| computational financial mathematics using mathematica: Computational Financial Mathematics using MATHEMATICA® Srdjan Stojanovic, 2002-10-04 Given the explosion of interest in mathematical methods for solving problems in finance and trading, a great deal of research and development is taking place in universities, large brokerage firms, and in the supporting trading software industry. Mathematical advances have been made both analytically and numerically in finding practical solutions. This book provides a comprehensive overview of existing and original material, about what mathematics when allied with Mathematica can do for finance. Sophisticated theories are presented systematically in a user-friendly style, and a powerful combination of mathematical rigor and Mathematica programming. Three kinds of solution methods are emphasized: symbolic, numerical, and Monte-- Carlo. Nowadays, only good personal computers are required to handle the symbolic and numerical methods that are developed in this book. Key features: * No previous knowledge of Mathematica programming is required * The symbolic, numeric, data management and graphic capabilities of Mathematica are fully utilized * Monte--Carlo solutions of scalar and multivariable SDEs are developed and utilized heavily in discussing trading issues such as Black--Scholes hedging * Black--Scholes and Dupire PDEs are solved symbolically and numerically * Fast numerical solutions to free boundary problems with details of their Mathematica realizations are provided * Comprehensive study of optimal portfolio diversification, including an original theory of optimal portfolio hedging under non-Log-Normal asset price dynamics is presented The book is designed for the academic community of instructors and students, and most importantly, will meet the everyday trading needs of quantitatively inclined professional and individual investors. |
| computational financial mathematics using mathematica: Computational Financial Mathematics using MATHEMATICA® Srdjan Stojanovic, 2019-05-09 This second edition presents an applied approach to financial mathematics and provides an overview of existing and original material. Sophisticated theories are presented systematically in a user-friendly style which promotes a powerful combination of mathematical rigor and Mathematica programming. Three kinds of solution methods are emphasized: symbolic, numerical, and Monte--Carlo. This new comprehensive study guide presents several additional financial problems that can be directly applied in the field, i.e., Integral-PDE Dupire equations, inverse problems, 3-D numerical pricing equations, obstacle problems, optimal portfolio problem for momentum markets. The book is intended for instructors and graduate students interested in financial mathematics as well as mathematically inclined investors and traders who rely on cash, stocks, and stock options on a regular basis. |
| computational financial mathematics using mathematica: Computational Financial Mathematics Using Mathematica Srdjan Stojanovic, 2003 CD-ROM contains: Electronic version of text, with executabile code and color pictures. |
| computational financial mathematics using mathematica: Computational Financial Mathematics using MATHEMATICA® Srdjan Stojanovic, 2012-12-06 Given the explosion of interest in mathematical methods for solving problems in finance and trading, a great deal of research and development is taking place in universities, large brokerage firms, and in the supporting trading software industry. Mathematical advances have been made both analytically and numerically in finding practical solutions. This book provides a comprehensive overview of existing and original material, about what mathematics when allied with Mathematica can do for finance. Sophisticated theories are presented systematically in a user-friendly style, and a powerful combination of mathematical rigor and Mathematica programming. Three kinds of solution methods are emphasized: symbolic, numerical, and Monte-- Carlo. Nowadays, only good personal computers are required to handle the symbolic and numerical methods that are developed in this book. Key features: * No previous knowledge of Mathematica programming is required * The symbolic, numeric, data management and graphic capabilities of Mathematica are fully utilized * Monte--Carlo solutions of scalar and multivariable SDEs are developed and utilized heavily in discussing trading issues such as Black--Scholes hedging * Black--Scholes and Dupire PDEs are solved symbolically and numerically * Fast numerical solutions to free boundary problems with details of their Mathematica realizations are provided * Comprehensive study of optimal portfolio diversification, including an original theory of optimal portfolio hedging under non-Log-Normal asset price dynamics is presented The book is designed for the academic community of instructors and students, and most importantly, will meet the everyday trading needs of quantitatively inclined professional and individual investors. |
