This book makes quantitative finance (almost) easy! Its new visual approach makes quantitative finance accessible to a broad audience, including those without strong backgrounds in math or finance. Michael Lovelady introduces a simplified but powerful technique for calculating profit probabilities and graphically representing the outcomes. Lovelady's "pictures" highlight key characteristics of structured securities such as the increased likelihood of profits, the level of virtual dividends being generated, and market risk exposures. After explaining his visual approach, he applies it to one of today's hottest investing trends: lower-volatility, higher-income strategies. Because of today's intense interest in alternative investments and structured securities, this book reviews their unique advantages to investors, managers and advisors of retail and institutional portfolios. Visual Quantitative Finance focuses on key topics directly related to the design, pricing and communication of structured securities, including stochastic price projections and the framework underlying options pricing formulas. The key is Lovelady's explicit use of probabilities in a spreadsheet format. By working directly with the underlying assumptions, he transforms the Black-Scholes framework into five columns of a simple Excel spreadsheet, with no complicated formulas -- making structured securities far more intuitive to design, evaluate and manage. For all investors, students, and financial professionals who are interested in quantitative finance, risk measurement, options pricing, structured securities, or financial model building - and for everyone who needs to explain these topics to someone else. For those with quantitative backgrounds, this guide offers powerful new tools for design and risk management, simplifying the design and evaluation of innovative instruments. For everyone else, Lovelady makes the subject comprehensible for the first time.

Quantitative Finance: An Object-Oriented Approach in C++ provides readers with a foundation in the key methods and models of quantitative finance. Keeping the material as self-contained as possible, the author introduces computational finance with a focus on practical implementation in C++. Through an approach based on C++ classes and templates, the text highlights the basic principles common to various methods and models while the algorithmic implementation guides readers to a more thorough, hands-on understanding. By moving beyond a purely theoretical treatment to the actual implementation of the models using C++, readers greatly enhance their career opportunities in the field. The book also helps readers implement models in a trading or research environment. It presents recipes and extensible code building blocks for some of the most widespread methods in risk management and option pricing. Web Resource The author’s website provides fully functional C++ code, including additional C++ source files and examples. Although the code is used to illustrate concepts (not as a finished software product), it nevertheless compiles, runs, and deals with full, rather than toy, problems. The website also includes a suite of practical exercises for each chapter covering a range of difficulty levels and problem complexity.

Using stereoscopic images and other novel pedagogical features, this book offers a comprehensive introduction to quantitative finance.

Teach Your Students How to Become Successful Working Quants Quantitative Finance: A Simulation-Based Introduction Using Excel provides an introduction to financial mathematics for students in applied mathematics, financial engineering, actuarial science, and business administration. The text not only enables students to practice with the basic techniques of financial mathematics, but it also helps them gain significant intuition about what the techniques mean, how they work, and what happens when they stop working. After introducing risk, return, decision making under uncertainty, and traditional discounted cash flow project analysis, the book covers mortgages, bonds, and annuities using a blend of Excel simulation and difference equation or algebraic formalism. It then looks at how interest rate markets work and how to model bond prices before addressing mean variance portfolio optimization, the capital asset pricing model, options, and value at risk (VaR). The author next focuses on binomial model tools for pricing options and the analysis of discrete random walks. He also introduces stochastic calculus in a nonrigorous way and explains how to simulate geometric Brownian motion. The text proceeds to thoroughly discuss options pricing, mostly in continuous time. It concludes with chapters on stochastic models of the yield curve and incomplete markets using simple discrete models. Accessible to students with a relatively modest level of mathematical background, this book will guide your students in becoming successful quants. It uses both hand calculations and Excel spreadsheets to analyze plenty of examples from simple bond portfolios. The spreadsheets are available on the book’s CRC Press web page.

This book puts numerical methods in action for the purpose of solving practical problems in quantitative finance. The first part develops a toolkit in numerical methods for finance. The second part proposes twenty self-contained cases covering model simulation, asset pricing and hedging, risk management, statistical estimation and model calibration. Each case develops a detailed solution to a concrete problem arising in applied financial management and guides the user towards a computer implementation. The appendices contain "crash courses" in VBA and Matlab programming languages.

Edited by Guido Deboeck, a leading exponent in the use of computation intelligence methods in finance and economic forecasting, and the originator of SOM, Teuvo Kohonen. An 8-page color section makes this book unique, colorful and exciting to read. Each chapter contains exercises and solutions, perfectly suited to aid self-study.

To develop your confidence in F#, this tutorial will first introduce you to simpler tasks such as curve fitting. You will then advance to more complex tasks such as implementing algorithms for trading semi-automation in a practical scenario-based format. If you are a data analyst or a practitioner in quantitative finance, economics, or mathematics and wish to learn how to use F# as a functional programming language, this book is for you. You should have a basic conceptual understanding of financial concepts and models. Elementary knowledge of the .NET framework would also be helpful.