This book describes the principles of model building in financial engineering. It explains those models as designs and working implementations for Java-based applications. The book provides software professionals with an accessible source of numerical methods or ready-to-use code for use in business applications. It is the first book to cover the topic of Java implementations for finance/investment applications and is written specifically to be accessible to software practitioners without prior accountancy/finance training. The book develops a series of packaged classes explained and designed to allow the financial engineer complete flexibility.
This book introduces the reader to the C++ programming language and how to use it to write applications in quantitative finance (QF) and related areas. No previous knowledge of C or C++ is required -- experience with VBA, Matlab or other programming language is sufficient. The book adopts an incremental approach; starting from basic principles then moving on to advanced complex techniques and then to real-life applications in financial engineering. There are five major parts in the book: C++ fundamentals and object-oriented thinking in QF Advanced object-oriented features such as inheritance and polymorphism Template programming and the Standard Template Library (STL) An introduction to GOF design patterns and their applications in QF Applications The kinds of applications include binomial and trinomial methods, Monte Carlo simulation, advanced trees, partial differential equations and finite difference methods. This book includes a companion website with all source code and many useful C++ classes that you can use in your own applications. Examples, test cases and applications are directly relevant to QF. This book is the perfect companion to Daniel J. Duffy’s book Financial Instrument Pricing using C++ (Wiley 2004, 0470855096 / 9780470021620)
Risk control, capital allocation, and realistic derivative pricing and hedging are critical concerns for major financial institutions and individual traders alike. Events from the collapse of Lehman Brothers to the Greek sovereign debt crisis demonstrate the urgent and abiding need for statistical tools adequate to measure and anticipate the amplitude of potential swings in the financial markets—from ordinary stock price and interest rate moves, to defaults, to those increasingly frequent "rare events" fashionably called black swan events. Yet many on Wall Street continue to rely on standard models based on artificially simplified assumptions that can lead to systematic (and sometimes catastrophic) underestimation of real risks. In Practical Methods of Financial Engineering and Risk Management, Dr. Rupak Chatterjee— former director of the multi-asset quantitative research group at Citi—introduces finance professionals and advanced students to the latest concepts, tools, valuation techniques, and analytic measures being deployed by the more discerning and responsive Wall Street practitioners, on all operational scales from day trading to institutional strategy, to model and analyze more faithfully the real behavior and risk exposure of financial markets in the cold light of the post-2008 realities. Until one masters this modern skill set, one cannot allocate risk capital properly, price and hedge derivative securities realistically, or risk-manage positions from the multiple perspectives of market risk, credit risk, counterparty risk, and systemic risk. The book assumes a working knowledge of calculus, statistics, and Excel, but it teaches techniques from statistical analysis, probability, and stochastic processes sufficient to enable the reader to calibrate probability distributions and create the simulations that are used on Wall Street to valuate various financial instruments correctly, model the risk dimensions of trading strategies, and perform the numerically intensive analysis of risk measures required by various regulatory agencies.
Written by two of the most distinguished finance scholars in the industry, this introductory textbook on derivatives and risk management is highly accessible in terms of the concepts as well as the mathematics.With its economics perspective, this rewritten and streamlined second edition textbook, is closely connected to real markets, and:Beginning at a level that is comfortable to lower division college students, the book gradually develops the content so that its lessons can be profitably used by business majors, arts, science, and engineering graduates as well as MBAs who would work in the finance industry. Supplementary materials are available to instructors who adopt this textbook for their courses. These include:Solutions Manual with detailed solutions to nearly 500 end-of-chapter questions and problemsPowerPoint slides and a Test Bank for adoptersPRICED! In line with current teaching trends, we have woven spreadsheet applications throughout the text. Our aim is for students to achieve self-sufficiency so that they can generate all the models and graphs in this book via a spreadsheet software, Priced!
The third edition updates the text in two significant ways. First, it updates the presentation to reflect changes that have occurred in financial markets since the publication of the 2nd edition. One such change is with respect to the over-the-counter interest rate derivatives markets and the abolishment of LIBOR as a reference rate. Second, it updates the theory to reflect new research related to asset price bubbles and the valuation of options. Asset price bubbles are a reality in financial markets and their impact on derivative pricing is essential to understand. This is the only introductory textbook that contains these insights on asset price bubbles and options.
A comprehensive text and reference, first published in 2002, on the theory of financial engineering with numerous algorithms for pricing, risk management, and portfolio management.
Implement numerical algorithms in Java using NM Dev, an object-oriented and high-performance programming library for mathematics.You’ll see how it can help you easily create a solution for your complex engineering problem by quickly putting together classes. Numerical Methods Using Java covers a wide range of topics, including chapters on linear algebra, root finding, curve fitting, differentiation and integration, solving differential equations, random numbers and simulation, a whole suite of unconstrained and constrained optimization algorithms, statistics, regression and time series analysis. The mathematical concepts behind the algorithms are clearly explained, with plenty of code examples and illustrations to help even beginners get started. What You Will Learn Program in Java using a high-performance numerical library Learn the mathematics for a wide range of numerical computing algorithms Convert ideas and equations into code Put together algorithms and classes to build your own engineering solution Build solvers for industrial optimization problems Do data analysis using basic and advanced statistics Who This Book Is For Programmers, data scientists, and analysts with prior experience with programming in any language, especially Java.
The new edition of this book presents a comprehensive and up-to-date description of the most effective methods in continuous optimization. It responds to the growing interest in optimization in engineering, science, and business by focusing on methods best suited to practical problems. This edition has been thoroughly updated throughout. There are new chapters on nonlinear interior methods and derivative-free methods for optimization, both of which are widely used in practice and are the focus of much current research. Because of the emphasis on practical methods, as well as the extensive illustrations and exercises, the book is accessible to a wide audience.
This book provides aspiring quant developers with the numerical techniques and programming skills needed in quantitative finance. No programming background required.
Optimization models play an increasingly important role in financial decisions. This is the first textbook devoted to explaining how recent advances in optimization models, methods and software can be applied to solve problems in computational finance more efficiently and accurately. Chapters discussing the theory and efficient solution methods for all major classes of optimization problems alternate with chapters illustrating their use in modeling problems of mathematical finance. The reader is guided through topics such as volatility estimation, portfolio optimization problems and constructing an index fund, using techniques such as nonlinear optimization models, quadratic programming formulations and integer programming models respectively. The book is based on Master's courses in financial engineering and comes with worked examples, exercises and case studies. It will be welcomed by applied mathematicians, operational researchers and others who work in mathematical and computational finance and who are seeking a text for self-learning or for use with courses.
