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Cette thèse traite de plusieurs sujets en mathématiques financières: risque de crédit, optimisation de portefeuille et modélisation des taux d’intérêts. Le chapitre 1 consiste en trois études dans le domaine du risque de crédit. La plus innovante est la première dans laquel nous construisons un modèle tel que la propriété d’immersion n’est vérifiée sous aucune mesure martingale équivalente. Le chapitre 2 étudie le problème de maximisation de la somme d’une utilité de la richesse terminale et d’une utilité de la consommation. Le chapitre 3 étudie l’évaluation des produits dérivés de taux d’intérêt dans un cadre multicourbe, qui prend en compte la différence entre une courbe de taux sans risque et des courbes de taux Libor de différents tenors. / This thesis deals with several topics in mathematical finance: credit risk, portfolio optimization and interest rate modeling. Chapter 1 consists of three studies in the field of credit risk. The most innovative is the first one, where we construct a model such that the immersion property does not hold under any equivalent martingale measure. Chapter 2 studies the problem of maximization of the sum of the utility of the terminal wealth and the utility of the consumption, in a case where a sudden jump in the risk-free interest rate induces market incompleteness. Chapter 3 studies the valuation of Libor interest rate derivatives in a multiple-curve setup, which accounts for the spreads between a risk-free discount curve and Libor curves of different tenors.

This thesis consists of two related parts. In the first part we conduct an empirical examination of the dynamics of Australian interest rates of six different maturities, covering the whole yield curve. This direct study of the long rates is quite novel. We use maximum likelihood estimation on a variety of models and find some results that are in stark contrast to previous studies. We estimate Poisson-jump diffusion (PJD) models and find very strong evidence for the existence of jumps in all daily interest rate series. We find that the PJD model fits short-rate data significantly better than a Bernoulli-jump diffusion model. We also estimate the CKLS model for our data and find that the only model not rejected for all six maturities is the CEV model in stark contrast to previous findings. Also, we find that the elasticity of variance estimate in the CKLS model is much higher for the short-rates than for the longer rates where the estimate is only about 0.25, indicating that different dynamics seem to be at work for different maturities. We also found that adding jumps to the simple diffusion model gives a larger improvement than comes from going from the simple diffusion to the CKLS model. In the second part of the thesis we examine the Flesaker and Hughston (FH) term structure model. We derive the dynamics of the short rate under both the original measure and the risk-neutral measure, and show that some criticisms of the bounds for the short rate may not be significant in actual applications. We also derive the dynamics of bond prices in the FH model and compare them to the HJM model. We also extend the FH model by allowing the martingale to follow a jump-diffusion process, rather than just a diffusion process. We derive the unique change of measure that guarantees the family of bond prices is arbitrage-free. We derive prices for caps and swaptions, and extend the results to include Bermudan swaptions and show how to price options with the jump-diffusion version of the FH model.

This thesis consists of two related parts. In the first part we conduct an empirical examination of the dynamics of Australian interest rates of six different maturities, covering the whole yield curve. This direct study of the long rates is quite novel. We use maximum likelihood estimation on a variety of models and find some results that are in stark contrast to previous studies. We estimate Poisson-jump diffusion (PJD) models and find very strong evidence for the existence of jumps in all daily interest rate series. We find that the PJD model fits short-rate data significantly better than a Bernoulli-jump diffusion model. We also estimate the CKLS model for our data and find that the only model not rejected for all six maturities is the CEV model in stark contrast to previous findings. Also, we find that the elasticity of variance estimate in the CKLS model is much higher for the short-rates than for the longer rates where the estimate is only about 0.25, indicating that different dynamics seem to be at work for different maturities. We also found that adding jumps to the simple diffusion model gives a larger improvement than comes from going from the simple diffusion to the CKLS model. In the second part of the thesis we examine the Flesaker and Hughston (FH) term structure model. We derive the dynamics of the short rate under both the original measure and the risk-neutral measure, and show that some criticisms of the bounds for the short rate may not be significant in actual applications. We also derive the dynamics of bond prices in the FH model and compare them to the HJM model. We also extend the FH model by allowing the martingale to follow a jump-diffusion process, rather than just a diffusion process. We derive the unique change of measure that guarantees the family of bond prices is arbitrage-free. We derive prices for caps and swaptions, and extend the results to include Bermudan swaptions and show how to price options with the jump-diffusion version of the FH model.

