Book Image

Learning Quantitative Finance with R

By : Dr. Param Jeet, PRASHANT VATS
Book Image

Learning Quantitative Finance with R

By: Dr. Param Jeet, PRASHANT VATS

Overview of this book

The role of a quantitative analyst is very challenging, yet lucrative, so there is a lot of competition for the role in top-tier organizations and investment banks. This book is your go-to resource if you want to equip yourself with the skills required to tackle any real-world problem in quantitative finance using the popular R programming language. You'll start by getting an understanding of the basics of R and its relevance in the field of quantitative finance. Once you've built this foundation, we'll dive into the practicalities of building financial models in R. This will help you have a fair understanding of the topics as well as their implementation, as the authors have presented some use cases along with examples that are easy to understand and correlate. We'll also look at risk management and optimization techniques for algorithmic trading. Finally, the book will explain some advanced concepts, such as trading using machine learning, optimizations, exotic options, and hedging. By the end of this book, you will have a firm grasp of the techniques required to implement basic quantitative finance models in R.
Table of Contents (16 chapters)
Learning Quantitative Finance with R
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Chapter 8. Optimization

Optimization is a way of selecting the best solution out of all feasible solutions. So, the first part of optimization is to formulate the problem according to given constraints, and to apply an advanced analytical method to get the best solution and help in making better decisions.

Optimization models play a pivotal role in quant and computational finance by solving complex problems more efficiently and accurately. Problems associated with asset allocation, risk management, option pricing, volatility estimation, portfolio optimization, and construction of index funds can be solved using optimization techniques such as nonlinear optimization models, quadratic programming formulations, and integer programming models. There is a variety of commercial and open source software available in the analytical space to solve these problems, and R is one of the preferred choices as it is open source and efficient.

In this chapter, we will be discussing some of the optimization...