| computational financial mathematics using mathematica: A Workout in Computational Finance, with Website Andreas Binder, Michael Aichinger, 2013-09-23 A comprehensive introduction to various numerical methods used in computational finance today Quantitative skills are a prerequisite for anyone working in finance or beginning a career in the field, as well as risk managers. A thorough grounding in numerical methods is necessary, as is the ability to assess their quality, advantages, and limitations. This book offers a thorough introduction to each method, revealing the numerical traps that practitioners frequently fall into. Each method is referenced with practical, real-world examples in the areas of valuation, risk analysis, and calibration of specific financial instruments and models. It features a strong emphasis on robust schemes for the numerical treatment of problems within computational finance. Methods covered include PDE/PIDE using finite differences or finite elements, fast and stable solvers for sparse grid systems, stabilization and regularization techniques for inverse problems resulting from the calibration of financial models to market data, Monte Carlo and Quasi Monte Carlo techniques for simulating high dimensional systems, and local and global optimization tools to solve the minimization problem. |
| computational financial mathematics using mathematica: Tools for Computational Finance Rüdiger U. Seydel, 2017-08-17 Computational and numerical methods are used in a number of ways across the field of finance. It is the aim of this book to explain how such methods work in financial engineering. By concentrating on the field of option pricing, a core task of financial engineering and risk analysis, this book explores a wide range of computational tools in a coherent and focused manner and will be of use to anyone working in computational finance. Starting with an introductory chapter that presents the financial and stochastic background, the book goes on to detail computational methods using both stochastic and deterministic approaches. Now in its sixth edition, Tools for Computational Finance has been significantly revised and contains: Several new parts such as a section on extended applications of tree methods, including multidimensional trees, trinomial trees, and the handling of dividends; Additional material in the field of generating normal variates with acceptance-rejection methods, and on Monte Carlo methods; 115 exercises, and more than 100 figures, many in color. Written from the perspective of an applied mathematician, all methods are introduced for immediate and straightforward application. A ‘learning by calculating’ approach is adopted throughout this book, enabling readers to explore several areas of the financial world. Interdisciplinary in nature, this book will appeal to advanced undergraduate and graduate students in mathematics, engineering, and other scientific disciplines as well as professionals in financial engineering. |
| computational financial mathematics using mathematica: Hypermodels in Mathematical Finance Siu-Ah Ng, 2003 At the beginning of the new millennium, two unstoppable processes aretaking place in the world: (1) globalization of the economy; (2)information revolution. As a consequence, there is greaterparticipation of the world population in capital market investment, such as bonds and stocks and their derivatives |
| computational financial mathematics using mathematica: Mathematica Cookbook Sal Mangano, 2010-04-02 Mathematica Cookbook helps you master the application's core principles by walking you through real-world problems. Ideal for browsing, this book includes recipes for working with numerics, data structures, algebraic equations, calculus, and statistics. You'll also venture into exotic territory with recipes for data visualization using 2D and 3D graphic tools, image processing, and music. Although Mathematica 7 is a highly advanced computational platform, the recipes in this book make it accessible to everyone -- whether you're working on high school algebra, simple graphs, PhD-level computation, financial analysis, or advanced engineering models. Learn how to use Mathematica at a higher level with functional programming and pattern matching Delve into the rich library of functions for string and structured text manipulation Learn how to apply the tools to physics and engineering problems Draw on Mathematica's access to physics, chemistry, and biology data Get techniques for solving equations in computational finance Learn how to use Mathematica for sophisticated image processing Process music and audio as musical notes, analog waveforms, or digital sound samples |
| computational financial mathematics using mathematica: Advances in Applied Mathematics and Global Optimization David Y. Gao, Hanif D. Sherali, 2009-04-09 The articles that comprise this distinguished annual volume for the Advances in Mechanics and Mathematics series have been written in honor of Gilbert Strang, a world renowned mathematician and exceptional person. Written by leading experts in complementarity, duality, global optimization, and quantum computations, this collection reveals the beauty of these mathematical disciplines and investigates recent developments in global optimization, nonconvex and nonsmooth analysis, nonlinear programming, theoretical and engineering mechanics, large scale computation, quantum algorithms and computation, and information theory. |
| computational financial mathematics using mathematica: Brownian Motion Calculus Ubbo F. Wiersema, 2008-08-06 Brownian Motion Calculus presents the basics of Stochastic Calculus with a focus on the valuation of financial derivatives. It is intended as an accessible introduction to the technical literature. A clear distinction has been made between the mathematics that is convenient for a first introduction, and the more rigorous underpinnings which are best studied from the selected technical references. The inclusion of fully worked out exercises makes the book attractive for self study. Standard probability theory and ordinary calculus are the prerequisites. Summary slides for revision and teaching can be found on the book website. |
