
Financial Data Analytics with R
Monte-Carlo Validation
Jenny K. Chen(Author)
Chapman & Hall/CRC (Publisher)
1st Edition
Published on 12. July 2024
Book
Paperback/Softback
276 pages
978-1-032-74149-9 (ISBN)
Description
Financial Data Analysis with R: Monte-Carlo Validation is a comprehensive exploration of statistical methodologies and their applications in finance. Readers are taken on a journey in each chapter through practical explanations and examples, enabling them to develop a solid foundation of these methods in R and their applications in finance.
This book serves as an indispensable resource for finance professionals, analysts, and enthusiasts seeking to harness the power of data-driven decision-making.
The book goes beyond just teaching statistical methods in R and incorporates a unique section of informative Monte-Carlo simulations. These Monte-Carlo simulations are uniquely designed to showcase the reader the potential consequences and misleading conclusions that can arise when fundamental model assumptions are violated. Through step-by-step tutorials and realworld cases, readers will learn how and why model assumptions are important to follow.
With a focus on practicality, Financial Data Analysis with R: Monte-Carlo Validation equips readers with the skills to construct and validate financial models using R. The Monte-Carlo simulation exercises provide a unique opportunity to understand the methods further, making this book an essential tool for anyone involved in financial analysis, investment strategy, or risk management. Whether you are a seasoned professional or a newcomer to the world of financial analytics, this book serves as a guiding light, empowering you to navigate the landscape of finance with precision and confidence.
Key Features:
An extensive compilation of commonly used financial data analytics methods from fundamental to advanced levels
Learn how to model and analyze financial data with step-by-step illustrations in R and ready-to-use publicly available data
Includes Monte-Carlo simulations uniquely designed to showcase the reader the potential consequences and misleading conclusions that arise when fundamental model assumptions are violated
Data and computer programs are available for readers to replicate and implement the models and methods themselves
This book serves as an indispensable resource for finance professionals, analysts, and enthusiasts seeking to harness the power of data-driven decision-making.
The book goes beyond just teaching statistical methods in R and incorporates a unique section of informative Monte-Carlo simulations. These Monte-Carlo simulations are uniquely designed to showcase the reader the potential consequences and misleading conclusions that can arise when fundamental model assumptions are violated. Through step-by-step tutorials and realworld cases, readers will learn how and why model assumptions are important to follow.
With a focus on practicality, Financial Data Analysis with R: Monte-Carlo Validation equips readers with the skills to construct and validate financial models using R. The Monte-Carlo simulation exercises provide a unique opportunity to understand the methods further, making this book an essential tool for anyone involved in financial analysis, investment strategy, or risk management. Whether you are a seasoned professional or a newcomer to the world of financial analytics, this book serves as a guiding light, empowering you to navigate the landscape of finance with precision and confidence.
Key Features:
An extensive compilation of commonly used financial data analytics methods from fundamental to advanced levels
Learn how to model and analyze financial data with step-by-step illustrations in R and ready-to-use publicly available data
Includes Monte-Carlo simulations uniquely designed to showcase the reader the potential consequences and misleading conclusions that arise when fundamental model assumptions are violated
Data and computer programs are available for readers to replicate and implement the models and methods themselves
Reviews / Votes
"...this text stands out as a thorough introduction that effectively bridges classical statistical methods with real-world financial applications. Its seamless integration of R code, data-driven demonstrations, and stepwise advancement of concepts ensures that readers will not only understand how to run a model, but also why it is suitable for particular financial questions. For instructors teaching finance-focused data analytics and for professionals seeking to enhance their statistical skill set within R, Financial Data Analytics with R is a recommended addition to the bookshelf."- Tony Sit, Financial Data Analytics with R: Monte-Carlo Validation. Journal of the American Statistical Association, July 2025
More details
Language
English
Place of publication
Boca Raton
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
Professional and scholarly
Professional Practice & Development
Illustrations
47 s/w Abbildungen, 47 s/w Zeichnungen, 5 s/w Tabellen
5 Tables, black and white; 47 Line drawings, black and white; 47 Illustrations, black and white
Dimensions
Height: 234 mm
Width: 156 mm
Thickness: 16 mm
Weight
456 gr
ISBN-13
978-1-032-74149-9 (9781032741499)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions

Book
07/2024
1st Edition
Chapman & Hall/CRC
€242.20
Shipment within 10-20 days

E-Book
07/2024
1st Edition
Chapman and Hall
€89.99
Available for download

E-Book
07/2024
1st Edition
Chapman and Hall
€89.99
Available for download
Person
Jenny K. Chen graduated with a Master's and Bachelor's degree in the Department of Statistics and Data Science at Cornell University. With expertise honed through academic pursuits and her current role as a quantitative product manager at Morgan Stanley, she is particularly interested in the applications of statistical modelling in finance and portfolio management. She was the youngest published author at the Joint Statistical Meetings in 2016 and has published several research papers in statistical modelling and data analytics.
Content
1. Introduction to R 2. Linear Regression 3. Transition from Linear to Nonlinear
Regression 4. Nonlinear Regression Modeling 5. The Logistic Regression 6. The Poisson Regression: Models for Count Data 7. Autoregressive Integrated Moving-Average Models 8. Generalized AutoRegressive Conditional Heteroskedasticity Model 9. Cointegration 10. Financial Statistical Modeling in Risk and Wealth Management Bibliography
Regression 4. Nonlinear Regression Modeling 5. The Logistic Regression 6. The Poisson Regression: Models for Count Data 7. Autoregressive Integrated Moving-Average Models 8. Generalized AutoRegressive Conditional Heteroskedasticity Model 9. Cointegration 10. Financial Statistical Modeling in Risk and Wealth Management Bibliography