9

405-442

38

 

John Wiley & Sons, Inc. (Visit publisher website)

2005

English

 

 

 

Advanced

 

 

Quantitative analysis, principal component analysis, factor models, macroeconometric factor models, single-factor models, multifactor models, fundamental factor models, BARRA factor model, industry factor model, factor mimicking portfolio, Fama–French approach, principal component analysis (PCA), empirical PCA, statistical factor analysis, estimation, principal component method, maximum likelihood method, factor rotation, asymptotic PCA

 

Material presented in this chapter is useful for financial researchers seeking an introduction to current methods used in modeling the expected return and risk tradeoff. The market model of Sharpe serves as the introduction to factor models with extensions to the macroeconomic factor model of Chen, Roll and Ross and the fundamental factor model of Fama and French. Principal component analysis is presented with a complete application to a set of log equity returns.

 

9.1 A Factor Model
9.2 Macroeconometric Factor Models
9.2.1 A Single-Factor Model
9.2.2 Multifactor Models
9.3 Fundamental Factor Models
9.3.1 BARRA Factor Model
9.3.2 Fama–French Approach
9.4 Principal Component Analysis
9.4.1 Theory of PCA
9.4.2 Empirical PCA
9.5 Statistical Factor Analysis
9.5.1 Estimation
9.5.2 Factor Rotation
9.5.3 Applications
9.6 Asymptotic Principal Component Analysis
9.6.1 Selecting the Number of Factors
9.6.2 An Example
Exercises
References

 

If you are interested in purchasing the book, please click here.

 

The Second Edition of this critically acclaimed text provides a comprehensive and systematic introduction to financial econometric models and their applications in modeling and predicting financial time series data. This edition continues to emphasize empirical financial data and focuses on real-world examples. Following this approach, readers will master key aspects of financial time series, including volatility modeling, neural network applications, market microstructure and high-frequency financial data, continuous-time models and Ito`s Lemma, Value at Risk, multiple returns analysis, financial factor models, and econometric modeling via computation-intensive methods.

The author begins with the basic characteristics of financial time series data, setting the foundation for the three main topics:

* Analysis and application of univariate financial time series
* Return series of multiple assets
* Bayesian inference in finance methods

This new edition is a thoroughly revised and updated text, including the addition of S-Plus® commands and illustrations. Among the new material, readers will find:

* Consistent covariance estimation under heteroscedasticity and serial correlation
* Alternative approaches to volatility modeling
* Financial factor models
* State-space models
* Kalman filtering
* Estimation of stochastic diffusion models

This is an ideal textbook for MBA students as well as a reference for researchers and professionals in business and finance.