Abstract
This paper reviews some of the stochastic programming (SP) frameworks that are useful in applications to asset-liability management (ALM). Two such frameworks include recourse models and SP with decision rules. Recent advances also provide a representation for the Conditional Value-at-Risk risk measure that can be easily optimized in SP. Uncertainty in ALM stochastic programs is represented through discrete scenarios that are often generated through time-series methods. Sophisticated methods, such as those incorporating stable distributions, are needed to capture typical characteristics of financial data.
Original language | English |
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Pages (from-to) | 82-90 |
Number of pages | 9 |
Journal | Investment Management and Financial Innovations |
Volume | 2 |
Issue number | 1 |
State | Published - 2005 |
Keywords
- Risk
- Stable distributions
- Stochastic programming
- Time-series analysis
- Uncertainty