Dynamic Asset Allocation for Pension Funds: A Stochastic Control Approach Using the Heston Model
Bibliographic record
Abstract
This paper develops a dynamic asset allocation strategy for defined contribution pension funds using a stochastic control framework under the Heston stochastic volatility model. By solving the associated Hamilton–Jacobi–Bellman partial differential equation, we derive optimal equity allocations that adapt to changing market volatility and investor risk aversion using a constant relative risk aversion utility function (parameter γ). The strategy increases equity exposure during stable periods and reduces it during volatile regimes, capturing both myopic and intertemporal hedging demands. We test the model using historical U.S. data from 2006 to 2025 and benchmark its performance against a traditional static 60/40 stock–bond portfolio, as well as rule-based strategies such as volatility targeting and constant proportion portfolio insurance. Our results show that with moderate risk aversion, the dynamic strategy achieves long-term wealth comparable to the 60/40 benchmark while substantially reducing drawdown risk. As risk aversion increases, drawdown risk is further reduced and risk-adjusted returns remain competitive. Although higher aversion yields lower final wealth, certainty-equivalent returns are highest at moderate aversion levels. These results demonstrate that volatility responsive dynamic policies grounded in realistic stochastic volatility modeling can substantially enhance downside protection and risk-adjusted utility, especially for long-horizon, risk-averse pension participants.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".