| computational financial mathematics using mathematica: Computational Mathematics with SageMath P. Zimmermann, A. Casamayou, N. Cohen, G. Connan, T. Dumont, L. Fousse, F. Maltey, M. Meulien, M. Mezzarobba, C. Pernet, N. M. Thi?ry, E. Bray, J. Cremona, M. Forets, A. Ghitza, H. Thomas, 2018-12-10 This fantastic and deep book about how to use Sage for learning and doing mathematics at all levels perfectly complements the existing Sage documentation. It is filled with many carefully thought through examples and exercises, and great care has been taken to put computational functionality into proper mathematical context. Flip to almost any random page in this amazing book, and you will learn how to play with and visualize some beautiful part of mathematics. --- William A. Stein, CEO, SageMath, and professor of mathematics, University of Washington SageMath, or Sage for short, is an open-source mathematical software system based on the Python language and developed by an international community comprising hundreds of teachers and researchers, whose aim is to provide an alternative to the commercial products Magma, Maple, Mathematica, and MATLAB. To achieve this, Sage relies on many open-source programs, including GAP, Maxima, PARI, and various scientific libraries for Python, to which thousands of new functions have been added. Sage is freely available and is supported by all modern operating systems. Sage provides a wonderful scientific and graphical calculator for high school students, and it efficiently supports undergraduates in their computations in analysis, linear algebra, calculus, etc. For graduate students, researchers, and engineers in various mathematical specialties, Sage provides the most recent algorithms and tools, which is why several universities around the world already use Sage at the undergraduate level. |
| computational financial mathematics using mathematica: Neutral and Indifference Portfolio Pricing, Hedging and Investing Srdjan Stojanovic, 2011-08-30 This book is written for quantitative finance professionals, students, educators, and mathematically inclined individual investors. It is about some of the latest developments in pricing, hedging, and investing in incomplete markets. With regard to pricing, two frameworks are fully elaborated: neutral and indifference pricing. With regard to hedging, the most conservative and relaxed hedging formulas are derived. With regard to investing, the neutral pricing methodology is also considered as a tool for connecting market asset prices with optimal positions in such assets. Srdjan D. Stojanovic is Professor in the Department of Mathematical Sciences at University of Cincinnati (USA) and Professor in the Center for Financial Engineering at Suzhou University (China). |
| computational financial mathematics using mathematica: Applied Stochastic Processes and Control for Jump-Diffusions Floyd B. Hanson, 2007-01-01 This self-contained, practical, entry-level text integrates the basic principles of applied mathematics, applied probability, and computational science for a clear presentation of stochastic processes and control for jump diffusions in continuous time. The author covers the important problem of controlling these systems and, through the use of a jump calculus construction, discusses the strong role of discontinuous and nonsmooth properties versus random properties in stochastic systems. |
| computational financial mathematics using mathematica: Principles of Financial Engineering Salih N. Neftci, 2008-12-09 Principles of Financial Engineering, Second Edition, is a highly acclaimed text on the fast-paced and complex subject of financial engineering. This updated edition describes the engineering elements of financial engineering instead of the mathematics underlying it. It shows you how to use financial tools to accomplish a goal rather than describing the tools themselves. It lays emphasis on the engineering aspects of derivatives (how to create them) rather than their pricing (how they act) in relation to other instruments, the financial markets, and financial market practices. This volume explains ways to create financial tools and how the tools work together to achieve specific goals. Applications are illustrated using real-world examples. It presents three new chapters on financial engineering in topics ranging from commodity markets to financial engineering applications in hedge fund strategies, correlation swaps, structural models of default, capital structure arbitrage, contingent convertibles, and how to incorporate counterparty risk into derivatives pricing. Poised midway between intuition, actual events, and financial mathematics, this book can be used to solve problems in risk management, taxation, regulation, and above all, pricing. This latest edition of Principles of Financial Engineering is ideal for financial engineers, quantitative analysts in banks and investment houses, and other financial industry professionals. It is also highly recommended to graduate students in financial engineering and financial mathematics programs. - The Second Edition presents 5 new chapters on structured product engineering, credit markets and instruments, and principle protection techniques, among other topics - Additions, clarifications, and illustrations throughout the volume show these instruments at work instead of explaining how they should act - The Solutions Manual enhances the text by presenting additional cases and solutions to exercises |
