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Stochastic Simulation and Applications in Finance with MATLAB Programs explains the fundamentals of Monte Carlo simulation techniques, their use in the numerical resolution of stochastic differential equations and their current applications in finance. Building on an integrated approach, it provides a pedagogical treatment of the need-to-know materials in risk management and financial engineering. The book takes readers through the basic concepts, covering the most recent research and problems in the area, including: the quadratic re-sampling technique, the Least Squared Method, the dynamic programming and Stratified State Aggregation technique to price American options, the extreme value simulation technique to price exotic options and the retrieval of volatility method to estimate Greeks. The authors also present modern term structure of interest rate models and pricing swaptions with the BGM market model, and give a full explanation of corporate securities valuation and credit risk based on the structural approach of Merton. Case studies on financial guarantees illustrate how to implement the simulation techniques in pricing and hedging. The book also includes an accompanying CD-ROM which provides MATLAB programs for the practical examples and case studies, which will give the reader confidence in using and adapting specific ways to solve problems involving stochastic processes in finance. "This book provides a very useful set of tools for those who are interested in the simulation method of asset pricing and its implementation with MatLab. It is pitched at just the right level for anyone who seeks to learn about this fascinating area of finance. The collection of specific topics thoughtfully selected by the authors, such as credit risk, loan guarantee and value-at-risk, is an additional nice feature, making it a great source of reference for researchers and practitioners. The book is a valuable contribution to the fast growing area of quantitative finance." -Tan
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This collaborative book coordinated by the European Journalism Centre and the Open Knowledge Foundation aims to answer questions like: Where can I find data? How can I request data? What tools can I use? How can I find stories in data? How can I make data journalism sustainable? It had a very successful start at the Mozilla Festival in London in November 2011 with fifty contributors, including data journalists and professors from the New York Times, Financial Times, Guardian, Chicago Tribune, Medill School of Journalism, and Cronkite School of Journalism. Additional contributors now include leading developers, analysts, FOI experts, and other practitioners from places like the BBC, City University London, Scraperwiki, Zeit Online, and many others. The project has received coverage in the New York Times, Liberation, Tech Crunch, World Editors Forum, and other major publications.
Data Structures and Algorithm Analysis in C++ is an advanced algorithms book that bridges the gap between traditional CS2 and Algorithms Analysis courses. As the speed and power of computers increases, so does the need for effective programming and algorithm analysis. By approaching these skills in tandem, Mark Allen Weiss teaches readers to develop well-constructed, maximally efficient programs using the C++ programming language. This book explains topics from binary heaps to sorting to NP-completeness, and dedicates a full chapter to amortized analysis and advanced data structures and their implementation. Figures and examples illustrating successive stages of algorithms contribute to Weiss' careful, rigorous and in-depth analysis of each type of algorithm.
