In this paper, we establish several strong convergence results of general approximate proximal algorithm and general Bregman-function-based approximate proximal algorithm for solving the generalized variational inequality problem with pseudomonotone multifunction.

The purpose of this paper is to investigate the problem of finding a common element of the set of solutions of a generalized equilibrium problem (for short, GEP) and the set of fixed points of a nonexpansive mapping in a Hilbert space. First, by using the well-known KKM technique we derive the existence and uniqueness of solutions of the auxiliary problems for the GEP. Second, on account of this result and Nadler's theorem, we introduce an iterative scheme by the viscosity approximation method for finding a common element of the set of solutions of the GEP and the set of fixed points of the nonexpansive mapping. Furthermore, it is proven that the sequences generated by this iterative scheme converge strongly to a common element of the set of solutions of the GEP and the set of fixed points of the nonexpansive mapping.

京都大学 / 新制・課程博士 / 博士(理学) / 甲第25090号 / 理博第4997号 / 新制||理||1714(附属図書館) / 京都大学大学院理学研究科数学・数理解析専攻 / (主査)教授 COLLINSBenoit Vincent Pierre, 教授 泉 正己, 准教授 窪田 陽介 / 学位規則第4条第1項該当 / Doctor of Agricultural Science / Kyoto University / DFAM

<p>This doctoral thesis is comprised of four papers that all relate to the subject of Time Series Analysis.</p><p>The first paper of the thesis considers point estimation in a nonnegative, hence non-Gaussian, AR(1) model. The parameter estimation is carried out using a type of extreme value estimators (EVEs). A novel estimation method based on the EVEs is presented. The theoretical analysis is complemented with Monte Carlo simulation results and the paper is concluded by an empirical example.</p><p>The second paper extends the model of the first paper of the thesis and considers semiparametric, robust point estimation in a nonlinear nonnegative autoregression. The nonnegative AR(1) model of the first paper is extended in three important ways: First, we allow the errors to be serially correlated. Second, we allow for heteroskedasticity of unknown form. Third, we allow for a multi-variable mapping of previous observations. Once more, the EVEs used for parameter estimation are shown to be strongly consistent under very general conditions. The theoretical analysis is complemented with extensive Monte Carlo simulation studies that illustrate the asymptotic theory and indicate reasonable small sample properties of the proposed estimators.</p><p>In the third paper we construct a simple nonnegative time series model for realized volatility, use the results of the second paper to estimate the proposed model on S&P 500 monthly realized volatilities, and then use the estimated model to make one-month-ahead forecasts. The out-of-sample performance of the proposed model is evaluated against a number of standard models. Various tests and accuracy measures are utilized to evaluate the forecast performances. It is found that forecasts from the nonnegative model perform exceptionally well under the mean absolute error and the mean absolute percentage error forecast accuracy measures.</p><p>In the fourth and last paper of the thesis we construct a multivariate extension of the popular Diebold-Mariano test. Under the null hypothesis of equal predictive accuracy of three or more forecasting models, the proposed test statistic has an asymptotic Chi-squared distribution. To explore whether the behavior of the test in moderate-sized samples can be improved, we also provide a finite-sample correction. A small-scale Monte Carlo study indicates that the proposed test has reasonable size properties in large samples and that it benefits noticeably from the finite-sample correction, even in quite large samples. The paper is concluded by an empirical example that illustrates the practical use of the two tests.</p>

