Introduction to R for Quantitative Finance
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Table of Contents
Sample Chapters
  • Use time series analysis to model and forecast house prices
  • Estimate the term structure of interest rates using prices of government bonds
  • Detect systemically important financial institutions by employing financial network analysis

Book Details

Language : English
Paperback : 164 pages [ 235mm x 191mm ]
Release Date : November 2013
ISBN : 178328093X
ISBN 13 : 9781783280933
Author(s) : Gergely Daróczi, Michael Puhle, Edina Berlinger, Péter Csóka, Daniel Havran, Márton Michaletzky, Zsolt Tulassay, Kata Váradi, Agnes Vidovics-Dancs
Topics and Technologies : All Books, Open Source

Table of Contents

Chapter 1: Time Series Analysis
Chapter 2: Portfolio Optimization
Chapter 3: Asset Pricing Models
Chapter 4: Fixed Income Securities
Chapter 5: Estimating the Term Structure of Interest Rates
Chapter 6: Derivatives Pricing
Chapter 7: Credit Risk Management
Chapter 8: Extreme Value Theory
Chapter 9: Financial Networks
Appendix: References
  • Chapter 1: Time Series Analysis
    • Working with time series data
    • Linear time series modeling and forecasting
      • Modeling and forecasting UK house prices
        • Model identification and estimation
        • Model diagnostic checking
        • Forecasting
    • Cointegration
      • Cross hedging jet fuel
    • Modeling volatility
      • Volatility forecasting for risk management
        • Testing for ARCH effects
        • GARCH model specification
        • GARCH model estimation
        • Backtesting the risk model
        • Forecasting
    • Summary
    • Chapter 2: Portfolio Optimization
      • Mean-Variance model
      • Solution concepts
        • Theorem (Lagrange)
      • Working with real data
      • Tangency portfolio and Capital Market Line
      • Noise in the covariance matrix
      • When variance is not enough
      • Summary
      • Chapter 3: Asset Pricing Models
        • Capital Asset Pricing Model
        • Arbitrage Pricing Theory
        • Beta estimation
          • Data selection
          • Simple beta estimation
          • Beta estimation from linear regression
        • Model testing
          • Data collection
          • Modeling the SCL
          • Testing the explanatory power of the individual variance
        • Summary
        • Chapter 4: Fixed Income Securities
          • Measuring market risk of fixed income securities
            • Example – implementation in R
          • Immunization of fixed income portfolios
            • Net worth immunization
            • Target date immunization
            • Dedication
          • Pricing a convertible bond
          • Summary
            • Chapter 6: Derivatives Pricing
              • The Black-Scholes model
              • The Cox-Ross-Rubinstein model
              • Connection between the two models
              • Greeks
              • Implied volatility
              • Summary
              • Chapter 7: Credit Risk Management
                • Credit default models
                  • Structural models
                  • Intensity models
                • Correlated defaults – the portfolio approach
                • Migration matrices
                • Getting started with credit scoring in R
                • Summary
                • Chapter 8: Extreme Value Theory
                  • Theoretical overview
                  • Application – modeling insurance claims
                    • Exploratory data analysis
                    • Tail behavior of claims
                    • Determining the threshold
                    • Fitting a GPD distribution to the tails
                    • Quantile estimation using the fitted GPD model
                    • Calculation of expected loss using the fitted GPD model
                  • Summary
                  • Chapter 9: Financial Networks
                    • Representation, simulation, and visualization of financial networks
                    • Analysis of networks’ structure and detection of topology changes
                    • Contribution to systemic risk – identification of SIFIs
                    • Summary
                    • Appendix: References
                      • Time series analysis
                      • Portfolio optimization
                      • Asset pricing
                      • Fixed income securities
                      • Estimating the term structure of interest rates
                      • Derivatives Pricing
                      • Credit risk management
                      • Extreme value theory
                      • Financial networks

                      Gergely Daróczi

                      Gergely Daróczi is a Ph.D. candidate in Sociology with around eight years' experience in data management and analysis tasks within the R programming environment. Besides teaching Statistics at different Hungarian universities and doing data analysis jobs for several years, Gergely has founded and coordinated a UK-based online reporting startup company recently. This latter software or platform as a service which is called will potentially provide an intuitive frontend and an interface to all the methods and techniques covered in the book. His role in the book was to provide R implementation of the QF problems and methods.

                      Michael Puhle

                      Michael Puhle obtained a Ph.D. in Finance from the University of Passau in Germany. He worked for several years as a Senior Risk Controller at Allianz Global Investors in Munich, and as an Assistant Manager at KPMG's Financial Risk Management practice, where he was advising banks on market risk models. Michael is also the author of Bond Portfolio Optimization published by Springer Publishing.

                      Edina Berlinger

                      Edina Berlinger has a Ph.D. in Economics from the Corvinus University of Budapest. She is an Associate Professor, teaching corporate finance, investments, and financial risk management. She is the Head of Department for Finance of the university and is also the Chair of the Finance Sub committee the Hungarian Academy of Sciences. Her expertise covers student loan systems, risk management, and, recently, network analysis. She has led several research projects in student loan design, liquidity management, heterogeneous agent models, and systemic risk.

