Download or read book Numerical Approximation of Stochastic Differential Equations Driven by Levy Motion with Infinitely Many Jumps written by Ernest Jum and published by . This book was released on 2015 with total page 128 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this dissertation, we consider the problem of simulation of stochastic differential equations driven by pure jump Levy processes with infinite jump activity. Examples include, the class of stochastic differential equations driven by stable and tempered stable Levy processes, which are suited for modeling of a wide range of heavy tail phenomena. We replace the small jump part of the driving Levy process by a suitable Brownian motion, as proposed by Asmussen and Rosinski, which results in a jump-diffusion equation. We obtain Lp̳ [the space of measurable functions with a finite p-norm], for p greater than or equal to 2, and weak error estimates for the error resulting from this step. Combining this with numerical schemes for jump diffusion equations, we provide a good approximation method for the original stochastic differential equation that can also be implemented numerically. We complement these results with concrete error estimates and simulation.

Download or read book Stochastic Integration with Jumps written by Klaus Bichteler and published by Cambridge University Press. This book was released on 2002-05-13 with total page 517 pages. Available in PDF, EPUB and Kindle. Book excerpt: The complete theory of stochastic differential equations driven by jumps, their stability, and numerical approximation theories.

Download or read book Approximation Theorems for L vy driven Marcus canonical Stochastic Differential Equations written by Sooppawat Thipyarat and published by . This book was released on 2024* with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this thesis, we consider the problem of the numerical approximation of the Marcus (canonical) stochastic differential equations (SDEs) driven by a Brownian motion and an independent the pure jump Lévy process. The numerical scheme used in this thesis is the non-linear discrete time approximation based on the Wong-Zakai approximation scheme. The main results of this thesis are presented in two parts. In the first part, we prove the uniform strong approximation theorem for solutions of the Marcus SDEs. This result is an extension of the approximation results known for Stratonovich SDEs driven by a Brownian motion. We also estimate the convergence rate of strong approximations. The approximation scheme requires the explicit knowledge of the increments of the pure jump Lévy process. In the second part, we apply the method suggested by Asmussen and Rosiński, and approximate the increments of the pure jump Lévy process by a sum of Gaussian and a compound Poisson random variables that can be simulated explicitly. Hence, we examine the weak and strong convergence of the modified Wong-Zakai approximations and also determine the convergence rates. We illustrate our results by a numerical example.

Download or read book Jump SDEs and the Study of Their Densities written by Arturo Kohatsu-Higa and published by Springer. This book was released on 2019-08-13 with total page 355 pages. Available in PDF, EPUB and Kindle. Book excerpt: The present book deals with a streamlined presentation of Lévy processes and their densities. It is directed at advanced undergraduates who have already completed a basic probability course. Poisson random variables, exponential random variables, and the introduction of Poisson processes are presented first, followed by the introduction of Poisson random measures in a simple case. With these tools the reader proceeds gradually to compound Poisson processes, finite variation Lévy processes and finally one-dimensional stable cases. This step-by-step progression guides the reader into the construction and study of the properties of general Lévy processes with no Brownian component. In particular, in each case the corresponding Poisson random measure, the corresponding stochastic integral, and the corresponding stochastic differential equations (SDEs) are provided. The second part of the book introduces the tools of the integration by parts formula for jump processes in basic settings and first gradually provides the integration by parts formula in finite-dimensional spaces and gives a formula in infinite dimensions. These are then applied to stochastic differential equations in order to determine the existence and some properties of their densities. As examples, instances of the calculations of the Greeks in financial models with jumps are shown. The final chapter is devoted to the Boltzmann equation.

Download or read book An Introduction to the Numerical Simulation of Stochastic Di erential Equations written by Desmond J. Higham and published by SIAM. This book was released on 2021-01-28 with total page 293 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a lively and accessible introduction to the numerical solution of stochastic differential equations with the aim of making this subject available to the widest possible readership. It presents an outline of the underlying convergence and stability theory while avoiding technical details. Key ideas are illustrated with numerous computational examples and computer code is listed at the end of each chapter. The authors include 150 exercises, with solutions available online, and 40 programming tasks. Although introductory, the book covers a range of modern research topics, including Itô versus Stratonovich calculus, implicit methods, stability theory, nonconvergence on nonlinear problems, multilevel Monte Carlo, approximation of double stochastic integrals, and tau leaping for chemical and biochemical reaction networks. An Introduction to the Numerical Simulation of Stochastic Differential Equations is appropriate for undergraduates and postgraduates in mathematics, engineering, physics, chemistry, finance, and related disciplines, as well as researchers in these areas. The material assumes only a competence in algebra and calculus at the level reached by a typical first-year undergraduate mathematics class, and prerequisites are kept to a minimum. Some familiarity with basic concepts from numerical analysis and probability is also desirable but not necessary.

