Este trabalho faz uma reconstituição histórica da política monetária praticada no Brasil desde a implementação do Plano Real, revisa uma determinada discussão teórica sobre o tema da taxa de juros brasileira e suas possíveis relações perversas com outras variáveis macroeconômicas, e apresenta um modelo para tentar captar esses possíveis efeitos perversos da política monetária, tais como descritos na maior parte dos trabalhos apontados na discussão teórica. No último decênio, a taxa de juros nominal doméstica sempre esteve acima dos 15% ao ano, sendo que em grande parte do período analisado, a taxa de juros real ficou acima deste patamar. Com efeito, essa condução da política monetária trouxe à tona determinados efeitos indesejados, tais como a contaminação do risco-País pela taxa de juros doméstica. Entre os principais resultados obtidos seguindo uma análise com base num modelo VAR em que se avaliam choques nas variáveis por meio de funções impulso-resposta generalizadas (GIR), encontra-se que o risco soberano brasileiro, no período pós-desvalorização cambial, tem como determinantes os fundamentos macroeconômicos, em particular variáveis fiscais, como a dívida líquida do setor público consolidado como proporção do PIB, e a participação da dívida externa como proporção da dívida total. Outro determinante do risco percebido de moratória é a taxa de juros nominal interna. Quanto mais elevada a taxa de juros, mais elevado o risco. Em terceiro lugar, um aumento da taxa de juros pode levar a uma desvalorização cambial, desde que as expectativas dos agentes sejam afetadas pelo aumento dos riscos provocados pela elevação dos juros. / This dissertation revisits the historical background of the monetary policy regime adopted in Brazil in the period after the implementation of the Real stabilization plan, addresses to a determined theoretical framework about the domestic interest rates and its possible undesired relations with other macroeconomic variables, and presents a model to capture these possible relations of monetary policy. In the last decade, domestic nominal interest rate have always been above 15% p.a., and in a significant period of time the real interest rate stood above this level. Therefore, the conduct of monetary policy has brought up some undesired effects, such as the contagion of the Country-Risk to the domestic interest rate. Amongst the main results obtained in this paper, using a VAR model in a Generalized Impulse Response (GIR) framework for the period after the adoption of the floating exchange rate regime, stands out that the sovereign risk of Brazil is determined by macroeconomic fundaments, especially fiscal variables such as the Net Debt of the Public Sector and the share of foreign debt in the total debt. Another significant determinant of the perceived risk of default is the domestic interest rate. The higher the domestic nominal interest rate, the higher the risk. Lastly, a domestic interest rate increase may take to exchange rate depreciation if expectations are affected by the augmented risk derived from the higher domestic interest rate.

Ratingsysteme sind ein zentraler Bestandteil der Kreditrisikomodellierung. Neben der Bonitätsbeurteilung auf der Ebene der Kreditnehmer und der Risikoquantifizierung auf der Ebene der Ratingklassen spielt dabei die Bildung der Ratingklassen eine wesentliche Rolle. Die Literatur zur Ratingklassenbildung setzt auf modellfreie, in gewisser Weise willkürliche Optimierungsverfahren. Ein Ziel der vorliegenden Arbeit ist es, stattdessen ein parametrisches statistisches Modell zur Bildung der Ratingklassen einzuführen. Ein geeignetes Modell ist im Bereich der Bruchpunktschätzung zu finden. Dieses Modell und die in der mathematischen Literatur vorgeschlagenen Parameter- und Intervallschätzer werden in der vorliegenden Arbeit dargestellt und gründlich diskutiert. Dabei wird Wert auf eine anwendungsnahe und anschauliche Formulierung der mathematisch-statistischen Sachverhalte gelegt. Anschließend wird die Methodik der Bruchpunktschätzung auf einen konkreten Datensatz angewendet und mit verschiedenen anderen Kriterien zur Ratingklassenbildung verglichen. Hier erweist sich die Bruchpunktschätzung als vorteilhaft. Aufbauend auf der empirischen Untersuchung wird abschließend weiterer Forschungsbedarf abgeleitet. Dazu werden insbesondere Konzepte für den Mehrklassenfall und für abhängige Daten entworfen. / Rating systems are a key component of credit risk modeling. In addition to scoring at borrowers’ level and risk quantification at the level of rating classes, the formation of the rating classes plays a fundamental role. The literature on rating classification uses in a way arbitrary optimization methods. Therefore, one aim of this contribution is to introduce a parametric statistical model to form the rating classes. A suitable model can be found in the area of split-point estimation. This model and the proposed parameter and interval estimators are presented and thoroughly discussed. Here, emphasis is placed on an application-oriented and intuitive formulation of the mathematical and statistical issues. Subsequently, the methodology of split-point estimation is applied to a specific data set and compared with several other criteria for rating classification. Here, split-point estimation proves to be advantageous. Finally, further research questions are derived on the basis of the empirical study. In particular, concepts for the case of more than two classes and for dependent data are sketched.