                      Péter Csóka

                      Péter Csóka is an Associate Professor at the Department of Finance, Corvinus University of Budapest, and a research fellow in the Game Theory Research Group, Centre For Economic and Regional Studies, Hungarian Academy of Sciences. He received his Ph.D. in Economics from Maastricht University in 2008. His research topics include risk measures, risk capital allocation, game theory, corporate finance, and general equilibrium theory. He is currently focused on analyzing risk contributions for systemic risk and for illiquid portfolios. He has papers published in journals such as Mathematical Methods of Operational Research, European Journal of Operational Research, Games and Economic Behaviour, and Journal of Banking and Finance. He is the Chair of the organizing committee of the Annual Financial Market Liquidity Conference in Budapest.

                      Daniel Havran

                      Daniel Havran is a Post Doctoral Fellow at the Institute of Economics, Centre for Economic and Regional Studies, Hungarian Academy of Sciences. He also holds a part-time Assistant Professorship position at the Corvinus University of Budapest, where he teaches Corporate Finance (BA and Ph.D. levels), and Credit Risk Management (MSc) courses. He obtained his Ph.D. in Economics at Corvinus University of Budapest in 2011. His research interests are corporate cash, funding liquidity management, and credit derivatives over-the-counter markets.

                      Márton Michaletzky

                      Márton Michaletzky obtained his Ph.D. degree in Economics in 2011 from Corvinus University of Budapest. Between 2000 and 2003, he has been a Risk Manager and Macroeconomic Analyst with Concorde Securities Ltd. As Capital Market Transactions Manager, he gained experience in an EUR 3 bn securitization at the Hungarian State Motorway Management Company. In 2012, he took part in the preparation of an IPO and the private placement of a Hungarian financial services provider. Prior to joining DBH Investment, he was an assistant professor at the Department of Finance of CUB.

                      Zsolt Tulassay

                      Zsolt Tulassay works as a Quantitative Analyst at a major US investment bank, validating derivatives pricing models. Previously, Zsolt worked as an Assistant Lecturer at the Department of Finance at Corvinus University, teaching courses on Derivatives, Quantitative Risk Management, and Financial Econometrics. Zsolt holds MA degrees in Economics from Corvinus University of Budapest and Central European University. His research interests include derivatives pricing, yield curve modeling, liquidity risk, and heterogeneous agent models.

                      Kata Váradi

                      Kata Váradi is an Assistant Professor at the Department of Finance, Corvinus University of Budapest since 2013. Kata graduated in Finance in 2009 from Corvinus University of Budapest, and was awarded a Ph.D. degree in 2012 for her thesis on the analysis of the market liquidity risk on the Hungarian stock market. Her research areas are market liquidity, fixed income securities, and networks in healthcare systems. Besides doing research, she is active in teaching as well. She teaches mainly Corporate Finance, Investments, Valuation, and Multinational Financial Management.

                      Agnes Vidovics-Dancs

                      Agnes Vidovics-Dancs is a Ph.D. candidate and an Assistant Professor at the Department of Finance, Corvinus University of Budapest. Previously, she worked as a Junior Risk Manager in the Hungarian Government Debt Management Agency. Her main research areas are government debt management in general, especially sovereign crises and defaults.
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                      Please let us know if you have found any errors not listed on this list by completing our errata submission form. Our editors will check them and add them to this list. Thank you.


                      - 2 submitted: last submission 08 Jul 2014

                      The code bundle has been updated with the source code for R.

                      Page no: 78 | Errata type: Code


                      The given code is:

                      > c(floor(min(y[, 1])), max(m[, ncol(m)]))


                      It should be:

                      > c(floor(min(m[, 1])), max(m[, ncol(m)]))



                      Page no: 79 | Errata type: Code

                      The given code is:

                      > c(floor(min(y[, 1])), apply(as.matrix(m[, h]), 2, max) + theta *
                      (apply(as.matrix(m[, h + 1]), 2, max) - apply(as.matrix(m[, h]), 2,
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                      It should be:

                      > c(floor(min(m[, 1])), apply(as.matrix(m[, h]), 2, max) + theta *
                      (apply(as.matrix(m[, h + 1]), 2, max) - apply(as.matrix(m[, h]), 2,
                      max)), max(m[, ncol(m)]))

                      Sample chapters

                      You can view our sample chapters and prefaces of this title on PacktLib or download sample chapters in PDF format.

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                      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

                      In Detail

                      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.


                      This book is a tutorial guide for new users that aims to help you understand the basics of and become accomplished with the use of R for quantitative finance.

                      Who this book is for

                      If you are looking to use R to solve problems in quantitative finance, then this book is for you. A basic knowledge of financial theory is assumed, but familiarity with R is not required. With a focus on using R to solve a wide range of issues, this book provides useful content for both the R beginner and more experience users.

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