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2638. Portfolio Optimization and Parameter Uncertainty
Invited abstract in session TC-63: Advanced Options Strategies Using O.R. and Machine Learning, stream OR in Banking, Finance and Insurance: New Tools for Risk Management.
Tuesday, 12:30-14:00Room: S14 (building: 101)
Authors (first author is the speaker)
| 1. | Anton Vorobets
|
| Fortitudo Technologies |
Abstract
Portfolio optimization has a mixed reputation among investment managers, with some being so skeptical that they believe it is almost useless due to the inherent parameter uncertainty. It is undeniable that portfolio optimization problems are sensitive to parameter estimates, especially the expected returns that are arguably also the hardest parameters to estimate. However, most practitioners still attempt to build mean-risk optimal portfolios, albeit in implicit ways. Resampled optimization is a popular mathematical heuristic to tackle the parameter uncertainty issue. It computes optimal portfolios using sampled parameter estimates and calculates a simple average of the portfolio exposures across samples. The unsatisfactory aspect of the resampled approach is that there is no mathematical justification for using the average of portfolio exposures, it just works well in practice. This article provides perspectives for understanding the resampling approach by analyzing the portfolio exposure estimation process from a bias-variance trade-off. We show that the traditional resampled optimization corresponds to a naive version of stacked generalization. Finally, we introduce a stacked generalization approach that can be used to handle both parameter uncertainty and combine optimization methods in full generality. We coin the new method Exposure Stacking.
Keywords
- Risk Analysis and Management
- Convex Optimization
- Financial Modelling
Status: accepted
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