Implementing Models in Quantitative Finance: Methods and Cases

ISBN-13: 9783642061073 / Angielski / Miękka / 2010 / 607 str.

Gianluca Fusai; Andrea Roncoroni

Opis

Introduction This book presents and develops major numerical methods currently used for solving problems arising in quantitative ?nance. Our presentation splits into two parts. Part I is methodological, and offers a comprehensive toolkit on numerical me- ods and algorithms. This includes Monte Carlo simulation, numerical schemes for partial differential equations, stochastic optimization in discrete time, copula fu- tions, transform-based methods and quadrature techniques. Part II is practical, and features a number of self-contained cases. Each case introduces a concrete problem and offers a detailed, step-by-step solution. Computer code that implements the cases and the resulting output is also included. The cases encompass a wide variety of quantitative issues arising in markets for equity, interest rates, credit risk, energy and exotic derivatives. The corresponding problems cover model simulation, derivative valuation, dynamic hedging, portfolio selection, risk management, statistical estimation and model calibration. R We provide algorithms implemented using either Matlab or Visual Basic for R Applications (VBA). Several codes are made available through a link accessible from the Editor s web site. Origin Necessity is the mother of invention and, as such, the present work originates in class notes and problems developed for the courses Numerical Methods in Finance and Exotic Derivatives offered by the authors at Bocconi University within the Master in Quantitative Finance and Insurance program (from 2000 2001 to 2003 2004) and the Master of Quantitative Finance and Risk Management program (2004 2005 to present)."

Recenzje

From the reviews:

"As the title suggests the book is divided into two parts. ... The style of the book is very inviting and it should be on the shelf of every serious researcher and practitioner in quantitative finance, including graduate students. Teachers could easily use the book in their applied courses. Overall, I think the book is a clear self-contained guide to implementing models in quantitative finance and as such it is going to be very popular in quant and academic circles." (Ita Cirovic Donev, MathDL, July, 2008)

"This application-oriented book presents the major numerical methods currently used and describes how these methods can be used to solve problems in quantitative finance. ... Each chapter includes exercises for student practice ... . The presentation is at an intermediate-advanced level and serves as an introductory tutorial to the field of quantitative finance. Quantitative analysts, researchers and graduate students in quantitative finance will find this book useful." (Stefan Henn, Mathematical Reviews, Issue 2009 g)

Spis treści

Methods.- Static Monte Carlo.- Dynamic Monte Carlo.- Dynamic Programming for Stochastic Optimization.- Finite Difference Methods.- Numerical Solution of Linear Systems.- Quadrature Methods.- The Laplace Transform.- Structuring Dependence using Copula Functions.- Problems.- Portfolio Selection: “Optimizing” an Error.- Alpha, Beta and Beyond.- Automatic Trading: Winning or Losing in a kBit.- Estimating the Risk-Neutral Density.- An “American” Monte Carlo.- Fixing Volatile Volatility.- An Average Problem.- Quasi-Monte Carlo: An Asian Bet.- Lookback Options: A Discrete Problem.- Electrifying the Price of Power.- A Sparkling Option.- Swinging on a Tree.- Floating Mortgages.- Basket Default Swaps.- Scenario Simulation Using Principal Components.- Parametric Estimation of Jump-Diffusions.- Nonparametric Estimation of Jump-Diffusions.- A Smiling GARCH.

O autorze

Gianluca Fusai is Associate Professor in Financial Calculus at Università degli Studi del Piemonte Orientale (Italy) and a Research Associate at Financial Options Research Center, Univeristy of Warwick. He holds a Ph.D in Finance from the Warwick Business School and a MS in Statistics and Operational Research from University of Essex, UK. His research interest are Financial Engineering, Numerical Methods, Portfolio Selection, and Financial Statistics. On this topics he has published in journals like Journal of Computational Finance, Risk, Annals of Applied Probability, International Journal of Theoretical and Applied Finance. He has worked as a consultant in the private sector (Mediolanum Assicurazioni, Selenia Luxco, Nike Consulting, Software Company, Equitable House).

Andrea Roncoroni is Associate Professor of Finance at ESSEC Business School (Paris-Singapore), Senior Lecturer at Bocconi University (Milan), and Co-director of the Master in Energy Finance at MIP - Politecnico di Milano. He holds PhDs in Applied Mathematics and in Finance. His research interests cover Energy and Commodity Finance, Financial Modeling, Risk Management and Derivative Structuring. He consults for private companies and lectures for private and public institutions (International Energy Agency, Italian Stock Exchange, Italian Energy Authority, Italian Power Exchange, University Paris Dauphine, University of Oslo). He regularly publishes in academic journals (J.of Business, J.of Banking and Finance, Intl.J.of Business).

Blurb

This book puts numerical methods into action for the purpose of solving concrete problems arising in quantitative finance. Part one develops a comprehensive toolkit including Monte Carlo simulation, numerical schemes for partial differential equations, stochastic optimization in discrete time, copula functions, transform-based methods and quadrature techniques. The content originates from class notes written for courses on numerical methods for finance and exotic derivative pricing held by the authors at Bocconi University since the year 2000. Part two proposes eighteen self-contained cases covering model simulation, derivative valuation, dynamic hedging, portfolio selection, risk management, statistical estimation and model calibration. It encompasses a wide variety of problems arising in markets for equity, interest rates, credit risk, energy and exotic derivatives. Each case introduces a problem, develops a detailed solution and illustrates empirical results. Proposed algorithms are implemented using either Matlab^® or Visual Basic for Applications^® in collaboration with contributors.