Introduction to R for Quantitative Finance
Solve a diverse range of problems with R, one of the most powerful tools for quantitative finance




Description:

Introduction to R for Quantitative Finance will show you how to solve real-world quantitative finance problems using the statistical computing language R. The book covers diverse topics ranging from time series analysis to financial networks. Each chapter briefly presents the theory behind specific concepts and deals with solving a diverse range of problems using R with the help of practical examples.

This book will be your guide on how to use and master R in order to solve real-world quantitative finance problems. This book covers the essentials of quantitative finance, taking you through a number of clear and practical examples in R that will not only help you to understand the theory, but how to effectively deal with your own real-life problems.

Starting with time series analysis, you will also learn how to optimize portfolios and how asset pricing models work. The book then covers fixed income securities and derivatives like credit risk management. The last chapters of this book will also provide you with an overview of exciting topics like extreme values and network analysis in quantitative finance.

What you will learn from this book

• How to model and forecast house prices and improve hedge ratios using cointegration and model volatility
• How to understand the theory behind portfolio selection and how it can be applied to real-world data
• How to utilize the Capital Asset Pricing Model and the Arbitrage Pricing Theory
• How to understand the basics of fixed income instruments
• You will discover how to use discrete- and continuous-time models for pricing derivative securities
• How to successfully work with credit default models and how to model correlated defaults using copulas
• How to understand the uses of the Extreme Value Theory in insurance and fi nance, model fitting, and risk measure calculation
  

Table of Contents