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Financial Risk Modelling and Portfolio Optimization with R

Financial Risk Modelling and Portfolio Optimization with R

by Bernhard Pfaff
Publisher: John Wiley & Sons
Release Date: January 2013
ISBN: 9781118477120
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Book Description

Introduces the latest techniques advocated for measuring financial market risk and portfolio optimisation, and provides a plethora of R code examples that enable the reader to replicate the results featured throughout the book.

Financial Risk Modelling and Portfolio Optimisation with R:

  • Demonstrates techniques in modelling financial risks and applying portfolio optimisation techniques as well as recent advances in the field.

  • Introduces stylised facts, loss function and risk measures, conditional and unconditional modelling of risk; extreme value theory, generalised hyperbolic distribution, volatility modelling and concepts for capturing dependencies.

  • Explores portfolio risk concepts and optimisation with risk constraints.

  • Enables the reader to replicate the results in the book using R code.

  • Is accompanied by a supporting website featuring examples and case studies in R.

Graduate and postgraduate students in finance, economics, risk management as well as practitioners in finance and portfolio optimisation will find this book beneficial. It also serves well as an accompanying text in computer-lab classes and is therefore suitable for self-study.

Table of Contents

  1. Cover
  2. Statistics in Practice
  3. Title Page
  4. Copyright
  5. Preface
  6. List of abbreviations
  7. Part I: Motivation
    1. Chapter 1: Introduction
      1. Reference
    2. Chapter 2: A brief course in R
      1. 2.1 Origin and development
      2. 2.2 Getting help
      3. 2.3 Working with R
      4. 2.4 Classes, methods and functions
      5. 2.5 The accompanying package FRAPO
      6. References
    3. Chapter 3: Financial market data
      1. 3.1 Stylized facts on financial market returns
      2. 3.2 Implications for risk models
      3. References
    4. Chapter 4: Measuring risks
      1. 4.1 Introduction
      2. 4.2 Synopsis of risk measures
      3. 4.3 Portfolio risk concepts
      4. References
    5. Chapter 5: Modern portfolio theory
      1. 5.1 Introduction
      2. 5.2 Markowitz portfolios
      3. 5.3 Empirical mean–variance portfolios
      4. References
  8. Part II: Risk Modelling
    1. Chapter 6: Suitable distributions for returns
      1. 6.1 Preliminaries
      2. 6.2 The generalized hyperbolic distribution
      3. 6.3 The generalized lambda distribution
      4. 6.4 Synopsis of R packages for the GHD
      5. 6.5 Synopsis of R packages for GLD
      6. 6.6 Applications of the GHD to risk modelling
      7. 6.7 Applications of the GLD to risk modelling and data analysis
      8. References
    2. Chapter 7: Extreme value theory
      1. 7.1 Preliminaries
      2. 7.2 Extreme value methods and models
      3. 7.3 Synopsis of R packages
      4. 7.4 Empirical applications of EVT
      5. References
    3. Chapter 8: Modelling volatility
      1. 8.1 Preliminaries
      2. 8.2 The class of ARCH models
      3. 8.3 Synopsis of R packages
      4. 8.4 Empirical application of volatility models
      5. References
    4. Chapter 9: Modelling dependence
      1. 9.1 Overview
      2. 9.2 Correlation, dependence and distributions
      3. 9.3 Copulae
      4. 9.4 Synopsis of R packages
      5. 9.5 Empirical applications of copulae
      6. References
  9. Part III: Portfolio Optimization Approaches
    1. Chapter 10: Robust portfolio optimization
      1. 10.1 Overview
      2. 10.2 Robust statistics
      3. 10.3 Robust optimization
      4. 10.4 Synopsis of R packages
      5. 10.5 Empirical applications
      6. References
    2. Chapter 11: Diversification reconsidered
      1. 11.1 Introduction
      2. 11.2 Most diversified portfolio
      3. 11.3 Risk contribution constrained portfolios
      4. 11.4 Optimal tail-dependent portfolios
      5. 11.5 Synopsis of R packages
      6. 11.6 Empirical applications
      7. References
    3. Chapter 12: Risk-optimal portfolios
      1. 12.1 Overview
      2. 12.2 Mean-VaR portfolios
      3. 12.3 Optimal CVaR portfolios
      4. 12.4 Optimal draw-down portfolios
      5. 12.5 Synopsis of R packages
      6. 12.6 Empirical applications
      7. References
    4. Chapter 13: Tactical asset allocation
      1. 13.1 Overview
      2. 13.2 Survey of selected time series models
      3. 13.3 Black–Litterman approach
      4. 13.4 Copula opinion and entropy pooling
      5. 13.5 Synopsis of R packages
      6. 13.6 Empirical applications
      7. References
  10. Appendix A: Package overview
    1. A.1 Packages in alphabetical order
    2. A.2 Packages ordered by topic
    3. References
  11. Appendix B: Time series data
    1. B.1 Date-time classes
    2. B.2 The ts class in the base package stats
    3. B.3 Irregular-spaced time series
    4. B.4 The package timeSeries
    5. B.5 The package zoo
    6. B.6 The packages tframe and xts
    7. References
  12. Appendix C: Back-testing and reporting of portfolio strategies
    1. C.1 R packages for back-testing
    2. C.2 R facilities for reporting
    3. C.3 Interfacing databases
    4. References
  13. Appendix D: Technicalities
  14. Index
  15. Statistics in Practice