信用連結債券的價值主要取決於所連結資產池內的資產違約情況，因此過去有許多文獻在評價時會利用Copula模擬各資產的違約時點，或是用Factor Copula估算他們在各時點下的違約機率。而本研究以Gaussian Factor Copula模型為主軸，對資產池違約機率做估計，以得到連結該資產池的信用連結債券價值。但過去文獻較常以給定參數的方式進行評價，本研究進一步利用市場實際資料估出模型參數並加入產業因子，以期達到符合市場的效果。 本研究利用已知的違約資訊對照模型結果，發現在給定原油價格成長率、產業GDP成長率及CAPM殘差之後，使用Factor Copula模型在資產池小且違約比例過高時容易低估損失，主要原因在於各資產的違約機率並非逼近1。且模型算出的預期損失會隨著距今時間變長而增加，但若資產池實際上沒有更多違約公司，模型的結果就可能會高估損失。而所有的變數又以參考價差對該商品價值的影響最大，因參考價差的數值取決於該公司的信用評等，因此可知信用連結債券價值主要還是與各公司信評有最大相關。 / The value of credit linked notes depends on whether the reference entities in the linked asset pool default or not, so some previous studies used Copula model to simulate the times to default or Factor Copula model to get the default probability. In this paper, with the Gaussian Factor Copula model adopted and industry factors taken into account, the default probability is estimated in order to obtain the value of the credit linked notes. Then, unlike other previous studies using the given parameters, this paper evaluated the parameters by using the model as well as market data, hoping to achieve the goal that results can reflect the real market situation. With real default information compared with the modeling results, three findings can be drawn given the growth rate of oil price, the growth rate of industrial GDP and the residuals of CAPM. First, the loss will be underestimated if the asset pool is small and the default proportion is too high mainly because not all the default probability approximates one. Second, expected default probability will be directly proportional to the time period between the present and the expected moment. So if there are not so many defaulting companies, then the loss might be overestimated. Last, the reference spread has the most impact on the product value among all the variables, and as we know, the reference spread of a company depends on its credit rating. Therefore, compared with other factors, credit rating remains the most essential to credit linked notes.

Submitted by Rodrigo Botelho (rodrigobotelho@gmail.com) on 2013-01-15T13:24:36Z No. of bitstreams: 1 Tese Mestrado_RodrigoBotelho.pdf: 783241 bytes, checksum: 6ac3257d172dff384fca0c601e146aa4 (MD5) / Approved for entry into archive by Vitor Souza (vitor.souza@fgv.br) on 2013-01-15T14:50:33Z (GMT) No. of bitstreams: 1 Tese Mestrado_RodrigoBotelho.pdf: 783241 bytes, checksum: 6ac3257d172dff384fca0c601e146aa4 (MD5) / Made available in DSpace on 2013-02-04T16:52:43Z (GMT). No. of bitstreams: 1 Tese Mestrado_RodrigoBotelho.pdf: 783241 bytes, checksum: 6ac3257d172dff384fca0c601e146aa4 (MD5) Previous issue date: 2013-11-27 / This paper explores the sovereign default due to the structure of Credit Default Swap spreads. These spreads show the default probability of a country. The methodology proposed in this paper applied for Argentina, Korea, Ecuador, Indonesia, Mexico, Peru, Turkey, Ukraine, Venezuela and Rússia. We could show that a single factor model following a lognormal process captures the probability of default. We also show that the macro economic variables like inflation, unemployment e growth do not explain the dependent variable of this study. Each country responds differently to the economic crisis that leads to don’t honor their commitments debts. / Este trabalho explora a realização de default soberano em função da estrutura de spreads de CDS (Credit Default Swap). Pode-se dizer que os spreads revelam a probabilidade de default de um país. Aplicamos a metodologia proposta neste trabalho para Argentina, Coreia, Equador, Indonésia, México, Peru, Turquia, Ucrânia, Venezuela e Rússia. Nós mostramos que um modelo de um único fator seguindo um processo lognormal captura a probabilidade de default. Também mostramos que as variáveis macro econômicas inflação, desemprego e crescimento não explicam a variável dependente do estudo (probabilidade de default). Cada país reage de maneira diferente a crise econômica que a leva a não honrar seus compromissos com as dívidas contraídas.

