- 298 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
About this book
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
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Information
Table of contents
- Cover Page
- Half-Title Page
- Title Page
- Copyright Page
- Contents
- List of Figures
- List of Tables
- Preface
- About the Author
- 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
- Index