Book Image

Introduction to R for Quantitative Finance

Book Image

Introduction to R for Quantitative Finance

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
About the Authors
About the Reviewers

Credit risk management

  • F. Black and J. Cox (1976), Valuing Corporate Securities: Some Effects of Bond Indenture Provisions, Journal of Finance 31, 351-367.

  • D. Wuertz and many others (2012), fOptions: Basics of Option Valuation, R package version 2160.82. Available at

  • K. Giesecke (2004), Credit Risk Modeling and Valuation: An Introduction. Available at SSRN: or

  • I. Kojadinovic and J. Yan (2010), Modeling Multivariate Distributions with Continuous Margins Using the copula R Package, Journal of Statistical Software 34, No. 9, 1-20. Available at

  • J. Yan (2007), Enjoy the Joy of Copulas: With a Package Copula, Journal of Statistical Software 21, No. 4, 1-21. Available at

  • R. Merton (1974), On the Pricing of Corporate Debt: The Risk Structure of Interest Rates, Journal of Finance. 29, 449-470.

  • D. Sharma (2011), Innovation in Corporate Credit Scoring: Z-Score Optimization. Available at SSRN: or

  • S. M. Iacus (2009), sde: Simulation and Inference for Stochastic Differential Equations, R package version 2.0.10. Available at

  • A. Wittmann (2007), CreditMetrics: Functions for calculating the CreditMetrics risk model, R package version 0.0-2.

  • X. Robin, N. Turck, A. Hainard, N. Tiberti, F. Lisacek, J. C. Sanchez, and M. Müller (2011), pROC: an open-source package for R and S+ to analyze and compare ROC curves, BMC Bioinformatics 12, 77.