Acteurs majeurs du tissu économique, les petites et moyennes entreprises (PME) font l’objet d’une attention croissante de la part des économistes depuis plusieurs années. Pour financer leur développement, ces entreprises privilégient le recours à l’endettement bancaire. Or ce mode de financement est générateur d'un risque de crédit, principalement lié à la probabilité de défaillance de l’entreprise. La question de l’évaluation du risque de crédit des entreprises est généralement abordée de manière indifférenciée. Cependant il est possible que les PME internationalisées présentent des caractéristiques spécifiques relatives à leur ouverture sur les marchés internationaux. Nous procédons à une analyse des déterminants du risque de défaillance des PME d’une part, et d'autre part, des risques auxquels elles sont confrontées dans leur processus d’internationalisation. A travers des estimations économétriques sur données de panel, nous montrons notamment que l’augmentation de l’intensité des exportations des PME françaises réduit leur probabilité de défaillance. Une PME internationalisée présente alors un risque de crédit moins important qu'une PME purement domestique. Nous montrons ensuite que cette information doit être intégrée dans la modélisation du risque de crédit, réalisée sur la base de variables financières. Celle-ci est plus performante (dans le sens d'un meilleur pouvoir prédictif)lorsque l’on estime la probabilité de défaillance à l’aide de modèles distincts pour les PME internationalisées et les PME domestiques. Selon ces résultats, l'internationalisation est un facteur important qui devrait être considéré dans la recherche future sur le risque de crédit des PME. / Small and medium-sized enterprises (SMEs) dominate the French business environment making a significant contribution to the national economy. Unsurprisingly, an extensive set of empirical studies explores critical issues that affect SMEs including factors that can reduce the credit risk associated with bank debt. Despite that internationalisation has a number of key characteristics that can influence credit risk, the nexus between internationalisation and credit risks remains underexplored. This thesis aims to address this knowledge gap by examining this nexus for a panel of French SMEs. To do so, the thesis estimates the effect of export intensity of French SMEs on their default probability. Key findings illustrate that internationalisation plays a critical role in decreasing the credit risk. Motivated by these results, the thesis assesses the relationship between internationalisation and modelling credit risk through evaluating the effect of several financial variables on default probability of domestic and international SMEs, separately. Interestingly, the findings reveal that modelling the credit risk of SMEs could be improved by considering domestic and international SMEs separately. According to these findings, internationalisation is one of the most important factors that should be considered in future research in relation to SMEs.

Ratingsysteme sind ein zentraler Bestandteil der Kreditrisikomodellierung. Neben der Bonitätsbeurteilung auf der Ebene der Kreditnehmer und der Risikoquantifizierung auf der Ebene der Ratingklassen spielt dabei die Bildung der Ratingklassen eine wesentliche Rolle. Die Literatur zur Ratingklassenbildung setzt auf modellfreie, in gewisser Weise willkürliche Optimierungsverfahren. Ein Ziel der vorliegenden Arbeit ist es, stattdessen ein parametrisches statistisches Modell zur Bildung der Ratingklassen einzuführen. Ein geeignetes Modell ist im Bereich der Bruchpunktschätzung zu finden. Dieses Modell und die in der mathematischen Literatur vorgeschlagenen Parameter- und Intervallschätzer werden in der vorliegenden Arbeit dargestellt und gründlich diskutiert. Dabei wird Wert auf eine anwendungsnahe und anschauliche Formulierung der mathematisch-statistischen Sachverhalte gelegt. Anschließend wird die Methodik der Bruchpunktschätzung auf einen konkreten Datensatz angewendet und mit verschiedenen anderen Kriterien zur Ratingklassenbildung verglichen. Hier erweist sich die Bruchpunktschätzung als vorteilhaft. Aufbauend auf der empirischen Untersuchung wird abschließend weiterer Forschungsbedarf abgeleitet. Dazu werden insbesondere Konzepte für den Mehrklassenfall und für abhängige Daten entworfen.:1. Einleitung 2. Ratingsystem 3. Bruchpunktschätzung 4. Anwendung 5. Zusammenfassung und Ausblick / Rating systems are a key component of credit risk modeling. In addition to scoring at borrowers’ level and risk quantification at the level of rating classes, the formation