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
15 - DP models: structuring asset-backed securities
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
The structuring of collateralized mortgage obligations will give us an opportunity to apply the dynamic programming approach studied in Chapter 13.
Mortgages represent the largest single sector of the US debt market, surpassing even the federal government. In 2000, there were over $5 trillion in outstanding mortgages. Because of the enormous volume of mortgages and the importance of housing in the US economy, numerous mechanisms have been developed to facilitate the provision of credit to this sector. The predominant method by which this has been accomplished since 1970 is securitization, the bundling of individual mortgage loans into capital market instruments. In 2000, $2.3 trillion of mortgage-backed securities were outstanding, an amount comparable to the $2.1 trillion corporate bond market and $3.4 trillion market in federal government securities. A mortgage-backed security (MBS) is a bond backed by a pool of mortgage loans. Principal and interest payments received from the underlying loans are passed through to the bondholders. These securities contain at least one type of embedded option due to the right of the home buyer to prepay the mortgage loan before maturity. Mortgage payers may prepay for a variety of reasons. By far the most important factor is the level of interest rates. As interest rates fall, those who have fixed rate mortgages tend to repay their mortgages faster.
- Type
- Chapter
- Information
- Optimization Methods in Finance , pp. 248 - 254Publisher: Cambridge University PressPrint publication year: 2006