This book describes computational finance tools. It covers fundamental numerical analysis and computational techniques, such as option pricing, and gives special attention to simulation and optimization. Many chapters are organized as case studies around portfolio insurance and risk estimation problems. In particular, several chapters explain optimization heuristics and how to use them for portfolio selection and in calibration of estimation and option pricing models. Such practical examples allow readers to learn the steps for solving specific problems and apply these steps to others. At the same time, the applications are relevant enough to make the book a useful reference. Matlab and R sample code is provided in the text and can be downloaded from the book's website. This title shows ways to build and implement tools that help test ideas; focuses on the application of heuristics; standard methods receive limited attention; and, presents as separate chapters problems from portfolio optimization, estimation of econometric models, and calibration of option pricing models.
This book describes computational finance tools. It covers fundamental numerical analysis and computational techniques, such as option pricing, and gives special attention to simulation and optimization. Many chapters are organized as case studies around portfolio insurance and risk estimation problems. In particular, several chapters explain optimization heuristics and how to use them for portfolio selection and in calibration of estimation and option pricing models. Such practical examples allow readers to learn the steps for solving specific problems and apply these steps to others. At the same time, the applications are relevant enough to make the book a useful reference. Matlab and R sample code is provided in the text and can be downloaded from the book's website. This title shows ways to build and implement tools that help test ideas; focuses on the application of heuristics; standard methods receive limited attention; and, presents as separate chapters problems from portfolio optimization, estimation of econometric models, and calibration of option pricing models.
VIP Value Investment Professionals, Switzerland
Teaches ways to make applications into software and test them empirically
1. Introduction
I. Fundamentals
2. Numerical Analysis in a Nutshell
3. Linear Equations and Least-Squares Problems
4. Finite Difference Methods
5. Binomial Trees
II Simulation
6. Generating Random Numbers 7. Modelling Dependencies
8. A Gentle Introduction to Financial Simulation
9. Financial Simulation at Work: Some Case Studies
III Optimization
10. Optimization Problems in Finance
11. Basic Methods
12. Heuristic Methods in a Nutshell
13. Portfolio Optimization
14. Econometric Models
15. Calibrating Option Pricing Models
VIP Value Investment Professionals, Switzerland
"This book aims at providing guidance which is practical and useful
for practitioners in finance with emphasis on computational
techniques which are manageable by modern day desktop personal
computers’ processing power when building, testing, comparing and
using mathematical and econometric models of finance in the pursuit
of analysis of actual financial market data in day to day
activities of financial analysts, be they students of courses in
finance programs or analysts in financial institutions."
--Zentralblatt MATH 2012-1236-91001
"With as much rigor as can be mastered by anyone in the
still-developing field of computational finance and a sense of
humor, the authors unravel its mysteries. The presentations are
clear and the models are practical --- these are the two
ingredients that make for a valuable book in this field. The book
is both practical in scope and rigorous on its theoretical
foundations. It is a must for anyone who needs to apply
quantitative methods for financial planning --- and who doesn’t
need to in our days?" --Stavros A. Zenios, University of Cyprus and
the Wharton Financial Institutions Center
"Numerical Methods and Optimization in Finance is an excellent
introduction to computational science. The combination of
methodology, software, and examples allows the reader to quickly
grasp and apply serious computational ideas." --Kenneth L. Judd,
Hoover Institution, Stanford University
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