of the rating classes plays a fundamental role. The literature on rating classification uses in a way arbitrary optimization methods. Therefore, one aim of this contribution is to introduce a parametric statistical model to form the rating classes. A suitable model can be found in the area of split-point estimation. This model and the proposed parameter and interval estimators are presented and thoroughly discussed. Here, emphasis is placed on an application-oriented and intuitive formulation of the mathematical and statistical issues. Subsequently, the methodology of split-point estimation is applied to a specific data set and compared with several other criteria for rating classification. Here, split-point estimation proves to be advantageous. Finally, further research questions are derived on the basis of the empirical study. In particular, concepts for the case of more than two classes and for dependent data are sketched.:1. Einleitung 2. Ratingsystem 3. Bruchpunktschätzung 4. Anwendung 5. Zusammenfassung und Ausblick

This thesis explores how survival analysis models performs for default risk prediction of small-to-medium sized enterprises (SME) and investigates when survival analysis models are preferable to use. This is examined by comparing the performance of three deep learning models in a survival analysis setting, a traditional survival analysis model Cox Proportional Hazards, and a traditional credit risk model logistic regression. The performance is evaluated by three metrics; concordance index, integrated Brier score and ROC-AUC. The models are trained on financial data from Swedish SME holding profit and loss statement and balance sheet results. The dataset is divided into two feature sets: a smaller and a larger, additionally the features are binned.  The results show that DeepHit and Logistic Hazard performed the best with the three metrics in mind. In terms of the AUC score all three deep learning survival models generally outperform the logistic regression model. The Cox Proportional Hazards (Cox PH) showed worse performance than the logistic regression model on the non-binned feature sets while having more comparable results in the case where the data was binned. In terms of the concordance index and integrated Brier score the Cox Proportional Hazards model consistently performed the worst out of all survival models. The largest significant performance gain for the concordance index and AUC score was however seen by the Cox PH model when binning was applied to the larger feature set. The concordance index went from 0.65 to 0.75 and the test AUC went from 76.56% to 83.91% for the larger set to larger dataset with binned features. The main conclusions is that the neural networks models did outperform the traditional models slightly and that binning had a great impact on all models, but in particular for the Cox PH model. / Det här examensarbete utreder hur modeller inom överlevnadsanalys presterar för kreditriskprediktion på små och medelstora företag (SMF) och utvärderar när överlevnadsanalys modeller är att föredra. För att besvara frågan jämförs prestandan av tre modeller för djupinlärning i en överlevnadsanalysmiljö, en traditionell överlevnadsanalys modell: Cox Proportional Hazards och en traditionell kreditriskmodell: logistik regression. Prestandan har utvärderats utifrån tre metriker; concordance index, integrated Brier score och AUC. Modellerna är tränade på finansiell data från små och medelstora företag som innefattar resultaträkning och balansräkningsresultat. Datasetet är fördelat i ett mindre variabelset och ett större set, dessutom är variablerna binnade.  Resultatet visar att DeepHit och Logistic Hazard presterar bäst baserat på alla metriker. Generellt sett är AUC måttet högre för alla djupinlärningsmodeller än för den logistiska regressionen. Cox Proportional Hazards (Cox PH) modellen presterar sämre för variabelset som inte är binnade men får jämförelsebar resultat när datan är binnad. När det gäller concordance index och integrated Brier score så har Cox PH överlag sämst resultat utav alla överlevnadsmodeller. Den största signifikanta förbättringen i resultatet för concordance index och AUC ses för Cox PH när datan binnas för det stora variabelsetet. Concordance indexet gick från 0.65 till 0.75 och test AUC måttet gick från 76.56% till 83.91% för det större variabel setet till större variabel setet med binnade variabler. De huvudsakliga slutsatserna är att de neurala nätverksmodeller presterar något bättre än de traditionella modellerna och att binning är mycket gynnsam för alla modeller men framförallt för Cox PH.

