Market Models_图书大全
Market Models
副标题: A Guide to Financial Data Analysis
ISBN: 9780471899754
出版社: John Wiley & Sons
出版年: 2001-11-15
页数: 494
定价: 976.00元
装帧: Hardcover
内容简介
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 you 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.
Carol Alexander brings many new insights to the pricing and hedging of options with her understanding of volatility and correlation, and the uncertainty which surrounds these key determinants of option portfolio risk. Modelling the market risk of portfolios is covered where the main focus is on a linear algebraic approach; the covariance matrix and principal component analysis are developed as key tools for the analysis of financial systems. The traditional time series econometric approach is also explained with coverage ranging from the application cointegration to long-short equity hedge funds, to high-frequency data prediction using neural networks and nearest neighbour algorithms.
Throughout this text the emphasis is on understanding concepts and implementing solutions. It has been designed to be accessible to a very wide audience: the coverage is comprehensive and complete and the technical appendix makes the book largely self-contained.
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.
作者简介
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
目录
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.
