Cette thèse étudie la valorisation des prêts en fonction du risque de crédit, du coût de liquidité et de l’option de prépaiement. Un prêt émis par une banque pour un de ses clients corporate est un accord financier qui est souvent plus flexible qu’un prêt au particulier. Ces options permettent ainsi de répondre aux attentes de leur client, par exemple avec l’option de prépaiement qui permet au client, s’il le souhaite, rembourser par anticipation une partie ou l’intégralité de son emprunt.Le prépaiement est la principale option et il fait l’objet d’une étude dans cette thèse. Afin de décider si l’exercice de l’option est profitable l’emprunteur compare les paiements restants avec le montant restant dû de son prêt. Si la somme des paiements restants est supérieure au montant nominal alors il est optimal pour l’emprunteur de refinancer sa dette à un taux d’intérêt inférieur. Pour une banque, l’option de prépaiement est essentiellement un risque de réinvestissement, ie. le risque qu’un emprunteur décide de prépayer et que la banque ne puisse pas réinvestir son excès de liquidité dans un nouveau prêt avec les mêmes caractéristiques.La résolution du problème de l’option de prépaiement peut être modélisée comme une option américaine sur la dette de l’emprunteur. Nous avons choisi dans cette thèse de valoriser le prix d’un prêt et de son option de prépaiement par une résolution d’un modèle EDP plutôt qu’un modèle d’arbres binomiaux (chronophage) ou que des techniques de Monte-Carlo (problème de convergence). / This PhD thesis investigates the pricing of a corporate loan according to the credit risk, the liquidity cost and the embedded prepayment option. A loan contract issued by a bank for its corporate clients is a financial agreement that often comes with more flexibility than a retail loan contract. These options are designed to meet clients’ expectations and can include e.g., a prepayment option (which entitles the client, if he desires so, to pay all or a fraction of its loan earlier than the maturity). The prepayment is the main option and it will be study in this thesis. In order to decide whether the exercise of the option is worthwhile the borrower compares the remaining payments with the outstanding amount of the loan. If the remaining payments exceed the nominal value then it is optimal for the borrower to refinance his debt at a lower rate. For a bank, the prepayment option is essentially a reinvestment risk, i.e. the risk that the borrower decides to repay earlier his/her loan and that the bank cannot reinvest his/her excess of cash in a new loan with same characteristics.The valuation problem of the prepayment option can be modelled as an embedded compound American option on a risky debt owned by the borrower. We choose in this thesis to price a loan and its prepayment option by resolving the associated PDE instead of binomial trees (time-consuming) or Monte Carlo techniques (slow to converge).

There is the need for applying numerical methods to problems that cannot be solved analytically and as the spatial dimension of the problem is increased the need for computational recourses increase exponentially, a phenomenon known as the “curse of dimensionality”. In the Black-Scholes-Merton framework the American option pricing problem has no closed form solution and a numerical procedure has to be employed for solving a PDE. The multi-asset American option introduces challenging computational problems, since for every added asset the dimension of the PDE is increased by one. One way to deal with the curse of dimensionality is threw parallelism. Here the finite element method-of-lines is used for pricing a multi-asset American option dependent on up to four assets in a parallel environment. The problem is also solved with the PSOR method giving a accurate benchmark used for comparison. In finance the put option is one of the most fundamental derivatives since it is basically asset-value insurance and a lot of research is done in the field of quantitative finance on accurate and fast pricing techniques for the multi-dimensional case. “What most experimenters take for granted before they begin their experiments is infinitely more interesting than any results to which their experiments lead.” Norbert Wiener “As soon as an Analytical Engine exists, it will necessarily guide the future course of the science. Whenever any result is sought by its aid, the question will then arise – by what course of calculation can these results be arrived at by the machine in the shortest time?” Charles Babbage

A dynamic semiparametric pricing method is proposed for financial derivatives including European and American type options and convertible bonds. The proposed method is an iterative procedure which uses nonparametric regression to approximate derivative values and parametric asset models to derive the continuation values. Extension to higher dimensional option pricing is also developed, in which the dependence structure of financial time series is modeled by copula functions. In the simulation study, we valuate one dimensional American options, convertible bonds and multi-dimensional American geometric average options and max options. The considered one-dimensional underlying asset models include the Black-Scholes, jump-diffusion, and nonlinear asymmetric GARCH models and for multivariate case we study copula models such as the Gaussian, Clayton and Gumbel copulae. Convergence of the method is proved under continuity assumption on the transition densities of the underlying asset models. And the orders of the supnorm errors are derived. Both the theoretical findings and the simulation results show the proposed approach to be tractable for numerical implementation and provides a unified and accurate technique for financial derivative pricing. The second part of this thesis studies the option pricing and hedging problems for conditional leptokurtic returns which is an important feature in financial data. The risk-neutral models for log and simple return models with heavy-tailed innovations are derived by an extended Girsanov change of measure, respectively. The result is applicable to the option pricing of the GARCH model with t innovations (GARCH-t) for simple eturn series. The dynamic semiparametric approach is extended to compute the option prices of conditional leptokurtic returns. The hedging strategy consistent with the extended Girsanov change of measure is constructed and is shown to have smaller cost variation than the commonly used delta hedging under the risk neutral measure. Simulation studies are also performed to show the effect of using GARCH-normal models to compute the option prices and delta hedging of GARCH-t model for plain vanilla and exotic options. The results indicate that there are little pricing and hedging differences between the normal and t innovations for plain vanilla and Asian options, yet significant disparities arise for barrier and lookback options due to improper distribution setting of the GARCH innovations.

