Financial Modeling of the Equity Market From CAPM to Cointegration,9780471699002

Financial Modeling of the Equity Market From CAPM to Cointegration

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Edition: 1st
ISBN13: 9780471699002
ISBN10: 0471699004
Format: Hardcover
Pub. Date: 2006-01-03
Publisher(s): Wiley
List Price: $110.00

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Summary

In Financial Modeling of the Equity Market, Frank Fabozzi, Sergio Focardi, and Petter Kolm provide you with the tools you need to succeed in managing equity portfolios.

Author Biography

FRANK J. FABOZZI, PHD, CFA, CPA, is the Frederick Frank Adjunct Professor of Finance at Yale University's School of Management and a Fellow of the International Center for Finance. Prior to joining the Yale faculty, Fabozzi was a visiting professor of finance in the Sloan School of Management at MIT. Fabozzi is the Editor of the Journal of Portfolio Management.

Sergio M. Focardi is a founding partner of the Paris-based consulting firm, The Intertek Group. He consults on, trains on, and implements quantitative financial models. He is also a member of the editorial board of the Journal of Portfolio Management and author of numerous articles and books on financial modeling.

Petter N. Kolm, PHD, is a doctoral student in finance at Yale University's School of Management and a financial consultant in New York City. Previously, he worked in the Quantitative Strategies group at Goldman Sachs Asset Management where he developed quantitative investment models and strategies.