| computational financial mathematics using mathematica: Principles of Financial Engineering Robert Kosowski, Salih N. Neftci, 2014-11-26 Principles of Financial Engineering, Third Edition, is a highly acclaimed text on the fast-paced and complex subject of financial engineering. This updated edition describes the engineering elements of financial engineering instead of the mathematics underlying it. It shows how to use financial tools to accomplish a goal rather than describing the tools themselves. It lays emphasis on the engineering aspects of derivatives (how to create them) rather than their pricing (how they act) in relation to other instruments, the financial markets, and financial market practices. This volume explains ways to create financial tools and how the tools work together to achieve specific goals. Applications are illustrated using real-world examples. It presents three new chapters on financial engineering in topics ranging from commodity markets to financial engineering applications in hedge fund strategies, correlation swaps, structural models of default, capital structure arbitrage, contingent convertibles, and how to incorporate counterparty risk into derivatives pricing. Poised midway between intuition, actual events, and financial mathematics, this book can be used to solve problems in risk management, taxation, regulation, and above all, pricing. A solutions manual enhances the text by presenting additional cases and solutions to exercises. This latest edition of Principles of Financial Engineering is ideal for financial engineers, quantitative analysts in banks and investment houses, and other financial industry professionals. It is also highly recommended to graduate students in financial engineering and financial mathematics programs. - The Third Edition presents three new chapters on financial engineering in commodity markets, financial engineering applications in hedge fund strategies, correlation swaps, structural models of default, capital structure arbitrage, contingent convertibles and how to incorporate counterparty risk into derivatives pricing, among other topics - Additions, clarifications, and illustrations throughout the volume show these instruments at work instead of explaining how they should act - The solutions manual enhances the text by presenting additional cases and solutions to exercises |
| computational financial mathematics using mathematica: Mathematical Modeling And Computation In Finance: With Exercises And Python And Matlab Computer Codes Cornelis W Oosterlee, Lech A Grzelak, 2019-10-29 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. |
| computational financial mathematics using mathematica: Mathematics and Computation Avi Wigderson, 2019-10-29 From the winner of the Turing Award and the Abel Prize, an introduction to computational complexity theory, its connections and interactions with mathematics, and its central role in the natural and social sciences, technology, and philosophy Mathematics and Computation provides a broad, conceptual overview of computational complexity theory—the mathematical study of efficient computation. With important practical applications to computer science and industry, computational complexity theory has evolved into a highly interdisciplinary field, with strong links to most mathematical areas and to a growing number of scientific endeavors. Avi Wigderson takes a sweeping survey of complexity theory, emphasizing the field’s insights and challenges. He explains the ideas and motivations leading to key models, notions, and results. In particular, he looks at algorithms and complexity, computations and proofs, randomness and interaction, quantum and arithmetic computation, and cryptography and learning, all as parts of a cohesive whole with numerous cross-influences. Wigderson illustrates the immense breadth of the field, its beauty and richness, and its diverse and growing interactions with other areas of mathematics. He ends with a comprehensive look at the theory of computation, its methodology and aspirations, and the unique and fundamental ways in which it has shaped and will further shape science, technology, and society. For further reading, an extensive bibliography is provided for all topics covered. Mathematics and Computation is useful for undergraduate and graduate students in mathematics, computer science, and related fields, as well as researchers and teachers in these fields. Many parts require little background, and serve as an invitation to newcomers seeking an introduction to the theory of computation. Comprehensive coverage of computational complexity theory, and beyond High-level, intuitive exposition, which brings conceptual clarity to this central and dynamic scientific discipline Historical accounts of the evolution and motivations of central concepts and models A broad view of the theory of computation's influence on science, technology, and society Extensive bibliography |
