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.
PART I: VOLATILITY AND CORRELATION ANALYSIS.
Understanding Volatility and Correlation.
Implied Volatility and Correlation.
Moving Average Models.
Forecasting Volatility and Correlation.
PART II: MODELLING THE MARKET RISK OF PORTFOLIOS.
Principal Component Analysis.
Risk Measurement in Factor Models.
Modelling Non–Normal Returns.
PART III: STATISTICAL MODELS FOR FINANCIAL MARKETS.
Time Series Models.
Forecasting High–Frequency Data.
A1 Linear Regression.
A2 Statistical Inference.
A3 Residual Analysis.
A4 Data Problems.
A6 Maximum Likelihood Methods.
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