This PhD thesis investigates the pricing of a corporate loan according to the credit risk, the liquidity cost and the embedded prepayment option. A loan contract issued by a bank for its corporate clients is a financial agreement that often comes with more flexibility than a retail loan contract. These options are designed to meet clients' expectations and can include e.g., a prepayment option (which entitles the client, if he desires so, to pay all or a fraction of its loan earlier than the maturity). The prepayment is the main option and it will be study in this thesis. In order to decide whether the exercise of the option is worthwhile the borrower compares the remaining payments with the outstanding amount of the loan. If the remaining payments exceed the nominal value then it is optimal for the borrower to refinance his debt at a lower rate. For a bank, the prepayment option is essentially a reinvestment risk, i.e. the risk that the borrower decides to repay earlier his/her loan and that the bank cannot reinvest his/her excess of cash in a new loan with same characteristics.The valuation problem of the prepayment option can be modelled as an embedded compound American option on a risky debt owned by the borrower. We choose in this thesis to price a loan and its prepayment option by resolving the associated PDE instead of binomial trees (time-consuming) or Monte Carlo techniques (slow to converge).

We consider the N step binomial tree model of stocks. Call options and put options of European and American type are computed explicitly. With appropriate scaling in time and jumps,  convergence of the stock prices and the option prices are obtained as N-> infinite. The obtained convergence is the Black-Scholes model and, for the particular case of European call option, the Black-Scholes formula is obtained. Furthermore, the Black-Scholes partial differential equation is obtained as a limit from the N step binomial tree model. Pricing of American put option under the Black-Scholes model is obtained as a limit from the N step binomial tree model. With this thesis, option pricing under the Black-Scholes model is achieved not by advanced stochastic analysis but by elementary, easily understandable probability computation. Results which in elementary books on finance are mentioned briefly are here derived in more details. Some important Java codes for N step binomial tree option prices are constructed by the author of the thesis.

Several existing pricing models of financial derivatives as well as the effects of volatility risk are analyzed. A new option pricing model is proposed which assumes that stock price follows a diffusion process with square-root stochastic volatility. The volatility itself is mean-reverting and driven by both diffusion and compound Poisson process. These assumptions better reflect the randomness and the jumps that are readily apparent when the historical volatility data of any risky asset is graphed. The European option price is modeled by a homogeneous linear second-order partial differential equation with variable coefficients. The case of underlying assets that pay continuous dividends is considered and implemented in the model, which gives the capability of extending the results to American options. An American option price model is derived and given by a non-homogeneous linear second order partial integro-differential equation. Using Fourier and Laplace transforms an exact closed-form solution for the price formula for European call/put options is obtained.

在傳統的Black-Scholes(B-S)選擇權評價公式中，並未將標的資產的漲跌幅限制(price limits)考慮進來。但是在某些國家如日本、韓國、台灣等其股票市場是有漲跌幅限制的。因此如果還是用傳統的B-S公式來評價，將會產生嚴重的誤差。而且在考慮漲跌幅限制下對於波動度(volatility)的估計，亦不同於傳統的計量方法，因為在漲跌幅限制下，價格會受到嚴重的扭曲，導致傳統的計量方法不再適用。本文的目的在推導出漲跌幅限制下選擇權之評價公式來取代B-S公式，並提供兩種估計波動度的方法，進而得出在考慮漲跌幅限制下正確的選擇權價值。我們發現距到期日越近、漲跌幅限制越小、波動度越大、越價外，標準B-S公式的評價誤差越嚴重。而本模型所推導的公式的誤差，相較B-S公式來的小。且實證結果也發現對較常碰觸漲跌停板的樣本而言利用GMM法來估計波動度較歷史波動度來的準確，其評價誤差也相對較小。

我們使用一個新方法來評價美式或歐式的或有求償權，其受到本地利率和權益價值的影響。我們使用標的資產的遠期價格的樹狀圖，進而對或有求償權作定價。其中我們評價了美式與歐式的股票選擇權，以及利率期貨和利率期貨選擇權。 / We introduce a methodology for pricing American or European style contingent claims, influenced by domestic interest rates, and equity prices. Instead of using trees of short-term interest rate, bond price or forward interest rate, this tree method will use the forward prices of underlying assets to derive implied binomial spot-price tree and in turn price long term American or European options, and interest rate futures and interest rate futures options.