| computational financial mathematics using mathematica: Neural Information Processing Tingwen Huang, Zhigang Zeng, Chuandong Li, Chi Sing Leung, 2012-11-05 The five volume set LNCS 7663, LNCS 7664, LNCS 7665, LNCS 7666 and LNCS 7667 constitutes the proceedings of the 19th International Conference on Neural Information Processing, ICONIP 2012, held in Doha, Qatar, in November 2012. The 423 regular session papers presented were carefully reviewed and selected from numerous submissions. These papers cover all major topics of theoretical research, empirical study and applications of neural information processing research. The 5 volumes represent 5 topical sections containing articles on theoretical analysis, neural modeling, algorithms, applications, as well as simulation and synthesis. |
| computational financial mathematics using mathematica: The Journal of Computational Finance , 2006 |
| computational financial mathematics using mathematica: Lectures on Global Optimization Thomas Frederick Coleman, 2009 A large number of mathematical models in many diverse areas of science and engineering have lead to the formulation of optimization problems where the best solution (globally optimal) is needed. This book covers a small subset of important topics in global optimization with emphasis on theoretical developments and scientific applications. |
| computational financial mathematics using mathematica: Modern Computational Finance Antoine Savine, 2018-11-20 Arguably the strongest addition to numerical finance of the past decade, Algorithmic Adjoint Differentiation (AAD) is the technology implemented in modern financial software to produce thousands of accurate risk sensitivities, within seconds, on light hardware. AAD recently became a centerpiece of modern financial systems and a key skill for all quantitative analysts, developers, risk professionals or anyone involved with derivatives. It is increasingly taught in Masters and PhD programs in finance. Danske Bank's wide scale implementation of AAD in its production and regulatory systems won the In-House System of the Year 2015 Risk award. The Modern Computational Finance books, written by three of the very people who designed Danske Bank's systems, offer a unique insight into the modern implementation of financial models. The volumes combine financial modelling, mathematics and programming to resolve real life financial problems and produce effective derivatives software. This volume is a complete, self-contained learning reference for AAD, and its application in finance. AAD is explained in deep detail throughout chapters that gently lead readers from the theoretical foundations to the most delicate areas of an efficient implementation, such as memory management, parallel implementation and acceleration with expression templates. The book comes with professional source code in C++, including an efficient, up to date implementation of AAD and a generic parallel simulation library. Modern C++, high performance parallel programming and interfacing C++ with Excel are also covered. The book builds the code step-by-step, while the code illustrates the concepts and notions developed in the book. |
| computational financial mathematics using mathematica: Introduction to Financial Mathematics Kevin J. Hastings, 2024-10-14 Introduction to Financial Mathematics motivates students through a discussion of personal finances and portfolio management. The book covers nearly all of the syllabus topics of the Financial Mathematics Actuarial examination to provide students with the foundation they require for future studies and in their careers. It begins |
| computational financial mathematics using mathematica: Foundations of Mathematical and Computational Economics Kamran Dadkhah, 2014-09-28 This is a book on the basics of mathematics and computation and their uses in economics for modern day students and practitioners. The reader is introduced to the basics of numerical analysis as well as the use of computer programs such as Matlab and Excel in carrying out involved computations. Sections are devoted to the use of Maple in mathematical analysis. Examples drawn from recent contributions to economic theory and econometrics as well as a variety of end of chapter exercises help to illustrate and apply the presented concepts. |
| computational financial mathematics using mathematica: Financial Mathematics Giuseppe Campolieti, Roman N. Makarov, 2022-12-21 The book has been tested and refined through years of classroom teaching experience. With an abundance of examples, problems, and fully worked out solutions, the text introduces the financial theory and relevant mathematical methods in a mathematically rigorous yet engaging way. This textbook provides complete coverage of continuous-time financial models that form the cornerstones of financial derivative pricing theory. Unlike similar texts in the field, this one presents multiple problem-solving approaches, linking related comprehensive techniques for pricing different types of financial derivatives. Key features: In-depth coverage of continuous-time theory and methodology Numerous, fully worked out examples and exercises in every chapter Mathematically rigorous and consistent, yet bridging various basic and more advanced concepts Judicious balance of financial theory and mathematical methods Guide to Material This revision contains: Almost 150 pages worth of new material in all chapters A appendix on probability theory An expanded set of solved problems and additional exercises Answers to all exercises This book is a comprehensive, self-contained, and unified treatment of the main theory and application of mathematical methods behind modern-day financial mathematics. The text complements Financial Mathematics: A Comprehensive Treatment in Discrete Time, by the same authors, also published by CRC Press. |