Download or read book Stochastic Integration with Jumps written by Klaus Bichteler and published by . This book was released on 2014-05-18 with total page 517 pages. Available in PDF, EPUB and Kindle. Book excerpt: The complete theory of stochastic differential equations driven by jumps, their stability, and numerical approximation theories.

Download or read book Stochastic Differential Equations Driven by Levy Processes written by Changyong Zhang and published by LAP Lambert Academic Publishing. This book was released on 2011-12 with total page 120 pages. Available in PDF, EPUB and Kindle. Book excerpt: Stochastic differential equations driven by Levy processes are used as mathematical models for random dynamic phenomena in applications arising from fields such as finance and insurance, to capture continuous and discontinuous uncertainty. For many applications, a stochastic differential equation does not have a closed-form solution and the weak Euler approximation is applied. In such numerical treatment of stochastic differential equations, it is of theoretical and practical importance to estimate the rate of convergence of the discrete time approximation. In this book, it is systematically investigated the dependence of the rate of convergence on the regularity of the coefficients and driving processes. The model under consideration is of a more general form than existing ones, and hence is applicable to a broader range of processes, from the widely-studied diffusions and stochastic differential equations driven by spherically-symmetric stable processes to stochastic differential equations driven by more general Levy processes. These processes can be found in a variety of fields, including physics, engineering, economics, and finance.

Download or read book Reflecting Stochastic Differential Equations with Jumps and Applications written by Situ Rong and published by CRC Press. This book was released on 1999-08-05 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many important physical variables satisfy certain dynamic evolution systems and can take only non-negative values. Therefore, one can study such variables by studying these dynamic systems. One can put some conditions on the coefficients to ensure non-negative values in deterministic cases. However, as a random process disturbs the system, the components of solutions to stochastic differential equations (SDE) can keep changing between arbitrary large positive and negative values-even in the simplest case. To overcome this difficulty, the author examines the reflecting stochastic differential equation (RSDE) with the coordinate planes as its boundary-or with a more general boundary. Reflecting Stochastic Differential Equations with Jumps and Applications systematically studies the general theory and applications of these equations. In particular, the author examines the existence, uniqueness, comparison, convergence, and stability of strong solutions to cases where the RSDE has discontinuous coefficients-with greater than linear growth-that may include jump reflection. He derives the nonlinear filtering and Zakai equations, the Maximum Principle for stochastic optimal control, and the necessary and sufficient conditions for the existence of optimal control. Most of the material presented in this book is new, including much new work by the author concerning SDEs both with and without reflection. Much of it appears here for the first time. With the application of RSDEs to various real-life problems, such as the stochastic population and neurophysiological control problems-both addressed in the text-scientists dealing with stochastic dynamic systems will find this an interesting and useful work.

Download or read book Continuous Time Random Walks for the Numerical Solution of Stochastic Differential Equations written by Nawaf Bou-Rabee and published by American Mathematical Soc.. This book was released on 2019-01-08 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper introduces time-continuous numerical schemes to simulate stochastic differential equations (SDEs) arising in mathematical finance, population dynamics, chemical kinetics, epidemiology, biophysics, and polymeric fluids. These schemes are obtained by spatially discretizing the Kolmogorov equation associated with the SDE in such a way that the resulting semi-discrete equation generates a Markov jump process that can be realized exactly using a Monte Carlo method. In this construction the jump size of the approximation can be bounded uniformly in space, which often guarantees that the schemes are numerically stable for both finite and long time simulation of SDEs.