This doctoral thesis is comprised of four papers that all relate to the subject of Time Series Analysis. The first paper of the thesis considers point estimation in a nonnegative, hence non-Gaussian, AR(1) model. The parameter estimation is carried out using a type of extreme value estimators (EVEs). A novel estimation method based on the EVEs is presented. The theoretical analysis is complemented with Monte Carlo simulation results and the paper is concluded by an empirical example. The second paper extends the model of the first paper of the thesis and considers semiparametric, robust point estimation in a nonlinear nonnegative autoregression. The nonnegative AR(1) model of the first paper is extended in three important ways: First, we allow the errors to be serially correlated. Second, we allow for heteroskedasticity of unknown form. Third, we allow for a multi-variable mapping of previous observations. Once more, the EVEs used for parameter estimation are shown to be strongly consistent under very general conditions. The theoretical analysis is complemented with extensive Monte Carlo simulation studies that illustrate the asymptotic theory and indicate reasonable small sample properties of the proposed estimators. In the third paper we construct a simple nonnegative time series model for realized volatility, use the results of the second paper to estimate the proposed model on S&amp;P 500 monthly realized volatilities, and then use the estimated model to make one-month-ahead forecasts. The out-of-sample performance of the proposed model is evaluated against a number of standard models. Various tests and accuracy measures are utilized to evaluate the forecast performances. It is found that forecasts from the nonnegative model perform exceptionally well under the mean absolute error and the mean absolute percentage error forecast accuracy measures. In the fourth and last paper of the thesis we construct a multivariate extension of the popular Diebold-Mariano test. Under the null hypothesis of equal predictive accuracy of three or more forecasting models, the proposed test statistic has an asymptotic Chi-squared distribution. To explore whether the behavior of the test in moderate-sized samples can be improved, we also provide a finite-sample correction. A small-scale Monte Carlo study indicates that the proposed test has reasonable size properties in large samples and that it benefits noticeably from the finite-sample correction, even in quite large samples. The paper is concluded by an empirical example that illustrates the practical use of the two tests.

We prove strong convergence of order 1/4 - E for arbitrarily small E > 0 of the Euler-Maruyama method for multidimensional stochastic differential equations (SDEs) with discontinuous drift and degenerate diffusion coefficient. The proof is based on estimating the difference between the Euler-Maruyama scheme and another numerical method, which is constructed by applying the Euler-Maruyama scheme to a transformation of the SDE we aim to solve.

Iterative methods are extensively used to solve linear and nonlinear problems arising from both pure and applied sciences, and in particular, in fixed point theory and optimization. An iterative method which is used to find a fixed point of an operator or an optimal solution to an optimization problem generates a sequence in an iterative manner. We are in a hope that this sequence can converge to a solution of the problem under investigation. It is therefore quite naturally to require that the distance of this sequence to the solution set of the problem under investigation be decreasing from iteration to iteration. This is the idea of Fejer-monotonicity. In this paper, We consider quasi-Fejer monotone sequences; that is, we consider Fejer monotone sequences together with errors. Properties of quasi-Fejer monotone sequences are investigated, weak and strong convergence of quasi-Fejer monotone sequences are obtained, and an application to the convex feasibility problem is included.

The concept of LR-nets was introduced and investigated firstly by H.P. Lotz in [27] and by F. Raebiger in [30]. Therefore we call such nets Lotz-Raebiger nets, shortly LR-nets. In this thesis we treat two problems on asymptotic behavior of these operator nets. First problem is to generalize well known theorems for Ces`aro averages of a single operator to LR-nets, namely to generalize the Eberlein and Sine theorems. The second problem is related to the strong convergence of Markov LR-nets on L1-spaces. We prove that the existence of a lower-bound functions is necessary and sufficient for asymptotic stability of LR-nets of Markov operators.

