Stochastic programming methods in asset-liability management

Michael Grebeck; Svetlozar Rachev

Stochastic programming methods in asset-liability management

Michael Grebeck, Svetlozar Rachev

Mathematics and Statistics

Research output: Contribution to journal › Article › peer-review

1 Scopus citations

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
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

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