Financial Data Analytics with R

Monte-Carlo Validation

Jenny K. Chen

• Publisher: Chapman and Hall/CRC
• Year: 2024
• ISBN:
  9781040048702
• Edition: 1
• Topic: Matemáticas
• Subtopic: Probabilidad y estadística

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

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