Practical Financial Optimization: A Library of GAMS Models (The Wiley Finance Series) [Kindle Edition]

This title provides a stable of GAMS models which can be adapted for use for particular optimization purposes. It sets out GAMS language and the basics of data management in GAMS Models. It introduces methods of building large-scale mean-variance models for managing international portfolios of bond and stock indices, extending these to incorporate various practical considerations on the portfolio trade ability.
The authors develop these models for scenario-based portfolio optimization and for dynamic portfolio optimization using stochastic programming. They also provide models for structuring index funds and for creating an index fund of fixed income securities.  There is an indexation model to provide hedging for the exchange rate risk of an international portfolio.
The title includes international asset allocation models to track the international bond index, while limiting foreign exchange exposure. Finally the authors develop models for the management of participating insurance policies with minimum guaranteed rate of return, and a scenario optimization model for asset and liability management of individual investors.

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.