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

Questions


  1. Define regression and how you can implement in R.

  2. How do you find the coefficient of determination for linear regression / multiple regression in R?

  3. How do you find the confidence interval for a prediction fitted with linear regression / multiple regression in R?

  4. How will you detect multicollinearity in R in multiple regression?

  5. What is the significance of ANOVA and how will you use it to compare the results of two linear regression models?

  6. How do you perform feature selection in R for multiple linear regression?

  7. How do you rank significance attributes in a multiple linear regression model in R?

  8. How do you install the waveslim package and load it into the R workspace?

  9. How do you plot a time series and extract the head and tail of the time series?

  10. How would you know the class of a variable created by the fft function?

  11. How do you use the dwt function using any given filter and take inverse dwt?

  12. How do you extract the real and imaginary parts of a series?

  13. How would you use fast Fourier transformation...