一九八一年世界銀行與IBM公司簽訂首筆貨幣交換交易之後，市場迅速發展。惟因需金融財務處理、證券發行及外匯交易等專業知識技術，牽涉相關法律、會計稅務等問題增加複雜性。又其屬於資產負債表外項目，不受相關法規牽制，參與者可依照個別需求選擇操作期間及方式，相對於其他標準化的期貨契約等可提供多樣選擇與長期的避險功能，但由於資訊不易公開揭露，容易產生法律與管理風險。 一九九五年二月霸菱銀行因衍生性金融商品交易操作不當，導致該銀行破產轉讓，以及一九九五年一月我國華僑銀行在外國債權投資與利率交換交易上遭受逾六千萬美元的帳面損失，致使國內金融主管單位與監理機構對其加強管理。一九九八年LTCM長期資本管理公司之避險基金在同年八月底，該基金契約之總面值在交換交易超過七千五百億美元、期貨交易超過五千億美元、選擇權和店頭市場衍生性商品交易超過一千五百億美元，由於異常槓桿操作、資訊揭露不足、忽視流動性風險、操作策略等因素，導致破產並造成前十七大交易對手所承受的實質損失金額總數約在三十至五十億美元之間。 由於交易型態不斷地推陳出新，法律屬性難以定位，使其存有高度法律不確定性，以美國為例，除管轄爭議外，鑒於店頭衍生性商品之高度風險，交易適當性也廣受討論。為了解上述業務開放可能產生的問題，並因應金融國際化的趨勢，有深入認識交換交易之必要，是以本文擬就交換交易可能涉及之法律問題加以探討研究。 研究範圍 交換交易可利用信用評等不同所產生的比較利益來進行，因此進行交易的任何一方均可獲利，傳統上視其為增加遊戲；與期貨與選擇權有所不同，期貨與選擇權除帶有投機性質外，係一方獲利以他方損失為代價之零和遊戲。又交換交易係屬資產負債表外之項目，不受相關法規限制，參與者可依照需要選擇操作期間與方式，相較於其他已經標準化的避險工具可提供多樣與長期避險功能，但由於不受法規牽制，交易本質並無要求標準化契約及特定交易方式，相對交易風險較高。本文擬就下列問題加以介紹： （一）交換交易之定義、種類、功能、特色 （二）ISDA標準化契約內容 （三）美國法院判決分析交換交易之法律性質 （四）美國與我國相關法令規範分析 第一章為緒論，介紹論文研究動機、範圍。第二章為交換交易類型介紹，並進一步介紹現行實務廣泛使用ISDA標準化契約內容。 第三章由美國判決角度探討交換交易法律性質，由判決所建立各項標準討論交換交易是否屬於美國證交法與證券交易法之「證券」。 第四章介紹美國管理機關與法規制度，說明交換交易管轄範圍爭議，主管機關間如何協調達成協議，與美國對於新交易類型權益交換所作修正之規範。 第五章介紹我國證券金融期貨法規範，鑑於諸多交易類型不斷變化衍生，其特性與交易模式於立法層面恐非立法者於制定證券交易法當時所能預料導致現行證券交易法些許規定在規範面與實際適用可能會產生漏洞。現行市場商品有諸多由第三人為發行機構，標的資產與股權或債券相連結之金融工具如股權連結商品可能與權益交換產生相同的問題，是以本文由權益交換出發討論其與證券交易法有價證券之關係，與內部人短線交易法律層面法律漏洞，以供我國證券交易法未來之參考。第六章為結論。

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This diploma thesis focuses on reporting of financial derivatives under the rules set by International Financial Reporting Standards (IFRS). It analyses the hedging transactions, but it also aims at the speculations. From the range of IFRS hedging transactions closely describes Fair Value hedge and Cash Flow hedge. Furthermore it deals with the most frequent hedging cases like the hedging of foreign currency risk and interest rate risk. The real historic data from the markets are applied in his diploma paper.

