Mathematics and Tools for Financial Engineering
Author | : Petros A. Ioannou |
Publisher | : SIAM |
Total Pages | : 285 |
Release | : 2021-09-07 |
ISBN-10 | : 9781611976762 |
ISBN-13 | : 1611976766 |
Rating | : 4/5 (766 Downloads) |
Download or read book Mathematics and Tools for Financial Engineering written by Petros A. Ioannou and published by SIAM. This book was released on 2021-09-07 with total page 285 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents an overview of fundamental concepts in mathematics and how they are applied to basic financial engineering problems, with the goal of teaching students to use mathematics and engineering tools to understand and solve financial problems. Part I covers mathematical preliminaries (set theory, linear algebra, sequences and series, real functions and analysis, numerical approximations and computations, basic optimization theory, and stochastic processes), and Part II addresses financial topics ranging from low- to high-risk investments (interest rates and value of money, bonds, dynamic asset modeling, portfolio theory and optimization, option pricing, and the concept of hedging). Based on lectures for a master’s program in financial engineering given by the author over 12 years at the University of Southern California, Mathematics and Tools for Financial Engineering contains numerous examples and problems, establishes a strong general mathematics background and engineering modeling techniques in a pedagogical fashion, and covers numerical techniques with applications to solving financial problems using different software tools. This textbook is intended for graduate and advanced undergraduate students in finance or financial engineering and is useful to readers with no prior knowledge in finance who want to understand some basic mathematical tools and theories associated with financial engineering. It is also appropriate as an overview of many mathematical concepts and engineering tools relevant to courses on numerical analysis, modeling and data science, numerical optimization, and approximation theory.