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

General time series


A time series is the sequence of data usually collected at regular intervals. There are a lot of domains where information is stored in time series form and needs to be analyzed for future planning.

For example, in the financial domain, we have the daily/monthly data available for unemployment, GDP, daily exchange rates, share prices, and so on. So all the investors or the people working in financial institutions need to plan their future strategy and so they want to analyze the time series data. Thus time series play a crucial role in the financial domain.

Time series data is very unpredictable in nature and to understand the data we need to decompose the time series data into various components, as given here:

  • Trend: This is a pattern of long-term movements in the mean of time series data. The trend may be linear or nonlinear and keeps changing across time. There is no sure process to identify the exact trend but if it is behaving monotonously then it is possible to estimate...