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El. knyga: Practical Financial Optimization: A Library of GAMS Models

Edited by (Technical University of Denmark; University of Texas at Austin; Wharton School of the University of Pennsylvania), Edited by (University of Cyprus), Edited by (University of Palermo, Italy)
  • Formatas: EPUB+DRM
  • Serija: The Wiley Finance Series
  • Išleidimo metai: 05-Feb-2010
  • Leidėjas: Wiley-Blackwell
  • Kalba: eng
  • ISBN-13: 9781444317237
Kitos knygos pagal šią temą:
Practical Financial Optimization: A Library of GAMS ModelsDidesnis paveikslėlis
  • Formatas: EPUB+DRM
  • Serija: The Wiley Finance Series
  • Išleidimo metai: 05-Feb-2010
  • Leidėjas: Wiley-Blackwell
  • Kalba: eng
  • ISBN-13: 9781444317237
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In Practical Financial Optimization: A Library of GAMS Models, the authors provide a diverse set of models for portfolio optimization, based on the General Algebraic Modelling System. ‘GAMS’ consists of a language which allows a high-level, algebraic representation of mathematical models and a set of solvers – numerical algorithms – to solve them. The system was developed in response to the need for powerful and flexible front-end tools to manage large, real-life models.

The work begins with an overview of the structure of the GAMS language, and discusses issues relating to the management of data in GAMS models. The authors provide models for mean-variance portfolio optimization which address the question of trading off the portfolio expected return against its risk. Fixed income portfolio optimization models perform standard calculations and allow the user to bootstrap a yield curve from bond prices. Dedication models allow for standard portfolio dedication with borrowing and re-investment decisions, and are extended to deal with maximisation of horizon return and to incorporate various practical considerations on the portfolio tradeability. Immunization models provide for the factor immunization of portfolios of treasury and corporate bonds.

The scenario-based portfolio optimization problem is addressed with mean absolute deviation models, tracking models, regret models, conditional VaR models, expected utility maximization models and put/call efficient frontier models. The authors employ stochastic programming for dynamic portfolio optimization, developing stochastic dedication models as stochastic extensions of the fixed income models discussed in chapter 4. Two-stage and multi-stage stochastic programs extend the scenario models analysed in Chapter 5 to allow dynamic rebalancing of portfolios as time evolves and new information becomes known. Models for structuring index funds and hedging interest rate risk on international portfolios are also provided.

The final chapter provides a set of ‘case studies’: models for large-scale applications of portfolio optimization, which can be used as the basis for the development of business support systems to suit any special requirements, including models for the management of participating insurance policies and personal asset allocation.

The title will be a valuable guide for quantitative developers and analysts, portfolio and asset managers, investment strategists and advanced students of finance.

