Practical Financial Optimization A Library of

This is the companion book to Stavros A. Zenios' Practical Financial Optimization: Decision Making for Financial Engineers. It thoroughly describes the actual implementation of the majority of the models developed in Practical Financial Optimization in the General Algebraic Modelling System, GAMS. In addition, the volume contains executable versions of these models, and a CD containing the software needed to execute them.

ANDREA CONSIGLIO is professor of Mathematical Finance at the University of Palermo, Italy. He has held positions at the University of Calabria and at the University of Cyprus. He has participated in consultancy projects with the Banca della Svizzera Italiana, Switzerland and Prometeia, Italy. He has co-authored one book and numerous articles for various leading academic journals. In 2006 he was awarded the EURO Excellence in Practice Award, jointly with Stavros A. Zenios and Flavio Cocco. His research interests encompass many areas in the field of financial modeling and computational finance. He holds a PhD in applied mathematics to finance and economics.

SØREN NIELSEN (1959-2003) was an Associate Professor in the Department of Informatics and Mathematical Modeling at the Technical University of Denmark. He worked at the World Bank and the University of Texas at Austin. He held degrees in computer science and a PhD in decision sciences from the Wharton School of the University of Pennsylvania.

STAVROS A. ZENIOS is Professor of Finance and Management Science at the University of Cyprus, Director of the HERMES European Centre of Excellence on Computational Finance and Economics, and Senior Fellow at the Wharton Financial Institutions Centre of the University of Pennsylvania. He has co-authored more than 130 articles in some of the premier journals in the filed, serves on the editorial board of six journals, and received numerous awards for his research and publications. His previous books include Practical Financial Optimization: Decision Making for Financial Engineers (Blackwell Publishing, 2007); Performance of Financial Institutions: Efficiency, Innovation, Regulation (Cambridge University Press, 2000); Parallel Optimization: Theory, Algorithms, and Applications (Oxford University Press, 1997); and Financial Optimization (Cambridge University Press, 1996).

Preface	p. xi
Acknowledgments	p. xiii
Notation	p. xv
List of Models	p. xix
An Introduction to the GAMS Modeling System	p. 1
Preview	p. 1
Basics of Modeling	p. 1
The GAMS Language	p. 2
Lexical conventions	p. 3
Sets	p. 4
Expressions, functions, and operators	p. 6
Assignment statements	p. 11
Variable declarations	p. 12
Constraints: Equation declarations	p. 13
Model declarations	p. 14
The SOLVE statement and model types	p. 15
Control structures	p. 16
Conditional compilation	p. 20
Getting Started	p. 21
The Integrated Development Environment	p. 21
Command line interaction	p. 22
The model library	p. 22
Notes and References	p. 22
Data Management	p. 25
Preview	p. 25
Basics of Data Handling	p. 25
Data entry: scalars, parameters, and tables	p. 26
External data files: include	p. 28
Output: display and put	p. 29
Data Generation	p. 31
A Complete Example: Portfolio Dedication	p. 31
The source file	p. 32
The FINLIB files	p. 39
Mean-Variance Portfolio Optimization	p. 41
Preview	p. 41
Basics of Mean-Variance Models	p. 42
Data estimation for the mean-variance model	p. 46
Allowing short sales	p. 48
The FINLIB files	p. 49
Sharpe Ratio Model	p. 50
Risk-free borrowing	p. 51
The FINLIB files	p. 53
Diversification Limits and Transaction Costs	p. 53
Transaction costs	p. 54
Portfolio revision	p. 56
The FINLIB files	p. 57
International Portfolio Management	p. 57
Implementation with dynamic sets	p. 58
The FINLIB files	p. 61
Portfolio Models for Fixed Income	p. 63
Preview	p. 63
Basics of Fixed-Income Modeling	p. 64
Modeling time	p. 64
GAMS as a financial calculator: continuous time	p. 66
Bootstrapping the term structure of interest rates	p. 68
Considerations for realistic modeling	p. 73
The FINLIB files	p. 74
Dedication Models	p. 74
Horizon return model	p. 78
Tradeability considerations	p. 79
The FINLIB files	p. 82
Immunization Models	p. 83
The FINLIB files	p. 85
Factor Immunization Model	p. 85
Direct yield maximization	p. 87
The FINLIB files	p. 89
Factor Immunization for Corporate Bonds	p. 89
The model data sets	p. 89
The optimization models	p. 90
The FINLIB files	p. 94
Scenario Optimization	p. 95
Preview	p. 95
Data sets	p. 96
The FINLIB files	p. 97
Mean Absolute Deyiation Models	p. 97
Downside risk and tracking models	p. 99
Comparing mean-variance and mean absolute deviation	p. 101
The FINLIB files	p. 103
Regret Models	p. 104
The FINLIB files	p. 106
Conditional Value-at-Risk Models	p. 106
The FINLIB files	p. 108
Utility Maximization Models	p. 109
The FINLIB files	p. 111
Put/Call Efficient Frontier Models	p. 111
The FINLIB files	p. 117
Dynamic Portfolio Optimization with Stochastic Programming	p. 119
Preview	p. 119
Dynamic Optimization for Fixed-Income Securities	p. 119
Stochastic dedication	p. 120
Stochastic dedication with borrowing and lending	p. 122
The FINLIB files	p. 124
Formulating Two-Stage Stochastic Programs	p. 124
Deterministic and stochastic two-stage programs	p. 125
The FINLIB files	p. 128
Single Premium Deferred Annuities: A Multi-stage Stochastic Program	p. 128
Background and data	p. 128
The FINLIB files	p. 133
Index Funds	p. 137
Preview	p. 137
Models for Index Funds	p. 138
A structural model for index funds	p. 138
A co-movement model for index funds	p. 139
A selective hedging model for index funds	p. 140
The FINLIB files	p. 143
Case Studies in Financial Optimization	p. 145
Preview	p. 145
Application I: International Asset Allocation	p. 146
Operational considerations	p. 149
Results	p. 151
The FINLIB files	p. 156
Application II: Corporate Bond Portfolio Management	p. 156
The FINLIB files	p. 159
Application III: Insurance Policies with Guarantees	p. 159
The FINLIB files	p. 164
Application IV: Personal Financial Planning	p. 164
The FINLIB files	p. 168
Bibliography	p. 169
Index	p. 171
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Practical Financial Optimization A Library of GAMS Models

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