In this thesis, the credit worthiness of a company is modelled using a stochastic process. Two credit models are considered; Merton's model, which models the value of a firm's assets using geometric Brownian motion, and the distance to default model, which is driven by a two factor jump diffusion process. The probability of default and the default time are simulated using Monte Carlo and the number of scenarios needed to obtain convergence in the simulations is investigated. The simulations are performed using the probability matrix method (PMM), which means that a transition probability matrix describing the process is created and used for the simulations. Besides this, two variance reduction techniques are investigated; importance sampling and antithetic variates. / I den här uppsatsen modelleras kreditvärdigheten hos ett företag med hjälp av en stokastisk process. Två kreditmodeller betraktas; Merton's modell, som modellerar värdet av ett företags tillgångar med geometrisk Brownsk rörelse, och "distance to default", som drivs av en två-dimensionell stokastisk process med både diffusion och hopp. Sannolikheten för konkurs och den förväntade tidpunkten för konkurs simuleras med hjälp av Monte Carlo och antalet scenarion som behövs för konvergens i simuleringarna undersöks. Vid simuleringen används metoden "probability matrix method", där en övergångssannolikhetsmatris som beskriver processen används. Dessutom undersöks två metoder för variansreducering; viktad simulering (importance sampling) och antitetiska variabler (antithetic variates).

Objective: We consider the following problem from credit risk modeling: Our sample (Xi; Yi), 1 < i < n, consists of pairs of variables. The first variable Xi measures the creditworthiness of individual i. The second variable Yi is the default indicator of individual i. It has two states: Yi = 1 indicates a default, Yi = 0 a non-default. A default occurs, if individual i cannot meet its contractual credit obligations, i. e. it cannot pay back its outstandings regularly. In afirst step, our objective is to estimate the threshold between good and bad creditworthiness in the sense of dividing the range of Xi into two rating classes: One class with good creditworthiness and a low probability of default and another class with bad creditworthiness and a high probability of default. Methods: Given observations of individual creditworthiness Xi and defaults Yi, the field of change point analysis provides a natural way to estimate the breakpoint between the rating classes. In order to account for dependency between the observations, the literature proposes a combination of three model classes: These are a breakpoint model, a linear one-factor model for the creditworthiness Xi, and a Bernoulli mixture model for the defaults Yi. We generalize the dependency structure further and use a generalized link between systematic factor and idiosyncratic factor of creditworthiness. So the systematic factor cannot only change the location, but also the form of the distribution of creditworthiness. Results: For the case of two rating classes, we propose several estimators for the breakpoint and for the default probabilities within the rating classes. We prove the strong consistency of these estimators in the given non-i.i.d. framework. The theoretical results are illustrated by a simulation study. Finally, we give an overview of research opportunities.

Les périodes marquées par une aversion au risque intense sont souvent l’origine de distorsions notables dans les prix de marché, et de pertes substantielles pour les investisseurs. Chaque épisode de crise financière montre que les mouvements de ventes généralisées sur les marchés ont des conséquences très négatives sur l’économie réelle. Ainsi, explorer le phénomène d’aversion au risque et la dynamique de propagation du sentiment de panique sur les marchés financiers peut aider à appréhender ces périodes de forte volatilité.Dans ce rapport de thèse, nous explorons différentes dimensions du phénomène d’aversion au risque, dans le cadre de portefeuilles d’obligations souveraines Européennes. Le rendement des obligations d’Etat, quotté par les traders, est sensé refléter entre autre le risque que le Trésor fasse défaut sur sa dette, avant que l’obligation vienne à maturation. Il s’agit là du risque souverain. Les crises financières habituellement occasionnent un mouvement important des rendements vers des niveaux plus élevés. Ce type de correction reflète un accroissement du risque souverain, et implique nécessairement une hausse du coût de financement pour les Trésors nationaux. Un objectif de ce rapport est donc de fournir des détails inédits aux Trésors sur la manière dont les rendements obligataires sont sensés se détériorer en période d’aversion au risque.Chapitre I explore le risque souverain dans le cadre d’un modèle probabiliste impliquant des distributions à queues lourdes, ainsi que la méthode GAS qui permet de capturer la dynamique de la volatilité. L’ajustement obtenu avec les distributions Hyperboliques Généralisées est robuste, et les résultats laissent penser que notre approche est particulièrement efficace durant les périodes marquées par une volatilité erratique. Dans un but de simplification, nous décrivons la mise en place d’un estimateur de volatilité intemporel, sensé refléter la volatilité intrinsèque de chaque obligation. Cet estimateur suggère que la volatilité croit de manière quadratique lorsque celle-ci est exprimée en fonction de la fonction de répartition des variations de rendements. Dans un second temps nous explorons une version bivariée du modèle. La calibration, robuste, met en valeur