| computational financial mathematics using mathematica: Modern Computational Finance Antoine Savine, Jesper Andreasen, 2021-11-02 An incisive and essential guide to building a complete system for derivative scripting In Volume 2 of Modern Computational Finance Scripting for Derivatives and xVA, quantitative finance experts and practitioners Drs. Antoine Savine and Jesper Andreasen deliver an indispensable and insightful roadmap to the interrogation, aggregation, and manipulation of cash-flows in a variety of ways. The book demonstrates how to facilitate portfolio-wide risk assessment and regulatory calculations (like xVA). Complete with a professional scripting library written in modern C++, this stand-alone volume walks readers through the construction of a comprehensive risk and valuation tool. This essential book also offers: Effective strategies for improving scripting libraries, from basic examples—like support for dates and vectors—to advanced improvements, including American Monte Carlo techniques Exploration of the concepts of fuzzy logic and risk sensitivities, including support for smoothing and condition domains Discussion of the application of scripting to xVA, complete with a full treatment of branching Perfect for quantitative analysts, risk professionals, system developers, derivatives traders, and financial analysts, Modern Computational Finance Scripting for Derivatives and xVA: Volume 2 is also a must-read resource for students and teachers in master’s and PhD finance programs. |
| computational financial mathematics using mathematica: Mathematica Navigator Heikki Ruskeepaa, Heikki Ruskeepää, 2004-02-06 Mathematica Navigator gives you a general introduction to Mathematica. The book emphasizes graphics, methods of applied mathematics and statistics, and programming. Mathematica Navigator can be used both as a tutorial and as a handbook. While no previous experience with Mathematica is required, most chapters also include advanced material, so that the book will be a valuable resource for both beginners and experienced users. |
| computational financial mathematics using mathematica: Mathematica Stephen Wolfram, 1991 Just out, the long-waited Release 2.0 of Mathematica. This new edition of the complete reference was released simultaneously and covers all the new features of Release 2.0. Includes a comprehensive review of the increased functionality of the program. Annotation copyrighted by Book News, Inc., Portland, OR |
| computational financial mathematics using mathematica: Mathematical Reviews , 2003 |
| computational financial mathematics using mathematica: Computational Financial Mathematics using MATHEMATICA® Srdjan Stojanovic, 2002-10-04 Given the explosion of interest in mathematical methods for solving problems in finance and trading, a great deal of research and development is taking place in universities, large brokerage firms, and in the supporting trading software industry. Mathematical advances have been made both analytically and numerically in finding practical solutions. This book provides a comprehensive overview of existing and original material, about what mathematics when allied with Mathematica can do for finance. Sophisticated theories are presented systematically in a user-friendly style, and a powerful combination of mathematical rigor and Mathematica programming. Three kinds of solution methods are emphasized: symbolic, numerical, and Monte-- Carlo. Nowadays, only good personal computers are required to handle the symbolic and numerical methods that are developed in this book. Key features: * No previous knowledge of Mathematica programming is required * The symbolic, numeric, data management and graphic capabilities of Mathematica are fully utilized * Monte--Carlo solutions of scalar and multivariable SDEs are developed and utilized heavily in discussing trading issues such as Black--Scholes hedging * Black--Scholes and Dupire PDEs are solved symbolically and numerically * Fast numerical solutions to free boundary problems with details of their Mathematica realizations are provided * Comprehensive study of optimal portfolio diversification, including an original theory of optimal portfolio hedging under non-Log-Normal asset price dynamics is presented The book is designed for the academic community of instructors and students, and most importantly, will meet the everyday trading needs of quantitatively inclined professional and individual investors. |
| computational financial mathematics using mathematica: Programming with Mathematica® Paul Wellin, 2013-01-10 This practical, example-driven introduction teaches the foundations of the Mathematica language so it can be applied to solving concrete problems. |
| computational financial mathematics using mathematica: Applied Computational Economics and Finance Mario J. Miranda, Paul L. Fackler, 2014-05-14 An introduction to the use of computational methods to solve problems in economics and finance. |
| computational financial mathematics using mathematica: A First Course in Quantitative Finance Thomas Mazzoni, 2018-03-29 Using stereoscopic images and other novel pedagogical features, this book offers a comprehensive introduction to quantitative finance. |
