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

Monte Carlo simulation


Monte Carlo simulation plays a very important role in risk management. Even if we have access to all the relevant information pertaining to risk associated with a firm, it is still not possible to predict the associated risk and quantify it. By the means of Monte Carlo simulation, we can generate all the possible scenarios of risk and using it, we can evaluate the impact of risk and build a better risk mitigation strategy.

Monte Carlo simulation is a computational mathematical approach which gives the user the option of creating a range of possible outcome scenarios, including extreme ones, with the probability associated with each outcome. The possible outcomes are also drawn on the expected line of distribution, which may be closer to real outcomes. The range of possible outcomes can be used in risk analysis for building the models and drawing the inferences. Analysis is repeated again and again using a different set of random values from the probability function...