Download or read book Stochastic Differential Equations and Their Numerical Approximations written by Liying Huang and published by . This book was released on 1995 with total page 194 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Numerical Methods for Stochastic Partial Differential Equations with White Noise written by Zhongqiang Zhang and published by Springer. This book was released on 2017-09-01 with total page 391 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers numerical methods for stochastic partial differential equations with white noise using the framework of Wong-Zakai approximation. The book begins with some motivational and background material in the introductory chapters and is divided into three parts. Part I covers numerical stochastic ordinary differential equations. Here the authors start with numerical methods for SDEs with delay using the Wong-Zakai approximation and finite difference in time. Part II covers temporal white noise. Here the authors consider SPDEs as PDEs driven by white noise, where discretization of white noise (Brownian motion) leads to PDEs with smooth noise, which can then be treated by numerical methods for PDEs. In this part, recursive algorithms based on Wiener chaos expansion and stochastic collocation methods are presented for linear stochastic advection-diffusion-reaction equations. In addition, stochastic Euler equations are exploited as an application of stochastic collocation methods, where a numerical comparison with other integration methods in random space is made. Part III covers spatial white noise. Here the authors discuss numerical methods for nonlinear elliptic equations as well as other equations with additive noise. Numerical methods for SPDEs with multiplicative noise are also discussed using the Wiener chaos expansion method. In addition, some SPDEs driven by non-Gaussian white noise are discussed and some model reduction methods (based on Wick-Malliavin calculus) are presented for generalized polynomial chaos expansion methods. Powerful techniques are provided for solving stochastic partial differential equations. This book can be considered as self-contained. Necessary background knowledge is presented in the appendices. Basic knowledge of probability theory and stochastic calculus is presented in Appendix A. In Appendix B some semi-analytical methods for SPDEs are presented. In Appendix C an introduction to Gauss quadrature is provided. In Appendix D, all the conclusions which are needed for proofs are presented, and in Appendix E a method to compute the convergence rate empirically is included. In addition, the authors provide a thorough review of the topics, both theoretical and computational exercises in the book with practical discussion of the effectiveness of the methods. Supporting Matlab files are made available to help illustrate some of the concepts further. Bibliographic notes are included at the end of each chapter. This book serves as a reference for graduate students and researchers in the mathematical sciences who would like to understand state-of-the-art numerical methods for stochastic partial differential equations with white noise.

Download or read book Trotter Kato Approximations of Stochastic Differential Equations in Infinite Dimensions and Applications written by T. E. Govindan and published by Springer Nature. This book was released on with total page 321 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Stochastic Calculus of Variations written by Yasushi Ishikawa and published by Walter de Gruyter GmbH & Co KG. This book was released on 2023-07-24 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a concise introduction to the stochastic calculus of variations for processes with jumps. The author provides many results on this topic in a self-contained way for e.g., stochastic differential equations (SDEs) with jumps. The book also contains some applications of the stochastic calculus for processes with jumps to the control theory, mathematical finance and so. This third and entirely revised edition of the work is updated to reflect the latest developments in the theory and some applications with graphics.

Download or read book Stabilized Numerical Methods for Stochastic Differential Equations Driven by Diffusion and Jump Diffusion Processes written by and published by . This book was released on 2015 with total page 186 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mots-clés de l'auteur: Stochastic Differential Equations ; Diffusion Processes ; Jump-Diffusion Processes ; Monte Carlo Method ; Variance Reduction Techniques ; Multilevel Monte Carlo Method ; Stiffness ; Stability ; S-ROCK Methods ; Variable Time Stepping.

