Book Image

Introduction to R for Quantitative Finance

By : Gergely Daróczi, Michael Puhle, Edina Berlinger (EURO), Daniel Daniel Havran, Kata Váradi, Agnes Vidovics-Dancs, Agnes Vidovics Dancs, Michael Phule, Zsolt Tulassay, Peter Csoka, Marton Michaletzky, Edina Berlinger (EURO), Varadi Kata
Book Image

Introduction to R for Quantitative Finance

By: Gergely Daróczi, Michael Puhle, Edina Berlinger (EURO), Daniel Daniel Havran, Kata Váradi, Agnes Vidovics-Dancs, Agnes Vidovics Dancs, Michael Phule, Zsolt Tulassay, Peter Csoka, Marton Michaletzky, Edina Berlinger (EURO), Varadi Kata

Overview of this book

Introduction to R for Quantitative Finance will show you how to solve real-world quantitative fi nance problems using the statistical computing language R. The book covers diverse topics ranging from time series analysis to fi nancial networks. Each chapter briefl y 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 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 such as credit risk management.
Table of Contents (17 chapters)
Introduction to R for Quantitative Finance
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
Index

Chapter 4. Fixed Income Securities

In Chapter 3, Asset Pricing Models, we focused on models establishing a relationship between the risk measured by its beta, the price of financial instruments, and portfolios. The first model, CAPM, used an equilibrium approach, while the second, APT, has built on the no-arbitrage assumption.

The general objective of fixed income portfolio management is to set up a portfolio of fixed income securities with a given risk/reward profile. In other words, portfolio managers are aiming at allocating their funds into different fixed income securities, in a way that maximizes the expected return of the portfolio while adhering to the given investment objectives.

The process encompasses the dynamic modeling of the yield curve, the prepayment behavior, and the default of the securities. The tools used are time series analysis, stochastic processes, and optimization.

The risks of fixed income securities include credit risk, liquidity risk, and market risk among others...