Esta tese analisa o método de colocação polinomial, para uma classe de equações integro-diferenciais singulares em espaços ponderados de funções contínuas e condições de fronteira não nulas. A convergência do método numérico em espaços com norma uniforme ponderada, é demonstrada, e taxas de convergências são determinadas, usando a suavidade dos dados das funções envolvidas no problema. Exemplos numéricos confirmam as estimativas / This thesis analyses the polynomial collocation method, for a class of singular integro-differential equations in weighted spaces of continuous functions, and non-homogeneous boundary conditions. Convergence of the numerical method, in weighted uniform norm spaces, is demonstrated and convergence rates are determined using the smoothness of the data functions involved in problem. Numerical examples confirm the estimates

Esta tese analisa o método de colocação polinomial, para uma classe de equações integro-diferenciais singulares em espaços ponderados de funções contínuas e condições de fronteira não nulas. A convergência do método numérico em espaços com norma uniforme ponderada, é demonstrada, e taxas de convergências são determinadas, usando a suavidade dos dados das funções envolvidas no problema. Exemplos numéricos confirmam as estimativas / This thesis analyses the polynomial collocation method, for a class of singular integro-differential equations in weighted spaces of continuous functions, and non-homogeneous boundary conditions. Convergence of the numerical method, in weighted uniform norm spaces, is demonstrated and convergence rates are determined using the smoothness of the data functions involved in problem. Numerical examples confirm the estimates

Neste trabalho estudaremos a existência de soluções fracas, semi-clássicas e clássicas, conceitos introduzidos no texto para uma classe de sistemas integro-diferenciais do tipo neutro com retardamento não limitado modelados na forma d/dt D(t, xt) = AD(t, xt) + ∫t0 B(t - s)D(s, xs)ds + g(t, xt), t ∈ (0, a), x0 = φ ∈ B, d/dt (x(t) + F(t, xt)) = Ax(t) + ∫t0 B(t - s)x(s)ds + G(t, xt), t ∈ (0, a), x0 = φ ∈ B, onde A é um operador linear fechado densamente definido em um espaço de Banach X, cada B(t) : D(B(t)) ⊂ X → X, t ≥ 0 é um operador linear fechado, a história xt : (-∞, 0] → X, xt(θ) = x(t + θ), pertence a um espaço de fase abstrato B definido axiomaticamente e D, F, g, G : [0, a] × B → X são funções apropriadas. Para obter alguns de nossos resultados, estudamos a existência e propriedades qualitativas de uma família resolvente de operadores lineares limitados (R(t))t≥0, para o sistema integro-diferencial d/dt (x(t) + ∫t0 N(t - s)x(s)ds) = Ax(t) + ∫t0 B(t - s)x(s) ds, t ∈ (0, a), x(0) = x0, onde (N(t)) t≥0 é uma família de operadores lineares limitados em X. Mencionamos que este tipo de sistemas aparece no estudo da condução de calor em materiais com memória amortecida. / In this work we study the existence of mild, semi-classical and classical solution, concepts introduced be later for a class of abstract neutral functional integrodifferential systems with unbounded delay in the form d/dt D(t, xt) = AD(t, xt) + ∫t0 B(t - s)D(s, xs)ds + g(t, xt), t ∈ (0, a), x0 = φ ∈ B, d/dt (x(t) + F(t, xt)) = Ax(t) + ∫t0 B(t - s)x(s)ds + G(t, xt), t ∈ (0, a), x0 = φ ∈ B, where A : D(A) ⊂ X → X is a closed linear densely defined operator in a Banach space X, each B(t) : D(B(t)) ⊂ X → X, is a closed linear operator, the history xt : (-∞, 0] → X, xt(θ) = x(t + θ), belongs to some abstract phase space B defined axiomatically and D, F, g :[0, a] × B → X are appropriate functions. To establish some of our results, we studied the existence and qualitative properties of a resolvent of bounded linear operators (R(t))t≥0, for a system in the form d/dt (x(t) + ∫t0 N(t - s)x(s)ds) = Ax(t) + ∫t0 B(t - s)x(s) ds, t ∈ (0, a), x(0) = x0, where (N(t)) t≥0 is a family of bounded linear operators on X. We mention that this class of system arise in the study of heat conduction in material with fading memory.

