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Learning Quantitative Finance with R

You're reading from  Learning Quantitative Finance with R

Product type Book
Published in Mar 2017
Publisher Packt
ISBN-13 9781786462411
Pages 284 pages
Edition 1st Edition
Languages
Authors (2):
Dr. Param Jeet Dr. Param Jeet
Profile icon Dr. Param Jeet
PRASHANT VATS PRASHANT VATS
Profile icon PRASHANT VATS
View More author details

Table of Contents (16) Chapters

Learning Quantitative Finance with R
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
1. Introduction to R 2. Statistical Modeling 3. Econometric and Wavelet Analysis 4. Time Series Modeling 5. Algorithmic Trading 6. Trading Using Machine Learning 7. Risk Management 8. Optimization 9. Derivative Pricing

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...

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