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Full 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
Contents
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