les corrélations entre chaque obligation. En guise d’observation générale, notre analyse confirme que les distributions à queues épaisses sont tout à fait appropriées pour l’exploration des prix de marché en période de crise financière.Chapitre II explore différentes manières d’exploiter notre modèle probabiliste. Afin d’identifier la dynamique de la contagion entre les obligations souveraines, nous analysons la réaction attendue du marché à une série de chocs financiers. Nous considérons un niveau important de granularité pour ce qui est de la sévérité du choc sous-jacent, et ceci nous permet d’identifier des lois empiriques supposées généraliser le comportement de la réaction de marché lorsque l’aversion au risque s’intensifie. Puis, nous incorporons nos estimateurs de volatilité et de réaction de marché à certaines approches reconnues d’optimisation de portefeuille et nous notons une amélioration de la résistance des portefeuilles, dans cette nouvelle version. Finalement, nous développons une nouvelle méthodologie d’optimisation de portefeuille basée sur le principe de mean-reversion.Chapitre III est dédié au pricing de produits dérivés de taux. Nous considérons maintenant que l’aversion au risque cause l’émergence de discontinuités dans les prix de marché, que nous simulons par le biais de processus à sauts. Notre modèle se concentre sur les processus de Hawkes qui ont l’avantage de capturer la présence d’auto-excitation dans la volatilité. Nous développons une procédure de calibration qui se distingue des procédures habituelles. Les résultats de volatilité implicite sont cohérents avec la volatilité réalisée, et suggèrent que les coefficients de prime de risque ont été estimés avec succès. / Periods of deep risk aversion are usually marked by sizeable distortions in market prices, and substantial losses in portfolios. As observed during financial crises, a generalized debacle in financial markets is a very negative shock for the real economy. Against this backdrop, it looks relevant to explore how risk aversion tends to affect global market valuations, especially if this exercise helps make the promotion of more optimal portfolio rebalancing procedures.In this dissertation, we investigate different dimensions of risk aversion, with a focus on European Sovereign debt securities. For a given sovereign bond, the (quoted) yield to maturity has to reflect the underlying risk that the Treasury may default on its debt, before maturation of the bond. This is sovereign risk. Financial crises usually occasion an upward correction in bond yields. Since higher yields reflect larger sovereign risk and higher funding costs, national Treasuries are usually inclined to get a deeper understanding of how sovereign risk could evolve under the influence of fierce risk aversion. This is another objective of our research analysis.In Chapter I, we consider a probabilistic approach to sovereign risk exploration, with the main purpose of illustrating the non-linear reaction ensuing from a gradual deterioration in market sentiment. We consider heavy-tailed distributions, and we use the Generalised Autoregressive Score method as a means to capture the volatility momentum. The goodness of fit provided by Generalised Hyperbolic distributions is compelling, and results suggest that our approach is particularly relevant to fit periods or erratic volatility, typical of financial crises. As an attempt to simplify the model, we focus on an empirical formulation of the ‘untemporal’ volatility of each security. This estimator of the intrinsic volatility suggests that volatility tends to accelerate in a quadratic manner when it is expressed against the cumulative distribution function of the yield variations. In a second part, we extend this approach to a problem of larger dimension and we explore the dynamics of risk aversion from a bivariate point of view. Results look robust and illustrate multivariate correlations between sovereign securities. As a general conclusion, heavy-tailed distributions look remarkably efficient to replicate the distribution of times-series affected by distorted volatility and erratic price variations.Chapter II explores different ways to extract information from the model, about financial contagion and how it is supposed to propagate through sovereign securities. In particular, we explore the market reaction to a series of many shocks with gradual intensity. Results offer a high degree of granularity and we extrapolate empirical rules on the expected market dynamics, when risk aversion intensifies. Then we incorporate our estimators of volatility and market reaction (to shocks) into popular portfolio optimisation procedures and we see positive implications on the general resilience of these portfolios. Finally, we also design an in-house methodology for optimal portfolio rebalancing, based on mean reversion.In Chapter III, we explore how sovereign risk tends to affect the price of financial derivatives in a risk-off environment. We consider that risk aversion and the ensuing volatility now favour the emergence of sizeable discontinuities in market prices, that we model with stochastic jumps. The different approaches we investigate extensively rely on Hawkes processes. These stochastic processes seek to estimate the durable impact of risk aversion onto the dynamics of jumps, via the introduction of dedicated self-excited loops. We develop an original approach to the calibration, different from conventional procedures. In the end, the calculated implied volatility remains in the vicinity of the realised volatility and there is a visible capability to jump on any rise in risk aversion.