| computational financial mathematics using mathematica: Financial Mathematics, Derivatives and Structured Products Raymond H. Chan, Yves ZY. Guo, Spike T. Lee, Xun Li, 2024-06-12 This book introduces readers to the financial markets, derivatives, structured products and how the products are modelled and implemented by practitioners. In addition, it equips readers with the necessary knowledge of financial markets needed in order to work as product structurers, traders, sales or risk managers. This second edition substantially extends, updates and clarifies the previous edition. New materials and enhanced contents include, but not limited to, the role of central counterparties for derivatives transactions, the reference rates to replace LIBOR, risk-neutral modelling for futures and forward, discussions and analysis on risk-neutral framework and numéraires, discrete dividend modelling, variance reduction techniques for Monte Carlo method, finite difference method analysis, tree method, FX modelling, multi-name credit derivatives modelling, local volatility model, forward variance model and local-stochastic volatility model to reflect market practice. As the book seeks to unify the derivatives modelling and the financial engineering practice in the market, it will be of interest to financial practitioners and academic researchers alike. The book can also be used as a textbook for the following courses: • Financial Mathematics (undergraduate level) • Stochastic Modelling in Finance (postgraduate level) • Financial Markets and Derivatives (undergraduate level) • Structured Products and Solutions (undergraduate/postgraduate level) |
| computational financial mathematics using mathematica: Studia Universitatis Babeș-Bolyai Universitatea "Babeș-Bolyai"., 2008 |
| computational financial mathematics using mathematica: Mathematics and Computation in Imaging Science and Information Processing Say Song Goh, Amos Ron, Zuowei Shen, 2007 The explosion of data arising from rapid advances in communication, sensing and computational power has concentrated research effort on more advanced techniques for the representation, processing, analysis and interpretation of data sets. In view of these exciting developments, the program OC Mathematics and Computation in Imaging Science and Information ProcessingOCO was held at the Institute for Mathematical Sciences, National University of Singapore, from July to December 2003 and in August 2004 to promote and facilitate multidisciplinary research in the area. As part of the program, a series of tutorial lectures were conducted by international experts on a wide variety of topics in mathematical image, signal and information processing. This compiled volume contains survey articles by the tutorial speakers, all specialists in their respective areas. They collectively provide graduate students and researchers new to the field a unique and valuable introduction to a range of important topics at the frontiers of current research. Sample Chapter(s). Foreword (46 KB). Chapter 1: Subdivision on Arbitrary Meshes: Algorithms and Theory (771 KB). Contents: Subdivision on Arbitrary Meshes: Algorithms and Theory (D Zorin); High Order Numerical Methods for Time Dependent Hamilton-Jacobi Equations (C-W Shu); Theory and Computation of Variational Image Deblurring (T F Chan & J Shen); Data Hiding OCo Theory and Algorithms (P Moulin & R Koetter); Image Steganography and Steganalysis: Concepts and Practice (M Kharrazi et al.); The Apriori Algorithm OCo A Tutorial (M Hegland). Readership: Graduate students and researchers in mathematical image, signal and information processing. |
| computational financial mathematics using mathematica: Finance Fouad Sabry, 2024-02-11 What is Finance Finance is the study and discipline of money, currency and capital assets. It is related to and distinct from Economics which is the study of production, distribution, and consumption of goods and services. The discipline of Financial Economics bridges the two fields. Based on the scope of financial activities in financial systems, the discipline can be divided into personal, corporate, and public finance. How you will benefit (I) Insights, and validations about the following topics: Chapter 1: Finance Chapter 2: Arbitrage Chapter 3: Long-Term Capital Management Chapter 4: Financial market Chapter 5: Financial economics Chapter 6: Capital asset pricing model Chapter 7: Valuation (finance) Chapter 8: Financial analyst Chapter 9: Portfolio (finance) Chapter 10: Financial risk management Chapter 11: Investment management Chapter 12: Structured product Chapter 13: Financial risk Chapter 14: Financial modeling Chapter 15: Government spending Chapter 16: Portfolio manager Chapter 17: Financial innovation Chapter 18: Quantitative fund Chapter 19: Quantitative analysis (finance) Chapter 20: Mathematical finance Chapter 21: Corporate finance (II) Answering the public top questions about finance. (III) Real world examples for the usage of finance in many fields. Who this book is for Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Finance. |
