Market models : a guide to financial data analysis with CD-ROM A Guide to Financial Data Analysis

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.

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.