Cette thèse est consacrée à l’analyse de ces modèles particulaires pour les mathématiques financières.Le manuscrit est organisé en quatre chapitres. Chacun peut être lu séparément.Le premier chapitre présente le travail de thèse de manière globale, définit les objectifs et résume les principales contributions. Le deuxième chapitre constitue une introduction générale à la théorie des méthodes particulaire, et propose un aperçu de ses applications aux mathématiques financières. Nous passons en revue les techniques et les résultats principaux sur les systèmes de particules en interaction, et nous expliquons comment ils peuvent être appliques à la solution numérique d’une grande variété d’applications financières, telles que l’évaluation d’options compliquées qui dépendent des trajectoires, le calcul de sensibilités, l’évaluation d’options américaines ou la résolution numérique de problèmes de contrôle et d’estimation avec observation partielle.L’évaluation d’options américaines repose sur la résolution d’une équation d’évolution à rebours, nommée l’enveloppe de Snell dans la théorie du contrôle stochastique et de l’arrêt optimal. Les deuxième et troisième chapitres se concentrent sur l’analyse de l’enveloppe de Snell et de ses extensions à différents cas particuliers. Un ensemble de modèles particulaires est alors proposé et analysé numériquement. / This thesis is concerned with the analysis of these particle models for computational finance.The manuscript is organized in four chapters. Each of them could be read separately.The first chapter provides an overview of the thesis, outlines the motivation and summarizes the major contributions. The second chapter gives a general in- troduction to the theory of interacting particle methods, with an overview of their applications to computational finance. We survey the main techniques and results on interacting particle systems and explain how they can be applied to the numerical solution of a variety of financial applications; to name a few: pricing complex path dependent European options, computing sensitivities, pricing American options, as well as numerically solving partially observed control and estimation problems.The pricing of American options relies on solving a backward evolution equation, termed Snell envelope in stochastic control and optimal stopping theory. The third and fourth chapters focus on the analysis of the Snell envelope and its variation to several particular cases. Different type of particle models are proposed and studied.

Le travail de recherche décrit dans cette thèse a pour objectif d'accélérer le temps de calcul pour valoriser des instruments financiers complexes, tels des options américaines sur panier de taille réaliste (par exemple de 40 sousjacents), en tirant partie de la puissance de calcul parallèle qu'offrent les accélérateurs graphiques (Graphics Processing Units). Dans ce but, nous partons d'un travail précédent, qui avait distribué l'algorithme de valorisation de J.Picazo, basé sur des simulations de Monte Carlo et l'apprentissage automatique. Nous en proposons une adaptation pour GPU, nous permettant de diviser par 2 le temps de calcul de cette précédente version distribuée sur un cluster de 64 cœurs CPU, expérimentée pour valoriser une option américaine sur 40 actifs. Cependant, le pricing de cette option de taille réaliste nécessite quelques heures de calcul. Nous étendons donc ce premier résultat dans le but de cibler un cluster de calculateurs, hétérogènes, mixant GPUs et CPUs, via OpenCL. Ainsi, nous accélérons fortement le temps de valorisation, même si les entrainements des différentes méthodes de classification expérimentées (AdaBoost, SVM) sont centralisés et constituent donc un point de blocage. Pour y remédier, nous évaluons alors l'utilisation d'une méthode de classification distribuée, basée sur l'utilisation de forêts aléatoires, rendant ainsi notre approche extensible. La dernière partie réutilise ces deux contributions dans le cas de calcul de la Value at Risk d’un portefeuille d'options, sur cluster hybride hétérogène. / The research work described in this thesis aims at speeding up the pricing of complex financial instruments, like an American option on a realistic size basket of assets (e.g. 40) by leveraging the parallel processing power of Graphics Processing Units. To this aim, we start from a previous research work that distributed the pricing algorithm based on Monte Carlo simulation and machine learning proposed by J. Picazo. We propose an adaptation of this distributed algorithm to take advantage of a single GPU. This allows us to get performances using one single GPU comparable to those measured using a 64 cores cluster for pricing a 40-assets basket American option. Still, on this realistic-size option, the pricing requires a handful of hours. Then we extend this first contribution in order to tackle a cluster of heterogeneous devices, both GPUs and CPUs programmed in OpenCL, at once. Doing this, we are able to drastically accelerate the option pricing time, even if the various classification methods we experiment with (AdaBoost, SVM) constitute a performance bottleneck. So, we consider instead an alternate, distributable approach, based upon Random Forests which allow our approach to become more scalable. The last part reuses these two contributions to tackle the Value at Risk evaluation of a complete portfolio of financial instruments, on a heterogeneous cluster of GPUs and CPUs.