We are interested in modeling the Darwinian evolution of a population described by two levels of biological parameters: individuals characterized by an heritable phenotypic trait submitted to mutation and natural selection and cells in these individuals influencing their ability to consume resources and to reproduce. Our models are rooted in the microscopic description of a random (discrete) population of individuals characterized by one or several adaptive traits and cells characterized by their type. The population is modeled as a stochastic point process whose generator captures the probabilistic dynamics over continuous time of birth, mutation and death for individuals and birth and death for cells. The interaction between individuals (resp. between cells) is described by a competition between individual traits (resp. between cell types). We are looking for tractable large population approximations. By combining various scalings on population size, birth and death rates and mutation step, the single microscopic model is shown to lead to contrasting nonlinear macroscopic limits of different nature: deterministic approximations, in the form of ordinary, integro- or partial differential equations, or probabilistic ones, like stochastic partial differential equations or superprocesses.

The evolution of the price of two financial assets may be modeled by correlated geometric Brownian motion with additional, independent, finite activity jumps. Similarly, the evolution of the price of one financial asset may be modeled by a stochastic volatility process and finite activity jumps. The value of a contingent claim, written on assets where the underlying evolves by either of these two-factor processes, is given by the solution of a linear, two-dimensional, parabolic, partial integro-differential equation (PIDE). The focus of this thesis is the development of new, efficient numerical solution approaches for these PIDE's for both linear and non-linear cases. A localization scheme approximates the initial-value problem on an infinite spatial domain by an initial-boundary value problem on a finite spatial domain. Convergence of the localization method is proved using a Green's function approach. An implicit, finite difference method discretizes the PIDE. The theoretical conditions for the stability of the discrete approximation are examined under both maximum and von Neumann analysis. Three linearly convergent, monotone variants of the approach are reviewed for the constant coefficient, two-asset case and reformulated for the non-constant coefficient, stochastic volatility case. Each monotone scheme satisfies the conditions which imply convergence to the viscosity solution of the localized PIDE. A fixed point iteration solves the discrete, algebraic equations at each time step. This iteration avoids solving a dense linear system through the use of a lagged integral evaluation. Dense matrix-vector multiplication is avoided by using an FFT method. By using Green's function analysis, von Neumann analysis and maximum analysis, the fixed point iteration is shown to be rapidly convergent under typical market parameters. Combined with a penalty iteration, the value of options with an American early exercise feature may be computed. The rapid convergence of the iteration is verified in numerical tests using European and American options with vanilla payoffs, and digital, one-touch option payoffs. These tests indicate that the localization method for the PIDE's is effective. Adaptations are developed for degenerate or extreme parameter sets. The three monotone approaches are compared by computational cost and resulting error. For the stochastic volatility case, grid rotation is found to be the preferred approach. Finally, a new algorithm is developed for the solution of option values in the non-linear case of a two-factor option where the jump parameters are known only to within a deterministic range. This case results in a Hamilton-Jacobi-Bellman style PIDE. A monotone discretization is used and a new fixed point, policy iteration developed for time step solution. Analysis proves that the new iteration is globally convergent under a mild time step restriction. Numerical tests demonstrate the overall convergence of the method and investigate the financial implications of uncertain parameters on the option value.

