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

Wavelet analysis


Time series information is not always sufficient to get insight into the data. Sometimes the frequency content of the data also contains important information about the data. In the time domain, Fourier transformation (FT) captures the frequency-amplitude of the data but it does not show when in time this frequency has happened. In the case of stationary data, all frequency components exist at any point in time but this is not true for non-stationary data. So, FT does not fit for non-stationary data. Wavelet transformation (WT) has the capacity to provide time and frequency information simultaneously in the form of time-frequency. WT is important to analyze financial time series as most of the financial time series are non-stationary. In the remainder of this chapter, wavelet analysis (WT), I will help you understand how to solve non-stationary data in R using wavelets analysis. Stock price/index data requires certain techniques or transformations to obtain further information...