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

Summary


In this chapter, we have discussed the most commonly used distributions in the finance domain and associated metrics computations in R; sampling (random and stratified); measures of central tendencies; correlations and types of correlation used for model selections in time series; hypothesis testing (one-tailed/two-tailed) with known and unknown variance; detection of outliers; parameter estimation; and standardization/normalization of attributes in R to bring attributes on comparable scales.

In the next chapter, analysis done in R associated with simple linear regression, multivariate linear regression, ANOVA, feature selection, ranking of variables, wavelet analysis, fast Fourier transformation, and Hilbert transformation will be covered.