Many introductory books on mathematical finance also outline some com puter algorithms. My goal is to contribute a closer look at algorithmic issues that arise from complex forms of the underlying pricing models-issues many practitioners need to solve sooner or later in their careers. This book takes such a close look at uncertain volatility models, an exten sion of Black-Scholes theory.It discusses applications to exotic option portfo lios with barriers and early exercise features. It describes an object-oriented C++ solution, included in source code on the accompanying CD. Practitioners and students who need to build analytic software libraries may benefit from reading this book and studying the software. The book focuses on a family of mathematical models, while in the field one encounters greater variation in instrument properties. In both cases mathematical and financial knowledge must be complemented by good programming skills to produce the best system. Analytic software needs design-a central message of the later chapters of this book. This book has come out of my Ph.D. thesis. I am very grateful to my academic advisor, Marco Avellaneda of New York University, who taught me mathematical finance and uncertain volatility. Computational finance be came exciting for me because Marco encouraged an algorithmic approach to uncertain volatility. I thank Afshin Bayrooti, Vladimir Finkelstein, and Antonio Paras for giving valuable feedback. Antonio is the co-inventor of the original uncertain volatility model, A-UVM. Richard Holmes has found a crucial bug in an early implementation of the software.
This book introduces Uncertain Volatility Models in mathematical finance. Uncertain Volatility Models evaluate option portfolios under worst- and best-case scenarios when the volatility coefficient of the pricing model cannot be determined exactly. The user defines subjective volatility constraints; within those constraints, extremal prices are computed. This book studies two types of constraints: volatility bands with upper and lower bounds, and shock scenarios with short periods of extreme volatility, but unknown timing. Uncertain Volatility Models are nonlinear. Worst- and best-case scenarios applied to isolated option positions do not always lead to the same extremal volatility. When applied to an options portfolio, a diversification effect reduces the overall exposure to volatility fluctuations within the subjective constraints. This book explores algorithmic issues that arise due to nonlinearity. Because Uncertain Volatility Models must be applied to option portfolios as a whole, they are difficult to implement on a computer if the portfolio contains barrier or American options. This book is for graduate students, researchers and practitioners who wish to study advanced aspects of volatility risk in portfolios of vanilla and exotic options.
