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Market Risk Analysis, Volume II, Practical Financial Econometrics


Written by leading market risk academic, Professor Carol Alexander, Practical Financial Econometrics forms part two of the Market Risk Analysis four volume set. It introduces the econometric techniques that are commonly applied to finance with a critical and selective exposition, emphasising the areas of econometrics, such as GARCH, cointegration and copulas that are required for resolving problems in market risk analysis. The book covers material for a one-semester graduate course in applied financial econometrics in a very pedagogical fashion as each time a concept is introduced an empirical example is given, and whenever possible this is illustrated with an Excel spreadsheet.

All together, the Market Risk Analysis four volume set illustrates virtually every concept or formula with a practical, numerical example or a longer, empirical case study. Across all four volumes there are approximately 300 numerical and empirical examples, 400 graphs and figures and 30 case studies many of which are contained in interactive Excel spreadsheets available from the the accompanying CD-ROM . Empirical examples and case studies specific to this volume include:

  • Factor analysis with orthogonal regressions and using principal component factors;
  • Estimation of symmetric and asymmetric, normal and Student tGARCH and E-GARCH parameters;
  • Normal, Student t, Gumbel, Clayton, normal mixture copula densities, and simulations from these copulas with application to VaR and portfolio optimization;
  • Principal component analysis of yield curves with applications to portfolio immunization and asset/liability management;
  • Simulation of normal mixture and Markov switching GARCH returns;
  • Cointegration based index tracking and pairs trading, with error correction and impulse response modelling;
  • Markov switching regression models (Eviews code);
  • GARCH term structure forecasting with volatility targeting;
  • Non-linear quantile regressions with applications to hedging.

Carol Alexander is a Professor of Risk Management at the ICMA Centre, University of Reading, and Chair of the Academic Advisory Council of the Professional Risk Manager’s International Association (PRMIA). She is the author of Market Models: A Guide to Financial Data Analysis(John Wiley & Sons Ltd, 2001) and has been editor and contributor of a very large number of books in finance and mathematics, including the multi-volume Professional Risk Manager’s Handbook(McGraw-Hill, 2008 and PRMIA Publications). Carol has published nearly 100 academic journal articles, book chapters and books, the majority of which focus on financial risk management and mathematical finance. Professor Alexander is one of the world’s leading authorities on market risk analysis. For further details, see
List of Figures.

List of Tables.

List of Examples.


Preface to Volume II.

II.1 Factor Models.

II.1.1 Introduction.

II.1.2 Single Factor Models.

II.13 Multi-Factor Models.

II.1.4 Case Study: Estimation of Fundamental Factor Models.

II.1.5 Analysis of Barra Model.

II.1.6 Tracking Error and Active Risk.

II.1.7 Summary and Conclusions.

II.2 Principal Component Analysis.

II.2.1 Introduction.

II.2.2 Review of Principal Component Analysis.

II.2.3 Case Study: PCA of UK Government Yield Curves.

II.2.4 Term Structure Factor Models.

II.2.5 Equity PCA Factor Models.

II.2.6 Summary and Conclusions.

II.3 Classical Models of Volatility and Correlation.

II.3.1 Introduction.

II.3.2 Variance and Volatility.

II.3.3 Covariance and Correlation.

II.3.4 Equally Weighted Averages.

II.3.5 Precision of Equally Weighted Estimates.

II.3.6 Case Study: Volatility and Correlation of US Treasuries.

II.3.7 Equally Weighted Moving Averages.

II.3.8 Exponentially Weighted Moving Averages.

II.3.9 Summary and Conclusions.

II.4 Introduction to GARCH Models.

II.4.1 Introduction.

II.4.2 The Symmetric Normal GARCH Model.

II.4.3 Asymmetric GARCH Models.

II.4.4 Non-Normal GARCH Models.

II.4.5 GARCH Covariance Matrices.

II.4.6 Orthogonal GARCH.

II.4.7 Monte Carlo Simulation with GARCH Models.

II.4.8 Applications of GARCH Models.

II.4.9 Summary and Conclusions.

II.5 Time Series Models and Cointegration.

II.5.1 Introduction.

II.5.2 Stationary Processes.

II.5.3 Stochastic Trends.

II.5.4 Long Term Equilibrium.

II.5.5 Modelling Short Term Dynamics.

II.5.6 Summary and Conclusions.

II.6 Introduction to Copulas.

II.6.1 Introduction.

II.6.2 Concordance Metrics.

II.6.3 Copulas and Associated Theoretical Concepts.

II.6.4 Examples of Copulas.

II.6.5 Conditional Copula Distributions and Quantile Curves.

II.6.6 Calibrating Copulas.

II.6.7 Simulation with Copulas.

II.6.8 Market Risk Applications.

II.6.9 Summary and Conclusions.

II.7 Advanced Econometric Models.

II.7.1 Introduction.

II.7.2 Quantile Regression.

II.7.3 Case Studies on Quantile Regression.

II.7.4 Other Non-Linear Regression Models.

II.7.5 Markov Switching Models.

II.7.6 Modelling Ultra High Frequency Data.

II.7.7 Summary and Conclusions.

II.8 Forecasting and Model Evaluation.

II.8.1 Introduction.

II.8.2 Returns Models.

II.8.3 Volatility Models.

II.8.4 Forecasting the Tails of a Distribution.

II.8.5 Operational Evaluation.

II.8.6 Summary and Conclusions.