Title: Integrated risk control using stochastic programming ALM models for money management
Abstract: A multistage stochastic programming modeling based approach is developed for asset and liability management for fund managers. Managing a fund of investments in a large pool of possible instruments, such as stocks, requires sophisticated analytical capability both in terms of selecting the pool and also in maintaining the performance of the fund within acceptable levels. Given the uncertainty of the future performance of the underlying stocks, the portfolio must be managed or rebalanced temporally as the market and economic conditions change. Such portfolio rebalancing at various points in time allows the fund manager to manage the riskiness of the fund from both the fund managers and the individual client's viewpoints. In doing so, a fund manager must resort to more-advanced analytical risk-control techniques. It applies a multi-pronged risk metric system that is designed for the portfolio to achieve desired performance characteristics. The model incorporates important issues such as market impact costs in trading, fund drawdown, market neutrality, and catastrophic risk, within an integrative framework, modeled via stochastic programming. The model is applied to stock fund management involving a large number of securities and its performance is demonstrated with various strategies for portfolio rebalancing. The integrated dynamic multistage stochastic programming model easily outperforms the standard static mean-variance approach for portfolio management. Sharpe ratios, percent worst draw downs, recovery periods from drawdown, and portfolio rate of returns are used for performance comparisons.
Publication Year: 2008
Publication Date: 2008-01-01
Language: en
Type: book-chapter
Indexed In: ['crossref']
Access and Citation
Cited By Count: 7
AI Researcher Chatbot
Get quick answers to your questions about the article from our AI researcher chatbot