The evolution of the price of two financial assets may be modeled by correlated geometric Brownian motion with additional, independent, finite activity jumps. Similarly, the evolution of the price of one financial asset may be modeled by a stochastic volatility process and finite activity jumps. The value of a contingent claim, written on assets where the underlying evolves by either of these two-factor processes, is given by the solution of a linear, two-dimensional, parabolic, partial integro-differential equation (PIDE). The focus of this thesis is the development of new, efficient numerical solution approaches for these PIDE's for both linear and non-linear cases. A localization scheme approximates the initial-value problem on an infinite spatial domain by an initial-boundary value problem on a finite spatial domain. Convergence of the localization method is proved using a Green's function approach. An implicit, finite difference method discretizes the PIDE. The theoretical conditions for the stability of the discrete approximation are examined under both maximum and von Neumann analysis. Three linearly convergent, monotone variants of the approach are reviewed for the constant coefficient, two-asset case and reformulated for the non-constant coefficient, stochastic volatility case. Each monotone scheme satisfies the conditions which imply convergence to the viscosity solution of the localized PIDE. A fixed point iteration solves the discrete, algebraic equations at each time step. This iteration avoids solving a dense linear system through the use of a lagged integral evaluation. Dense matrix-vector multiplication is avoided by using an FFT method. By using Green's function analysis, von Neumann analysis and maximum analysis, the fixed point iteration is shown to be rapidly convergent under typical market parameters. Combined with a penalty iteration, the value of options with an American early exercise feature may be computed. The rapid convergence of the iteration is verified in numerical tests using European and American options with vanilla payoffs, and digital, one-touch option payoffs. These tests indicate that the localization method for the PIDE's is effective. Adaptations are developed for degenerate or extreme parameter sets. The three monotone approaches are compared by computational cost and resulting error. For the stochastic volatility case, grid rotation is found to be the preferred approach. Finally, a new algorithm is developed for the solution of option values in the non-linear case of a two-factor option where the jump parameters are known only to within a deterministic range. This case results in a Hamilton-Jacobi-Bellman style PIDE. A monotone discretization is used and a new fixed point, policy iteration developed for time step solution. Analysis proves that the new iteration is globally convergent under a mild time step restriction. Numerical tests demonstrate the overall convergence of the method and investigate the financial implications of uncertain parameters on the option value.

This thesis is divided into two independent parts. The first part concerns the 3D Navier-Stokes equations. The second part deals with regularity issues for a family of integro-differential equations. In the first part of this thesis, we consider weak solutions of the 3D Navier-Stokes equations with L² initial data. We prove that ([Nabla superscript alpha])u is locally integrable in space-time for any real [alpha] such that 1 < [alpha] < 3. Up to now, only the second derivative ([Nabla]²)u was known to be locally integrable by standard parabolic regularization. We also present sharp estimates of those quantities in local weak-L[superscript (4/([alpha]+1))]. These estimates depend only on the L² norm of the initial data and on the domain of integration. Moreover, they are valid even for [alpha] ≥ 3 as long as u is smooth. The proof uses a standard approximation of Navier-Stokes from Leray and blow-up techniques. The local study is based on De Giorgi techniques with a new pressure decomposition. To handle the non-locality of fractional Laplacians, Hardy space and Maximal functions are introduced. In the second part of this thesis, we consider non-local integro-differential equations under certain natural assumptions on the kernel, and obtain persistence of Hölder continuity for their solutions. In other words, we prove that a solution stays in C[superscript beta] for all time if its initial data lies in C[superscript beta]. Also, we prove a C[superscript beta]-regularization effect from [mathematical equation] initial data. It provides an alternative proof to the result of Caffarelli, Chan and Vasseur [10], which was based on De Giorgi techniques. This result has an application for a fully non-linear problem, which is used in the field of image processing. In addition, we show Hölder regularity for solutions of drift diffusion equations with supercritical fractional diffusion under the assumption [mathematical equation]on the divergent-free drift velocity. The proof is in the spirit of Kiselev and Nazarov [48] where they established Hölder continuity of the critical surface quasi-geostrophic (SQG) equation by observing the evolution of a dual class of test functions. / text