| computational financial mathematics using mathematica: Financial Economics Fouad Sabry, 2023-12-17 What is Financial Economics The subfield of economics known as financial economics is distinguished by its concentration on monetary activities and the fact that money of one type or another is likely to appear on both sides of a trade. It is therefore concerned with the interrelationship of financial factors, such as share prices, interest rates, and exchange rates, as opposed to those that pertain to the actual economy. Asset pricing and corporate finance are the two primary areas of concentration that it focuses on. The first is the viewpoint of those who offer capital, sometimes known as investors, and the second is the viewpoint of those who need capital.The theoretical foundation for a significant portion of finance is therefore provided by it. How you will benefit (I) Insights, and validations about the following topics: Chapter 1: Financial economics Chapter 2: Finance Chapter 3: Black-Scholes model Chapter 4: Capital asset pricing model Chapter 5: Real options valuation Chapter 6: Risk-neutral measure Chapter 7: Rational pricing Chapter 8: Arbitrage pricing theory Chapter 9: Beta (finance) Chapter 10: Monte Carlo methods in finance Chapter 11: Monte Carlo methods for option pricing Chapter 12: Business valuation Chapter 13: Asset pricing Chapter 14: Financial modeling Chapter 15: Lattice model (finance) Chapter 16: Georgism Chapter 17: Option (finance) Chapter 18: Heston model Chapter 19: Quantitative analysis (finance) Chapter 20: Mathematical finance Chapter 21: Contingent claim (II) Answering the public top questions about financial economics. (III) Real world examples for the usage of financial economics in many fields. (IV) Rich glossary featuring over 1200 terms to unlock a comprehensive understanding of financial economics. (eBook only). Who will benefit Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of financial economics. |
| computational financial mathematics using mathematica: Elements of Numerical Analysis with Mathematica John Loustau, 2017-08-24 Here we present numerical analysis to advanced undergraduate and master degree level grad students. This is to be done in one semester. The programming language is Mathematica. The mathematical foundation and technique is included. The emphasis is geared toward the two major developing areas of applied mathematics, mathematical finance and mathematical biology. |
| computational financial mathematics using mathematica: Differential Dynamical Systems James D. Meiss, 2007-01-01 Differential equations are the basis for models of any physical systems that exhibit smooth change. This book combines much of the material found in a traditional course on ordinary differential equations with an introduction to the more modern theory of dynamical systems. Applications of this theory to physics, biology, chemistry, and engineering are shown through examples in such areas as population modeling, fluid dynamics, electronics, and mechanics.Differential Dynamical Systems begins with coverage of linear systems, including matrix algebra; the focus then shifts to foundational material on nonlinear differential equations, making heavy use of the contraction-mapping theorem. Subsequent chapters deal specifically with dynamical systems conceptsflow, stability, invariant manifolds, the phase plane, bifurcation, chaos, and Hamiltonian dynamics. Throughout the book, the author includes exercises to help students develop an analytical and geometrical understanding of dynamics. Many of the exercises and examples are based on applications and some involve computation; an appendix offers simple codes written in Maple, Mathematica, and MATLAB software to give students practice with computation applied to dynamical systems problems. Audience This textbook is intended for senior undergraduates and first-year graduate students in pure and applied mathematics, engineering, and the physical sciences. Readers should be comfortable with elementary differential equations and linear algebra and should have had exposure to advanced calculus. Contents List of Figures; Preface; Acknowledgments; Chapter 1: Introduction; Chapter 2: Linear Systems; Chapter 3: Existence and Uniqueness; Chapter 4: Dynamical Systems; Chapter 5: Invariant Manifolds; Chapter 6: The Phase Plane; Chapter 7: Chaotic Dynamics; Chapter 8: Bifurcation Theory; Chapter 9: Hamiltonian Dynamics; Appendix: Mathematical Software; Bibliography; Index |
| computational financial mathematics using mathematica: Event Study Fouad Sabry, 2024-02-03 What is Event Study In the field of statistics, an event study is a procedure that is used to evaluate the effects of an event. How you will benefit (I) Insights, and validations about the following topics: Chapter 1: Event study Chapter 2: Finance Chapter 3: Mergers and acquisitions Chapter 4: Financial economics Chapter 5: Technical analysis Chapter 6: Efficient-market hypothesis Chapter 7: Arbitrage pricing theory Chapter 8: Beta (finance) Chapter 9: Abnormal return Chapter 10: Financial econometrics Chapter 11: Market anomaly Chapter 12: Random walk hypothesis Chapter 13: Algorithmic trading Chapter 14: Experimental finance Chapter 15: Earnings response coefficient Chapter 16: Experience Economy Chapter 17: Fama-French three-factor model Chapter 18: Jonathan Kinlay Chapter 19: Fossil Fuel Beta Chapter 20: Quantitative analysis (finance) Chapter 21: Share price (II) Answering the public top questions about event study. (III) Real world examples for the usage of event study in many fields. Who this book is for Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Event Study. |
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