Preface xi
Acknowledgments xiii
Notation xv
List of Models
xix
1 An Introduction to the GAMS Modeling System
1(24)
1.1 Preview
1(1)
1.2 Basics of Modeling
1(1)
1.3 The GAMS Language
2(19)
1.3.1 Lexical conventions
3(1)
1.3.2 Sets
4(2)
1.3.3 Expressions, functions, and operators
6(5)
1.3.4 Assignment statements
11(1)
1.3.5 Variable declarations
12(1)
1.3.6 Constraints: Equation declarations
13(1)
1.3.7 Model declarations
14(1)
1.3.8 The SOLVE statement and model types
15(1)
1.3.9 Control structures
16(4)
1.3.10 Conditional compilation
20(1)
1.4 Getting Started
21(4)
1.4.1 The Integrated Development Environment
21(1)
1.4.2 Command line interaction
22(1)
1.4.3 The model library
22(1)
Notes and References
22(3)
2 Data Management
25(16)
2.1 Preview
25(1)
2.2 Basics of Data Handling
25(6)
2.2.1 Data entry: SCALARs, PARAMETERs, and TABLES
26(2)
2.2.2 External data files: INCLUDE
28(1)
2.2.3 Output: DISPLAY and PUT
29(2)
2.3 Data Generation
31(1)
2.4 A Complete Example: Portfolio Dedication
31(10)
2.4.1 The source file
32(7)
2.4.2 The FINLIB files
39(2)
3 Mean-Variance Portfolio Optimization
41(22)
3.1 Preview
41(1)
3.2 Basics of Mean-Variance Models
42(8)
3.2.1 Data estimation for the mean-variance model
46(2)
3.2.2 Allowing short sales
48(1)
3.2.3 The FINLIB files
49(1)
3.3 Sharpe Ratio Model
50(3)
3.3.1 Risk-free borrowing
51(2)
3.3.2 The FINLIB files
53(1)
3.4 Diversification Limits and Transaction Costs
53(4)
3.4.1 Transaction costs
54(2)
3.4.2 Portfolio revision
56(1)
3.4.3 The FINLIB files
57(1)
3.5 International Portfolio Management
57(6)
3.5.1 Implementation with dynamic sets
58(3)
3.5.2 The FINLIB files
61(2)
4 Portfolio Models for Fixed Income
63(32)
4.1 Preview
63(1)
4.2 Basics of Fixed-Income Modeling
64(10)
4.2.1 Modeling time
64(2)
4.2.2 GAMS as a financial calculator: continuous time
66(2)
4.2.3 Bootstrapping the term structure of interest rates
68(5)
4.2.4 Considerations for realistic modeling
73(1)
4.2.5 The FINLIB files
74(1)
4.3 Dedication Models
74(9)
4.3.1 Horizon return model
78(1)
4.3.2 Tradeability considerations
79(3)
4.3.3 The FINLIB files
82(1)
4.4 Immunization Models
83(2)
4.4.1 The FINLIB files
85(1)
4.5 Factor Immunization Model
85(4)
4.5.1 Direct yield maximization
87(2)
4.5.2 The FINLIB files
89(1)
4.6 Factor Immunization for Corporate Bonds
89(6)
4.6.1 The model data sets
89(1)
4.6.2 The optimization models
90(4)
4.6.3 The FINLIB files
94(1)
5 Scenario Optimization
95(24)
5.1 Preview
95(1)
5.2 Data sets
96(1)
5.2.1 The FINLIB files
97(1)
5.3 Mean Absolute Deviation Models
97(7)
5.3.1 Downside risk and tracking models
99(2)
5.3.2 Comparing mean-variance and mean absolute deviation
101(2)
5.3.3 The FINLIB files
103(1)
5.4 Regret Models
104(2)
5.4.1 The FINLIB files
106(1)
5.5 Conditional Value-at-Risk Models
106(3)
5.5.1 The FINLIB files
108(1)
5.6 Utility Maximization Models
109(2)
5.6.1 The FINLIB files
111(1)
5.7 Put/Call Efficient Frontier Models
111(8)
5.7.1 The FINLIB files
117(2)
6 Dynamic Portfolio Optimization with Stochastic Programming
119(18)
6.1 Preview
119(1)
6.2 Dynamic Optimization for Fixed-Income Securities
119(5)
6.2.1 Stochastic dedication
120(2)
6.2.2 Stochastic dedication with borrowing and lending
122(2)
6.2.3 The FINLIB files
124(1)
6.3 Formulating Two-Stage Stochastic Programs
124(4)
6.3.1 Deterministic and stochastic two-stage programs
125(3)
6.3.2 The FINLIB files
128(1)
6.4 Single Premium Deferred Annuities: A Multi-stage Stochastic Program
128(9)
6.4.1 Background and data
128(5)
6.4.2 The FINLIB files
133(4)
7 Index Funds
137(8)
7.1 Preview
137(1)
7.2 Models for Index Funds
138(7)
7.2.1 A structural model for index funds
138(1)
7.2.2 A co-movement model for index funds
139(1)
7.2.3 A selective hedging model for index funds
140(3)
7.2.4 The FINLIB files
143(2)
8 Case Studies in Financial Optimization
145(24)
8.1 Preview
145(1)
8.2 Application I: International Asset Allocation
146(10)
8.2.1 Operational considerations
149(2)
8.2.2 Results
151(5)
8.2.3 The FINLIB files
156(1)
8.3 Application II: Corporate Bond Portfolio Management
156(3)
8.3.1 The FINLIB files
159(1)
8.4 Application III: Insurance Policies with Guarantees
159(5)
8.4.1 The FINLIB files
164(1)
8.5 Application IV: Personal Financial Planning
164(5)
8.5.1 The FINLIB files
168(1)
Bibliography 169(2)
Index 171
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. SŲREN 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).
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