Market Models. A Guide to Financial Data Analysis

  • ID: 2217640
  • Book
  • 514 Pages
  • John Wiley and Sons Ltd
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Market Models provides an authoritative and up–to–date treatment of the use of market data to develop models for financial analysis. Written by a leading figure in the field of financial data analysis, this book is the first of its kind to address the vital techniques required for model selection and development. Model developers are faced with many decisions, about the pricing, the data, the statistical methodology and the calibration and testing of the model prior to implementation. It is important to make the right choices and Carol Alexander′s clear exposition provides valuable insights at every stage.

In each of the 13 Chapters, Market Models presents real world illustrations to motivate theoretical developments. The accompanying CD contains spreadsheets with data and programs; this enables the reader to implement and adapt many of the examples. The pricing of options using normal mixture density functions to model returns; the use of Monte Carlo simulation to calculate the VaR of an options portfolio; modifying the covariance VaR to allow for fat–tailed P&L distributions; the calculation of implied, EWMA and ′historic′ volatilities; GARCH volatility term structure forecasting; principal components analysis; and many more are all included.

Market Models: A Guide to Financial Data Analysis is the ideal reference for all those involved in market risk measurement, quantitative trading and investment analysis.
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Preface.

Acknowledgments.

PART I: VOLATILITY AND CORRELATION ANALYSIS.

Understanding Volatility and Correlation.

Implied Volatility and Correlation.

Moving Average Models.

GARCH Models.

Forecasting Volatility and Correlation.

PART II: MODELLING THE MARKET RISK OF PORTFOLIOS.

Principal Component Analysis.

Covariance Matrices.

Risk Measurement in Factor Models.

Value–At–Risk.

Modelling Non–Normal Returns.

PART III: STATISTICAL MODELS FOR FINANCIAL MARKETS.

Time Series Models.

Cointegration.

Forecasting High–Frequency Data.

Technical Appendices.

A1 Linear Regression.

A2 Statistical Inference.

A3 Residual Analysis.

A4 Data Problems.

A5 Prediction.

A6 Maximum Likelihood Methods.

References.

Tables.

Index.
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CAROL ALEXANDER is Professor of Risk Management at the ISMA Centre, the Business School of Reading University. Prior to this post, she has held positions in both academia and financial institutions at: Gemente Universiteit in Amsterdam; UBS Phillips and Drew; The University of Sussex; Algorithmics Inc. and Nikko Global Holdings.

Professor Alexander has edited many books, most recently ′Risk Management and Analysis: Measuring and Modelling Financial Risk′ and ′New Markets and Products′ (John Wiley,1998) ′Visions of Risk (FT–Prentice Hall, 2000) and Mastering Risk Volume 2 (FT–Prentice Hall, 2001). For over a decade Professor Alexander has been consulting in risk management and investment analysis, developing solutions for private and commercial clients. She is also a principal of Pennoyer Capital Management, New York. She has published a large number of papers in international academic and professional journals and further details are available
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