In this paper we aim to improve existing empirical exchange rate models by accounting for uncertainty with respect to the underlying structural representation. Within a flexible Bayesian non-linear time series framework, our modeling approach assumes that different regimes are characterized by commonly used structural exchange rate models, with their evolution being driven by a Markov process. We assume a time-varying transition probability matrix with transition probabilities depending on a measure of the monetary policy stance of the central bank at the home and foreign country. We apply this model to a set of eight exchange rates against the US dollar. In a forecasting exercise, we show that model evidence varies over time and a model approach that takes this empirical evidence seriously yields improvements in accuracy of density forecasts for most currency pairs considered. / Series: Department of Economics Working Paper Series

Em estudos cujo interesse é avaliar o efeito de fatores prognósticos sobre a sobrevida ou algum outro evento de interesse, é comum o uso de modelos de regressão que relacionam tempos de sobrevivência e covariáveis. Quando covariáveis que apresentam dados omissos são incluídas nos modelos de regressão, os programas estatísticos usuais automaticamente excluem aqueles indivíduos que apresentam omissão em pelo menos uma das covariáveis. Com isso, muitos pesquisadores utilizam apenas as observações completas, descartando grande parte da informação disponível. Está comprovado que a análise baseada apenas nos dados completos pode levar a estimadores altamente viesados e ineficientes. Para lidar com este problema, alguns métodos foram propostos na literatura. O objetivo deste trabalho é estender métodos que lidam com dados de sobrevivência e omissão nas covariáveis para a situação em que existe uma proporção de pacientes na população que não são suscetíveis ao evento de interesse. A idéia principal é utilizar modelos com fração de cura incluindo ponderações para compensar possíveis desproporcionalidades na subamostra de casos completos, levando-se em conta uma possível relação entre omissão e pior prognóstico. Foi considerado um modelo de mistura no qual os tempos de falha foram modelados através da família Weibull ou do modelo semiparamétrico de Cox e as probabilidade de cura foram especificadas por um modelo logístico. Os métodos propostos foram aplicados a dados reais, em que a omissão foi simulada em 10\\%, 30\\% e 50\\% das observações. / Survival regression models are considered to evaluate the effect of prognostic factors for survival or some other event of interest. The standard statistical packages automatically exclude cases with at least one missing covariate value. Thus, many researchers use only the complete cases, discarding substantial part of the available information. It is known that this complete case analysis provides biased and inefficient estimates. The aim of this work is to extend survival models with missing covariate values to situations where some individuals are not susceptible to the event of interest. The main idea is to use cure rate models introducing individual weights to incorporate possible bias in the sample with complete cases, taking a possible relation between missingness and worse prognosis into account. Mixture models in which Weibull and Cox models are used to represent the failure times and logistic models to model the cure probabilities are considered. The performance of the procedure was evaluated via a simulation study. The proposed methods were applied to real data where the missingness was simulated in 10\\%, 30\\% and 50\\% of the observations.

Em estudos cujo interesse é avaliar o efeito de fatores prognósticos sobre a sobrevida ou algum outro evento de interesse, é comum o uso de modelos de regressão que relacionam tempos de sobrevivência e covariáveis. Quando covariáveis que apresentam dados omissos são incluídas nos modelos de regressão, os programas estatísticos usuais automaticamente excluem aqueles indivíduos que apresentam omissão em pelo menos uma das covariáveis. Com isso, muitos pesquisadores utilizam apenas as observações completas, descartando grande parte da informação disponível. Está comprovado que a análise baseada apenas nos dados completos pode levar a estimadores altamente viesados e ineficientes. Para lidar com este problema, alguns métodos foram propostos na literatura. O objetivo deste trabalho é estender métodos que lidam com dados de sobrevivência e omissão nas covariáveis para a situação em que existe uma proporção de pacientes na população que não são suscetíveis ao evento de interesse. A idéia principal é utilizar modelos com fração de cura incluindo ponderações para compensar possíveis desproporcionalidades na subamostra de casos completos, levando-se em conta uma possível relação entre omissão e pior prognóstico. Foi considerado um modelo de mistura no qual os tempos de falha foram modelados através da família Weibull ou do modelo semiparamétrico de Cox e as probabilidade de cura foram especificadas por um modelo logístico. Os métodos propostos foram aplicados a dados reais, em que a omissão foi simulada em 10\\%, 30\\% e 50\\% das observações. / Survival regression models are considered to evaluate the effect of prognostic factors for survival or some other event of interest. The standard statistical packages automatically exclude cases with at least one missing covariate value. Thus, many researchers use only the complete cases, discarding substantial part of the available information. It is known that this complete case analysis provides biased and inefficient estimates. The aim of this work is to extend survival models with missing covariate values to situations where some individuals are not susceptible to the event of interest. The main idea is to use cure rate models introducing individual weights to incorporate possible bias in the sample with complete cases, taking a possible relation between missingness and worse prognosis into account. Mixture models in which Weibull and Cox models are used to represent the failure times and logistic models to model the cure probabilities are considered. The performance of the procedure was evaluated via a simulation study. The proposed methods were applied to real data where the missingness was simulated in 10\\%, 30\\% and 50\\% of the observations.

The uncertainty attached to future movements of interest rates is an essential part of the Financial Decision Theory and requires an awareness of the stochastic movement of these rates. Several approaches have been proposed for modeling the one-factor short rate models where some lead to arbitrage-free term structures. However, no definite consensus has been reached with regard to the best approach for interest rate modeling. In this work, we briefly examine the existing one-factor interest rate models and calibrate Vasicek and Hull-White (Extended Vasicek) Models by using Turkey&#039 / s term structure. Moreover, a trinomial interest rate tree is constructed to represent the evolution of Turkey&rsquo / s zero coupon rates.