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.