This thesis develops a generic framework based on the Fourier transform for pricing and hedging of various options in equity, commodity, currency, and insurance markets. The pricing problem can be reduced to solving a partial integro-differential equation (PIDE). The Fourier Space Time-stepping (FST) framework developed in this thesis circumvents the problems associated with the existing finite difference methods by utilizing the Fourier transform to solve the PIDE. The FST framework-based methods are generic, highly efficient and rapidly convergent. The Fourier transform can be applied to the pricing PIDE to obtain a linear system of ordinary differential equations that can be solved explicitly. Solving the PIDE in Fourier space allows for the integral term to be handled efficiently and avoids the asymmetrical treatment of diffusion and integral terms, common in the finite difference schemes found in the literature. For path-independent options, prices can be obtained for a range of stock prices in one iteration of the algorithm. For exotic, path-dependent options, a time-stepping methodology is developed to handle barriers, free boundaries, and exercise policies. The thesis includes applications of the FST framework-based methods to a wide range of option pricing problems. Pricing of single- and multi-asset, European and path-dependent options under independent-increment exponential Levy stock price models, common in equity and insurance markets, can be done efficiently via the cornerstone FST method. Mean-reverting Levy spot price models, common in commodity markets, are handled by introducing a frequency transformation, which can be readily computed via scaling of the option value function. Generating stochastic volatility, to match the long-term equity options market data, and stochastic skew, observed in currency markets, is addressed by introducing a non-stationary extension of multi-dimensional Levy processes using regime-switching. Finally, codependent jumps in multi-asset models are introduced through copulas. The FST methods are computationally efficient, running in O(MN^d log_2 N) time with M time steps and N space points in each dimension on a d-dimensional grid. The methods achieve second-order convergence in space; for American options, a penalty method is used to attain second-order convergence in time. Furthermore, graphics processing units are utilized to further reduce the computational time of FST methods.

This thesis develops a generic framework based on the Fourier transform for pricing and hedging of various options in equity, commodity, currency, and insurance markets. The pricing problem can be reduced to solving a partial integro-differential equation (PIDE). The Fourier Space Time-stepping (FST) framework developed in this thesis circumvents the problems associated with the existing finite difference methods by utilizing the Fourier transform to solve the PIDE. The FST framework-based methods are generic, highly efficient and rapidly convergent. The Fourier transform can be applied to the pricing PIDE to obtain a linear system of ordinary differential equations that can be solved explicitly. Solving the PIDE in Fourier space allows for the integral term to be handled efficiently and avoids the asymmetrical treatment of diffusion and integral terms, common in the finite difference schemes found in the literature. For path-independent options, prices can be obtained for a range of stock prices in one iteration of the algorithm. For exotic, path-dependent options, a time-stepping methodology is developed to handle barriers, free boundaries, and exercise policies. The thesis includes applications of the FST framework-based methods to a wide range of option pricing problems. Pricing of single- and multi-asset, European and path-dependent options under independent-increment exponential Levy stock price models, common in equity and insurance markets, can be done efficiently via the cornerstone FST method. Mean-reverting Levy spot price models, common in commodity markets, are handled by introducing a frequency transformation, which can be readily computed via scaling of the option value function. Generating stochastic volatility, to match the long-term equity options market data, and stochastic skew, observed in currency markets, is addressed by introducing a non-stationary extension of multi-dimensional Levy processes using regime-switching. Finally, codependent jumps in multi-asset models are introduced through copulas. The FST methods are computationally efficient, running in O(MN^d log_2 N) time with M time steps and N space points in each dimension on a d-dimensional grid. The methods achieve second-order convergence in space; for American options, a penalty method is used to attain second-order convergence in time. Furthermore, graphics processing units are utilized to further reduce the computational time of FST methods.

Ordinary and partial differential equations are often derived as a first approximation to model a real-world situation, where the state of the system depends not only on the present time, but also on the history of the system. In this situation, a higher level of realism can be achieved by incorporating distributed delays in the mathematical models described by differential equations which results in delay Volterra integro-differential equations (denoted DVIDEs). Although DVIDEs serve as indispensable tools for modelling real systems, we still lack efficient and reliable software to approximate the solution of systems of DVIDEs. This thesis is concerned with designing, analyzing and implementing an efficient method to approximate the solution of a general system of neutral Volterra integro-differential equations with time-dependent delay arguments using a continuous Runge-Kutta (CRK) method. We introduce an adaptive stepsize selection strategy resulting in an approximate solution whose associated defect (residual) satisfies certain properties that allow us to monitor the global error reliably and efficiently. We prove the classic and optimal convergence of the numerical approximation to the exact solution. In addition, a companion system of equations is introduced in order to estimate the mathematical conditioning of the problem. A side effect of introducing this companion system is that it provides an effective estimate of the global error of the approximate solution, at a modest increase in cost. We have implemented our approach as an experimental Fortran 90 code capable of handling various kinds of DVIDEs with non-vanishing, vanishing, and infinite delay arguments.