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 3. Econometric and Wavelet Analysis

In financial analytics, we need techniques to do predictive modeling for forecasting and finding the drivers for different target variables. In this chapter, we will discuss types of regression and how we can build a regression model in R for building predictive models. Also we will discuss, how we can implement a variable selection method and other aspects associated with regression. This chapter will not contain theoretical description but will just guide you in how to implement a regression model in R in the financial space. Regression analysis can be used for doing forecast on cross-sectional data in the financial domain. We will also cover frequency analysis of the data, and how transformations such as Fast Fourier, wavelet, Hilbert, haar transformations in time, and frequency domains help to remove noise in the data.

This chapter covers the following topics:

  • Simple linear regression

  • Multivariate linear regression

  • Multicollinearity

  • ANOVA

  • Feature...