This dissertation analyzes key features of credit derivatives market, basic risks of the products and trends the market has experienced for several years since its inception, discusses regulatory issues of the market with regard to the Basel II treatment and key reasons for investors using credit derivatives. Dissertation also examines whether and how credit derivatives affected current financial turmoil, analyzes credit derivatives losses of selected institutions on the financial markets and compares them with total losses of these institutions. The main result of the work is that there was no substantial effect of the credit derivatives market on the current financial crisis. Dissertation also examines whether there is any connection between U.S. banks credit derivatives trades and their capital adequacy ratio. According to the analysis, there is no evidence for credit derivatives to essentially affect capital adequacy ratio of U.S. banks. A potential explanation for the higher values of U.S. banks' capital adequacy ratio may be that there are sophisticated risk management strategies banks have been implicating for many years.

[pt] O mercado brasileiro de energia elétrica é composto basicamente de matrizes hidroelétricas e termoelétricas, sendo que seu fornecimento pode ser contratado em dois ambientes, um de contratação regulamentada e outro livre. Dessa forma o apreçamento da energia é algo complexo e com incertezas, pois seu modelo leva em consideração comportamentos de afluências futuras, além de estimar a utilização de termoelétricas, que possuem fontes de energia mais caras. No Brasil, existem quatro submercados que podem ter preços divergentes. Algumas comercializadoras se utilizam dessa diferença buscando aferir ganhos extraordinários fazendo Swaps. Essa operação consiste em compra e venda de uma mesma quantidade de energia com liquidação fixada em uma determinada data com o preço à vista entre diferentes submercados. Essas empresas utilizam medidas de otimização de carteiras e controle de risco para fazerem operações ótimas, onde há maior probabilidade de maximizar o lucro, tendo o prejuízo máximo sob controle. Esse trabalho tem como objetivo encontrar a carteira de Swaps de energia que maximiza a medida Ômega, usada como avaliador de desempenho, tendo uma expectativa de lucro e com uma restrição de risco com um limite para o Conditional Value at Risk (CVaR), assim auxiliando as comercializadoras a maximizarem seu lucro não ultrapassando seu limite de risco. O estudo levou em consideração valores de previsão reais feitos por modelos fornecidos por órgãos especializados, levando em consideração os dados para os anos de 2012 e 2013 sendo estudadas todas as combinações possíveis de Swaps para a composição da carteira ótima para cada um dos anos estudados. A carteira ótima foi encontrada, no entanto, pode-se concluir que sua composição varia de acordo com os dados simulados não existindo assim uma carteira ótima única devendo essa ser calculada caso a caso. / [en] The Brazilian energy market is composed basically by hydroelectric and thermoelectric energy sources, which can be contracted in two different environments, one regulated and the other free. In this way, the pricing of energy is something complex and uncertain, because its model takes in consideration the behavior of future water affluences, besides estimating the more expensive thermal units. In Brazil, there are four submarkets that have diverging prices and some traders use this difference to reach extraordinary gains by entering into Swap operations. This operation consists of buying and selling a same amount of energy with its liquidation fixed at pre-determined date, at a spot price between different submarkets. These companies use portfolio optimization and risk management methods to reach optimal operations, in which there is a greater probability of maximizing profits, while measuring risk. This study aim to find the portfolio of Swaps that`s maximize the Omega measurement as the performance measurement, has a estimated profit and uses the conditional Value at Risk (CVaR) as the restriction for the risk that can be taken. Its objective is to help traders maximize their profit without exceeding their risk limit. The study took in consideration values from real previsions made by models provided by specialized agencies, taking in consideration all the data for the years of 2012 and 2013, with all the combinations of Swaps being studied. The optimal portfolio was achieved in both cases however, it`s possible to conclude that this composition varies according the input data, not existing thereby a unique optimal portfolio should that be calculated by case.

The credit derivatives market has known an incredible development since its advent in the 1990's. Today there is a plethora of credit derivatives going from the simplest ones, credit default swaps (CDS), to more complex ones such as synthetic single-tranche collateralized debt obligations. Valuing this rich panel of products involves modeling credit risk. For this purpose, two main approaches have been explored and proposed since 1976. The first approach is the Structural approach, first proposed by Merton in 1976, following the work of Black-Scholes for pricing stock options. This approach relies in the capital structure of a firm to model its probability of default. The other approach is called the Reduced-form approach or the hazard rate approach. It is pioneered by Duffie, Lando, Jarrow among others. The main thesis in this approach is that default should be modeled as a jump process. The objective of this work is to value Asset-backed Credit default swaps using the hazard rate approach.