Download or read book On Stochastic Differential Equations written by Various and published by Maurice Press. This book was released on 2007-03 with total page 56 pages. Available in PDF, EPUB and Kindle. Book excerpt: MEMOIRS O F T H i-AMERICAN MATHEMATICAL SOCIETY NLMBKR 4 ON STOCHASTIC DlFFliRL. NT. lAL LUAUONS KFYOSl 1TO PUBLISHED BY THh AMERICAN MATHEMATFCAL SCXJF1T 531 West 116th St., New York City ON STOCHASTIC DIFFERENTIAL EQUATIONS By KIYOSI ITO Let Xj. be a simple Markoff process with a continuous parameter t, and F t, s, E be the transition probability law of the process D F t, -s, E - Prfx E X.-3, where the right side means the probability of x a E under the condition x. f Hie differential of x. at t s is given by the transition probability law of x in an infinitesimal neighborhood of t s 2 FCs-A jjs E. W. Feller has discussed the case in which it has the following form 3 F s-A 2, JJS A E 1-p s, I yA 2 G s-A 2, j js A E yA 2 p s, j P s, 3, E o yA 2, where G s-Ag, 5 s A, j, E is a probability distribution as a function of E and satisfies 5 T- T f 1 2 J -j h-jl f 6 2 J, l-J G s-A 2, J js dn - b t, J, for A A and p s, J and P s, J, E is a probability distribution in E. The special case of M p s, J O 11 has already been treated by A, Kolmogoroff and S. Bernstein. 3 We shall introduce a somewhat general definition of the differential of the process x. Cf. 85. Let P A denote the conditional probability law L 8,5, 2 Mx-V E-3, A V A 2 0. If the 1 A -times convolution of P fl A tends to a probability law L with regard to Levys law-distance as A A 0, then L is called the I d S, J stochastic differential coefficient at s. L is clearly an infinitely divisible law. In the above Fellers case the logarithmic characteristic function Received by the editors March 29, 5 KIYOSI I TO V, L S of L f is given by 7 z, L ib s, j z - a s, j z p s, 5 f 03 e iu2 - 1 P s, J, du J . 6 8 j 7 - 00 A problem of stochastic differential equations is to construct a Markoff process whose stochastic differential coefficient L. - is given as a function of t, . 9 W. Feller has deduced the following integro-differential equation from 3, 4, 5 and 6 F t, J s, E - P t, j F t, J s, E p t, f F t, 7 s, E P t, J, dT 0. He has proved the J-oo existence and uniqueness of the solution of this equation under some conditions and has shown that the solution becomes a transition probability law, and satisfies 3, 4, 5 6. He has termed the case p t, j as continuous case and the case a t, J and b t, J as purely discontinuous case. It is true that we can construct a simple Markoff process from the transition probability law by introducing a probability distribution into the functional space RR by Kolmogoroff f s theorem, 7 but it is impossible to discuss the regularity of the ob tained process, for example measurability, continuity, discontinuity of the first kind etc, as was pointed out by J. L, Doob. 8 To discuss the measurability of the process for example, J, L. Doob has introduced a probability distribution on a subspace of RR and E, Slutsky has introduced a new concept tf measurable kernel 1,9 We shall in vestigate the sense of the term lf continuous case 11 and fl purely discontinuous case 11 used by W, Feller from the rigorous view-point of J. L. Doob and E. Slutsky. A recent research of J, L, Doob O concerning a simple Markoff process taking values in an en umerable set has been achieved from this view-point, A research of R. FortetH con cerning the above continuous case seems also to stand on the same idea but the author is not yet informed of the details . In his paper ON STOCHASTIC PROCESSES I 11 12 the author has deduced Levys canonical form of differential processes with no fixed discontinuities by making use of the rigorous scheme of J. L, Doob, Using the results of the above paper, we shall here construct the solution of the above stochastic differential equation in such a way that we may be able to discuss the regularity of the solution. For this purpose we transform the stochastic differential equation into a stochastic integral . equation...

Download or read book Contributions to Quadratic Backward Stochastic Differential Equations with Jumps and Applications written by Rym Salhi and published by . This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis focuses on backward stochastic differential equation with jumps and their applications. In the first chapter, we study a backward stochastic differential equation (BSDE for short) driven jointly by a Brownian motion and an integer valued random measure that may have infinite activity with compensator being possibly time inhomogeneous. In particular, we are concerned with the case where the driver has quadratic growth and unbounded terminal condition. The existence and uniqueness of the solution are proven by combining a monotone approximation technics and a forward approach. Chapter 2 is devoted to the well-posedness of generalized doubly reflected BSDEs (GDRBSDE for short) with jumps under weaker assumptions on the data. In particular, we study the existence of a solution for a one-dimensional GDRBSDE with jumps when the terminal condition is only measurable with respect to the related filtration and when the coefficient has general stochastic quadratic growth. We also show, in a suitable framework, the connection between our class of backward stochastic differential equations and risk sensitive zero-sum game. In chapter 3, we investigate a general class of fully coupled mean field forward-backward under weak monotonicity conditions without assuming any non-degeneracy assumption on the forward equation. We derive existence and uniqueness results under two different sets of conditions based on proximation schema weither on the forward or the backward equation. Later, we give an application for storage in smart grids.

Download or read book Implicit Numerical Simulation of Stochastic Differential Equations with Jumps written by Graeme D. Chalmers and published by . This book was released on 2008 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: