Book contents
- Frontmatter
- Contents
- Foreword
- 1 Introduction
- 2 Linear programming: theory and algorithms
- 3 LP models: asset/liability cash-flow matching
- 4 LP models: asset pricing and arbitrage
- 5 Nonlinear programming: theory and algorithms
- 6 NLP models: volatility estimation
- 7 Quadratic programming: theory and algorithms
- 8 QP models: portfolio optimization
- 9 Conic optimization tools
- 10 Conic optimization models in finance
- 11 Integer programming: theory and algorithms
- 12 Integer programming models: constructing an index fund
- 13 Dynamic programming methods
- 14 DP models: option pricing
- 15 DP models: structuring asset-backed securities
- 16 Stochastic programming: theory and algorithms
- 17 Stochastic programming models: Value-at-Risk and Conditional Value-at-Risk
- 18 Stochastic programming models: asset/liability management
- 19 Robust optimization: theory and tools
- 20 Robust optimization models in finance
- Appendix A Convexity
- Appendix B Cones
- Appendix C A probability primer
- Appendix D The revised simplex method
- References
- Index
18 - Stochastic programming models: asset/liability management
Published online by Cambridge University Press: 06 July 2010
- Frontmatter
- Contents
- Foreword
- 1 Introduction
- 2 Linear programming: theory and algorithms
- 3 LP models: asset/liability cash-flow matching
- 4 LP models: asset pricing and arbitrage
- 5 Nonlinear programming: theory and algorithms
- 6 NLP models: volatility estimation
- 7 Quadratic programming: theory and algorithms
- 8 QP models: portfolio optimization
- 9 Conic optimization tools
- 10 Conic optimization models in finance
- 11 Integer programming: theory and algorithms
- 12 Integer programming models: constructing an index fund
- 13 Dynamic programming methods
- 14 DP models: option pricing
- 15 DP models: structuring asset-backed securities
- 16 Stochastic programming: theory and algorithms
- 17 Stochastic programming models: Value-at-Risk and Conditional Value-at-Risk
- 18 Stochastic programming models: asset/liability management
- 19 Robust optimization: theory and tools
- 20 Robust optimization models in finance
- Appendix A Convexity
- Appendix B Cones
- Appendix C A probability primer
- Appendix D The revised simplex method
- References
- Index
Summary
Asset/liability management
The financial health of any company, and in particular those of financial institutions, is reflected in the balance sheets of the company. Proper management of the company requires attention to both sides of the balance sheet - assets and liabilities. Asset/liability management (ALM) offers sophisticated mathematical tools for an integrated management of assets and liabilities and is the focus of many studies in financial mathematics.
ALM recognizes that static, one-period investment planning models (such as mean-variance optimization) fail to incorporate the multi-period nature of the liabilities faced by the company. A multi-period model that emphasizes the need to meet liabilities in each period for a finite (or possibly infinite) horizon is often required. Since liabilities and asset returns usually have random components, their optimal management requires tools of “optimization under uncertainty” and, most notably, stochastic programming approaches.
We recall the ALM setting we introduced in Section 1.3.4: let Lt be the liability of the company in year t for t = 1, …, T. The Lt's are random variables. Given these liabilities, which assets (and in which quantities) should the company hold each year to maximize its expected wealth in year T? The assets may be domestic stocks, foreign stocks, real estate, bonds, etc. Let Rit denote the return on asset i in year t. The Rit 's are random variables.
- Type
- Chapter
- Information
- Optimization Methods in Finance , pp. 279 - 291Publisher: Cambridge University PressPrint publication year: 2006