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Financial Data Analytics with R: Monte-Carlo Validation     КНИГИ » ПРОГРАММИНГ

Название: Financial Data Analytics with R: Monte-Carlo Validation
Автор: Jenny K. Chen
Издательство: CRC Press
Год: 2025
Страниц: 298
Язык: английский
Формат: pdf (true)
Размер: 13.7 MB

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 aims to serve as a comprehensive guide for students, researchers, and practitioners seeking to harness the power of R for analyzing financial data. The journey begins with a solid foundation in both financial theory and R programming, gradually progressing to more advanced topics such as time series analysis, risk and risk management. The practical examples provided throughout the book are rooted in real-world financial scenarios, offering readers a bridge between academic concepts and practical applications.

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.

This book is structured in ten chapters from classical statistical modeling all the way to time series modeling. As the opening of this book, Chapter 1 serves as a fundamental exploration to understand R language. From the very basics such as from acquiring R software, installing it, upgrading R packages, to using R for data management and simulating data to showcase the renowned Central Limit Theorem in statistics.

With a basic understanding of R from Chapter 1, Chapter 2 to Chapter 4 are designed to analyze financial data for continuous data. Chapter 2 is for financial data analysis with linear regression models, which is the fundamental statistical model in financial data analysis. Linear regression modeling is commonly used to understand and quantify relationships between variables in finance, which can aid in making informed decisions and predictions. In this chapter, we first give an overview of linear regression modeling and then illustrate a step-by-step approach to use R to analyze publicly available data (i.e., wages) with both simple linear regression and multiple linear regression. Concluding Chapter 2, we undertake a series of Monte-Carlo simulation studies designed to show the potential misleading conclusions that arise when the fundamental assumptions underlying linear regression are violated. Through these simulation studies, we aim to illuminate the risks associated with disregarding the underlying assumptions and encourage a more informed approach to financial analysis using linear regression.

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

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