Computer Codes And Mathematical Models

This book discusses the interplay of stochastics (applied probability theory) and numerical analysis in the field of quantitative finance. The stochastic models, numerical valuation techniques, computational aspects, financial products, and risk management applications presented will enable readers to progress in the challenging field of computational finance.When the behavior of financial market participants changes, the corresponding stochastic mathematical models describing the prices may also change. Financial regulation may play a role in such changes too. The book thus presents several models for stock prices, interest rates as well as foreign-exchange rates, with increasing complexity across the chapters. As is said in the industry, 'do not fall in love with your favorite model.' The book covers equity models before moving to short-rate and other interest rate models. We cast these models for interest rate into the Heath-Jarrow-Morton framework, show relations between the different models, and explain a few interest rate products and their pricing.The chapters are accompanied by exercises. Students can access solutions to selected exercises, while complete solutions are made available to instructors. The MATLAB and Python computer codes used for most tables and figures in the book are made available for both print and e-book users. This book will be useful for people working in the financial industry, for those aiming to work there one day, and for anyone interested in quantitative finance. The topics that are discussed are relevant for MSc and PhD students, academic researchers, and for quants in the financial industry.Supplementary Material:Solutions Manual is available to instructors who adopt this textbook for their courses. Please contact [email protected].

Computational Mathematics: Models, Methods, and Analysis with MATLAB and MPI is a unique book covering the concepts and techniques at the core of computational science. The author delivers a hands-on introduction to nonlinear, 2D, and 3D models; nonrectangular domains; systems of partial differential equations; and large algebraic problems requirin

Introduction to Mathematical Modeling and Computer Simulations is written as a textbook for readers who want to understand the main principles of Modeling and Simulations in settings that are important for the applications, without using the profound mathematical tools required by most advanced texts. It can be particularly useful for applied mathematicians and engineers who are just beginning their careers. The goal of this book is to outline Mathematical Modeling using simple mathematical descriptions, making it accessible for first- and second-year students.

Applied mathematics, together with modeling and computer simulation, is central to engineering and computer science and remains intrinsically important in all aspects of modern technology. This book presents the proceedings of AMMCS 2022, the 2nd International Conference on Applied Mathematics, Modeling and Computer Simulation, held in Wuhan, China, on 13 and 14 August 2022, with online presentations available for those not able to attend in person due to continuing pandemic restrictions. The conference served as an open forum for the sharing and spreading of the newest ideas and latest research findings among all those involved in any aspect of applied mathematics, modeling and computer simulation, and offered an ideal platform for bringing together researchers, practitioners, scholars, professors and engineers from all around the world to exchange the newest research results and stimulate scientific innovation. More than 150 participants were able to exchange knowledge and discuss the latest developments at the conference. The book contains 127 peer-reviewed papers, selected from more than 200 submissions and ranging from the theoretical and conceptual to the strongly pragmatic; all addressing industrial best practice. Topics covered included mathematical modeling and application, engineering applications and scientific computations, and simulation of intelligent systems. The book shares practical experiences and enlightening ideas and will be of interest to researchers and practitioners in applied mathematics, modeling and computer simulation everywhere.

Daniel Maki and Maynard Thompson provide a conceptual framework for the process of building and using mathematical models, illustrating the uses of mathematical and computer models in a variety of situations.

Employing a practical, "learn by doing" approach, this first-rate text fosters the development of the skills beyond the pure mathematics needed to set up and manipulate mathematical models. The author draws on a diversity of fields — including science, engineering, and operations research — to provide over 100 reality-based examples. Students learn from the examples by applying mathematical methods to formulate, analyze, and criticize models. Extensive documentation, consisting of over 150 references, supplements the models, encouraging further research on models of particular interest. The lively and accessible text requires only minimal scientific background. Designed for senior college or beginning graduate-level students, it assumes only elementary calculus and basic probability theory for the first part, and ordinary differential equations and continuous probability for the second section. All problems require students to study and create models, encouraging their active participation rather than a mechanical approach. Beyond the classroom, this volume will prove interesting and rewarding to anyone concerned with the development of mathematical models or the application of modeling to problem solving in a wide array of applications.