A standard problem in mathematical finance is the calculation of the price of some financial derivative such as various types of options. Since there exists analytical solutions in only a few cases it will often boil down to estimating the price with Monte Carlo simulation in conjunction with some numerical discretization scheme. The upside of using what we can call standard Monte Carlo is that it is relative straightforward to apply and can be used for a wide variety of problems. The downside is that it has a relatively slow convergence which means that the computational cost or complexity can be very large. However, this slow convergence can be improved upon by using Multilevel Monte Carlo instead of standard Monte Carlo. With this approach it is possible to reduce the computational complexity and cost of simulation considerably. The aim of this thesis is to introduce the reader to the Multilevel Monte Carlo method with applications to European and Asian call options in both the Black-Scholes-Merton (BSM) model and in the Heston model. To this end we first cover the necessary background material such as basic probability theory, estimators and some of their properties, the stochastic integral, stochastic processes and Ito’s theorem. We introduce stochastic differential equations and two numerical discretizations schemes, the Euler–Maruyama scheme and the Milstein scheme. We define strong and weak convergence and illustrate these concepts with examples. We also describe the standard Monte Carlo method and then the theory and implementation of Multilevel Monte Carlo. In the applications part we perform numerical experiments where we compare standard Monte Carlo to Multilevel Monte Carlo in conjunction with the Euler–Maruyama scheme and Milsteins scheme. In the case of a European call in the BSM model, using the Euler–Maruyama scheme, we achieved a cost O(ε-2(log ε)2) to reach the desired error in accordance with theory in comparison to the O(ε-3) cost for standard Monte Carlo. When using Milsteins scheme instead of the Euler–Maruyama scheme it was possible to reduce the cost in terms of the number of simulations needed to achieve the desired error even further. By using Milsteins scheme, a method with greater order of strong convergence than Euler–Maruyama, we achieved the O(ε-2) cost predicted by the complexity theorem compared to the standard Monte Carlo cost of order O(ε-3). In the final numerical experiment we applied the Multilevel Monte Carlo method together with the Euler–Maruyama scheme to an Asian call in the Heston model. In this case, where the coefficients of the Heston model do not satisfy a global Lipschitz condition, the study of strong or weak convergence is much harder. The numerical experiments suggested that the strong convergence was slightly slower compared to what was found in the case of a European call in the BSM model. Nevertheless, we still achieved substantial savings in computational cost compared to using standard Monte Carlo.

Cette thèse est consacrée à l'étude des propriétés de convergence forte du schéma de Ninomiya et Victoir. Les auteurs de ce schéma proposent d'approcher la solution d'une équation différentielle stochastique (EDS), notée $X$, en résolvant $d+1$ équations différentielles ordinaires (EDOs) sur chaque pas de temps, où $d$ est la dimension du mouvement brownien. Le but de cette étude est d'analyser l'utilisation de ce schéma dans une méthode de Monte-Carlo multi-pas. En effet, la complexité optimale de cette méthode est dirigée par l'ordre de convergence vers $0$ de la variance entre les schémas utilisés sur la grille grossière et sur la grille fine. Cet ordre de convergence est lui-même lié à l'ordre de convergence fort entre les deux schémas. Nous montrons alors dans le chapitre $2$, que l'ordre fort du schéma de Ninomiya-Victoir, noté $X^{NV,eta}$ et de pas de temps $T/N$, est $1/2$. Récemment, Giles et Szpruch ont proposé un estimateur Monte-Carlo multi-pas réalisant une complexité $Oleft(epsilon^{-2}right)$ à l'aide d'un schéma de Milstein modifié. Dans le même esprit, nous proposons un schéma de Ninomiya-Victoir modifié qui peut-être couplé à l'ordre fort $1$ avec le schéma de Giles et Szpruch au dernier niveau d'une méthode de Monte-Carlo multi-pas. Cette idée est inspirée de Debrabant et Rossler. Ces auteurs suggèrent d'utiliser un schéma d'ordre faible élevé au niveau de discrétisation le plus fin. Puisque le nombre optimal de niveaux de discrétisation d'une méthode de Monte-Carlo multi-pas est dirigé par l'erreur faible du schéma utilisé sur la grille fine du dernier niveau de discrétisation, cette technique permet d'accélérer la convergence de la méthode Monte-Carlo multi-pas en obtenant une approximation d'ordre faible élevé. L'utilisation du couplage à l'ordre $1$ avec le schéma de Giles-Szpruch nous permet ainsi de garder un estimateur Monte-Carlo multi-pas réalisant une complexité optimale $Oleft( epsilon^{-2} right)$ tout en profitant de l'erreur faible d'ordre $2$ du schéma de Ninomiya-Victoir. Dans le troisième chapitre, nous nous sommes intéressés à l'erreur renormalisée définie par $sqrt{N}left(X - X^{NV,eta}right)$. Nous montrons la convergence en loi stable vers la solution d'une EDS affine, dont le terme source est formé des crochets de Lie entre les champs de vecteurs browniens. Ainsi, lorsqu'au moins deux champs de vecteurs browniens ne commutent pas, la limite n'est pas triviale. Ce qui assure que l'ordre fort $1/2$ est optimal. D'autre part, ce résultat peut être vu comme une première étape en vue de prouver un théorème de la limite centrale pour les estimateurs Monte-Carlo multi-pas. Pour cela, il faut analyser l'erreur en loi stable du schéma entre deux niveaux de discrétisation successifs. Ben Alaya et Kebaier ont prouvé un tel résultat pour le schéma d'Euler. Lorsque les champs de vecteurs browniens commutent, le processus limite est nul. Nous montrons que dans ce cas précis, que l'ordre fort est $1$. Dans le chapitre 4, nous étudions la convergence en loi stable de l'erreur renormalisée $Nleft(X - X^{NV}right)$ où $X^{NV}$ est le schéma de Ninomiya-Victoir lorsque les champs de vecteurs browniens commutent. Nous démontrons la convergence du processus d'erreur renormalisé vers la solution d'une EDS affine. Lorsque le champ de vecteurs dritf ne commute pas avec au moins un des champs de vecteurs browniens, la vitesse de convergence forte obtenue précédemment est optimale / This thesis is dedicated to the study of the strong convergence properties of the Ninomiya-Victoir scheme, which is based on the resolution of $d+1$ ordinary differential equations (ODEs) at each time step, to approximate the solution to a stochastic differential equation (SDE), where $d$ is the dimension of the Brownian. This study is aimed at analysing the use of this scheme in a multilevel Monte Carlo estimator. Indeed, the optimal complexity of this method is driven by the order of convergence to zero of the variance between the two schemes used on the coarse and fine grids at each level, which is related to the strong convergence order between the two schemes. In the second chapter, we prove strong convergence with order $1/2$ of the Ninomiya-Victoir scheme $X^{NV,eta}$, with time step $T/N$, to the solution $X$ of the limiting SDE. Recently, Giles and Szpruch proposed a modified Milstein scheme and its antithetic version, based on the swapping of each successive pair of Brownian increments in the scheme, permitting to construct a multilevel Monte Carlo estimator achieving the optimal complexity $Oleft(epsilon^{-2}right)$ for the precision $epsilon$, as in a simple Monte Carlo method with independent and identically distributed unbiased random variables. In the same spirit, we propose a modified Ninomiya-Victoir scheme, which may be strongly coupled with order $1$ to the Giles-Szpruch scheme at the finest level of a multilevel Monte Carlo estimator. This idea is inspired by Debrabant and R"ossler who suggest to use a scheme with high order of weak convergence on the finest grid at the finest level of the multilevel Monte Carlo method. As the optimal number of discretization levels is related to the weak order of the scheme used in the finest grid at the finest level, Debrabant and R"ossler manage to reduce the computational time, by decreasing the number of discretization levels. The coupling with the Giles-Szpruch scheme allows us to combine both ideas. By this way, we preserve the optimal complexity $Oleft(epsilon^{-2}right)$ and we reduce the computational time, since the Ninomiya-Victoir scheme is known to exhibit weak convergence with order 2. In the third chapter, we check that the normalized error defined by $sqrt{N}left(X - X^{NV,eta}right)$ converges to an affine SDE with source terms involving the Lie brackets between the Brownian vector fields. This result ensures that the strong convergence rate is actually $1/2$ when at least two Brownian vector fields do not commute. To link this result to the multilevel Monte Carlo estimator, it can be seen as a first step to adapt to the Ninomiya-Victoir scheme the central limit theorem of Lindeberg Feller type, derived recently by Ben Alaya and Kebaier for the multilevel Monte Carlo estimator based on the Euler scheme. When the Brownian vector fields commute, the limit vanishes. We then prove strong convergence with order $1$ in this case. The fourth chapter deals with the convergence of the normalized error process $Nleft(X - X^{NV}right)$, where $X^{NV}$ is the Ninomiya-Victoir in the commutative case. We prove its stable convergence in law to an affine SDE with source terms involving the Lie brackets between the Brownian vector fields and the drift vector field. This result ensures that the strong convergence rate is actually $1$ when the Brownian vector fields commute, but at least one of them does not commute with the Stratonovich drift vector field