Implementing Models in Quantitative Finance: Methods and Cases

This book puts numerical methods in action for the purpose of solving practical problems in quantitative finance. It fills a gap in the current published literature by delivering a case-study collection together with a self-contained course on major numerical methods developed and used by the finance industry.

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 20002001 to 20032004) and the Master of Quantitative Finance and Risk Management program (20042005 to present).

Fills a gap in the current published literature by delivering a case-study collection together with a self-contained course on major numerical methods developed and used by the finance industry Learning-by-doing approach: all steps detailed in a self-contained way Covers a range of numerical methods Blends theoretical presentation and practical implementations Originality in the choice of cases Provides detailed algorithm and the corresponding code Includes supplementary material: sn.pub/extras

Autorentext
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).

Inhalt
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.