Table of Contents

Preface xiii
Acknowledgments xvii
About the Authors xix
CHAPTER 1 Introduction 1(12)
Historical Perspective on the Financial Modeling of the Equity Market
1(7)
Central Themes of the Book
8(1)
Organization of the Book
9(4)
PART ONE Portfolio Allocation: Classical Theory and Modern Extensions 13(176)
CHAPTER 2 Mean-Variance Analysis and Modern Portfolio Theory
15(36)
The Benefits of Diversification
17(2)
Mean-Variance Analysis: Overview
19(3)
Classical Framework for Mean-Variance Optimization
22(12)
The Capital Market Line
34(6)
Selection of the Optimal Portfolio When there Is a Risk-Free Asset
40(2)
More on Utility Functions: A General Framework for Portfolio Choice
42(6)
Summary
48(3)
CHAPTER 3 Transaction and Trading Costs
51(36)
A Taxonomy of Transaction Costs
52(8)
Liquidity and Transaction Costs
60(3)
Market Impact Measurements and Empirical Findings
63(5)
Forecasting and Modeling Market Impact
68(6)
Incorporating Transaction Costs in Asset-Allocation Models
74(6)
Optimal Trading
80(2)
Integrated Portfolio Management: Beyond Expected Return and Portfolio Risk
82(3)
Summary
85(2)
CHAPTER 4 Applying the Portfolio Selection Framework in Practice
87(28)
Rebalancing in the Mean-Variance Optimization Framework
88(12)
Portfolio Constraints Commonly Used in Practice
100(13)
Summary
113(2)
CHAPTER 5 Incorporating Higher Moments and Extreme Risk Measures
115(34)
Dispersion and Downside Measures
116(15)
Portfolio Selection with Higher Moments through Expansions of Utility
131(8)
Polynomial Goal Programming for Portfolio Optimization with Higher Moments
139(2)
Some Remarks on the Estimation of Higher Moments
141(1)
The Approach of Malevergne and Sornette
142(5)
Summary
147(2)
CHAPTER 6 Mathematical and Numerical Optimization
149(40)
Mathematical Programming
150(8)
Necessary Conditions for Optimality for Continuous Optimization Problems
158(1)
How Do Optimization Algorithms Work?
159(17)
Optimization Software
176(4)
Practical Considerations when Using Optimization Software
180(7)
Summary
187(2)
PART TWO Managing Uncertainty in Practice 189(132)
CHAPTER 7 Equity Price Models
191(24)
Definitions
191(2)
Theoretical and Econometric Models
193(1)
Random Walk Models
194(13)
General Equilibrium Theories
207(1)
Capital Asset Pricing Model (CAPM)
208(4)
Arbitrage Pricing Theory (APT)
212(1)
Summary
213(2)
CHAPTER 8 Forecasting Expected Return and Risk
215(52)
Dividend Discount and Residual Income Valuation Models
217(5)
The Sample Mean and Covariance Estimator
222(9)
Random Matrices
231(3)
Arbitrage Pricing Theory and Factor Models
234(7)
Factor Models in Practice
241(4)
Factor Models in Practice: An Example
245(14)
Other Approaches to Volatility Estimation
259(5)
Application to Investment Strategies and Proprietary Trading
264(1)
Summary
265(2)
CHAPTER 9 Robust Frameworks for Estimation and Portfolio Allocation
267(54)
Robust Frameworks for Estimation and Portfolio Allocation
267(2)
Practical Problems Encountered in Mean-Variance Optimization
269(6)
Shrinkage Estimation
275(6)
Bayesian Approaches
281(23)
Incorporating Estimation Error and Uncertainty in the Portfolio Allocation Process
304(14)
Summary
318(3)
PART THREE Dynamic Models for Equity Prices 321(116)
CHAPTER 10 Feedback and Predictors in Stock Markets
323(24)
Random Walk Models and Their Shortcomings
323(10)
Time Diversification
333(6)
A Multiagent Economy: Effects of Agent Heterogeneity and Interactions
339(4)
Market Predictors
343(2)
Time Aggregation
345(1)
Summary
345(2)
CHAPTER 11 Individual Price Processes: Univariate Models
347(20)
Time Series Concepts
348(2)
Digression on White Noise and Martingale Difference Sequences
350(3)
The Lag Operator L
353(1)
Univariate Autoregressive Moving Average (ARMA) Models
353(1)
Stationarity Conditions
354(3)
Auto Correlations at Different Lags
357(1)
Solutions of an AR(p) Process
358(4)
MA(q) Moving Average Models
362(1)
ARMA(p,q) Models
363(1)
Integrated Processes
364(1)
Summary
365(2)
CHAPTER 12 Multivariate Models
367(40)
Dynamic Models: A Historical Perspective
368(2)
Vector Autoregressive Models
370(15)
Vector Autoregressive Moving Average Models (VARMA)
385(1)
Distributional Properties
386(1)
Cointegration
386(6)
Stochastic and Deterministic Cointegration
392(1)
Common Trends
393(2)
Error Correction Models
395(1)
Forecasting with VAR Models
396(1)
State-Space Models
397(2)
Autoregressive Distributed Lag Models
399(3)
Dynamic Factor Models
402(1)
The ARCH/GARCH Family of Models
402(2)
Nonlinear Markov-Switching Models
404(1)
Summary
405(2)
CHAPTER 13 Model Selection and its Pitfalls
407(30)
Model Selection and Estimation
407(3)
The (Machine) Learning Approach to Model Selection
410(5)
Sample Size and Model Complexity
415(4)
Dangerous Patterns of Behavior
419(5)
Data Snooping
424(2)
Survivorship Biases and Other Sample Defects
426(2)
Moving Training Windows
428(2)
Model Risk
430(1)
Model Selection in a Nutshell
431(2)
Summary
433(4)
PART FOUR Model Estimation and Model Risk Mitigation 437(140)
CHAPTER 14 Estimation of Regression Models
439(38)
Probability Theory and Statistics
439(3)
Populations of Prices and Returns
442(2)
Estimation at Work
444(1)
Estimators
445(1)
Sampling Distributions
446(4)
Critical Values and Confidence Intervals
450(1)
Maximum Likelihood, OLS, and Regressions
450(3)
The Fisher Information Matrix and the Cramer-Rao Bound
453(1)
Regressions
454(2)
Linear Regressions
456(8)
Sampling Distributions of Regressions
464(4)
Relaxing the Normality and Uncorrelated Noise Assumptions
468(1)
Pitfalls of Regressions
469(2)
The Method of Moments and its Generalizations
471(4)
Summary
475(2)
CHAPTER 15 Estimation of Linear Dynamic Models
477(52)
An Approach to Estimation
477(1)
Unit Root Testing
478(1)
Estimation of Linear Regression Models
479(3)
Estimation of Stable Vector Autoregressive (VAR) Models
482(17)
Estimating the Number of Lags
499(2)
Autocorrelation and Distributional Properties of Residuals
501(1)
Stationary Autoregressive Distributed Lag Models
502(1)
Applying Stable VAR Processes to Financial Econometrics
503(3)
Stationary Dynamic Factor Models
506(3)
Estimation of Nonstationary VAR Models
509(11)
Estimation with Canonical Correlations
520(1)
Estimation with Principal Component Analysis
521(2)
Estimation with the Eigenvalues of the Companion Matrix
523(1)
Estimation with Subspace Methods and Dynamic Factor Analysis
524(1)
Application of Cointegration Methods to the Analysis of Predictors
524(1)
Summary
525(4)
CHAPTER 16 Estimation of Hidden Variable Models
529(26)
Estimation of State-Space Models
530(13)
Estimation of Factor Analytic Models
543(3)
Estimation Methods for Markov-Switching Models
546(2)
Applications
548(4)
Summary
552(3)
CHAPTER 17 Model Risk and its Mitigation
555(22)
Sources of Model Risk
555(3)
The Information Theory Approach to Model Risk
558(5)
Bayesian Modeling
563(10)
Model Averaging and the Shrinkage Approach to Model Risk
573(1)
Random Coefficients Models
574(1)
Summary
575(2)
APPENDICES 577(52)
APPENDIX A Difference Equations
579(24)
Homogeneous Difference Equations
579(9)
Nonhomogeneous Difference Equations
588(6)
Systems of Linear Difference Equations
594(1)
Systems of Homogeneous Linear Difference Equations
595(8)
APPENDIX B Correlations, Regressions, and Copulas
603(16)
Probability Density Function, Marginal Density, and Conditional Density
603(1)
Expectations and Conditional Expectations
604(2)
Variances, Covariances, and Correlations
606(2)
Normal Distributions
608(2)
Regression
610(2)
Multivariate Extension
612(1)
Multiple and Multivariate Regressions
613(2)
Canonical Correlations
615(1)
Copula Functions
616(3)
APPENDIX C Data Description
619(10)
INDEX 629

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