ASSET-LIABILITY MANAGEMENT WITH EPSTEIN-ZIN UTILITY UNDER STOCHASTIC INTEREST RATE AND UNKNOWN MARKET PRICE OF RISK
Bibliographic record
Abstract
This paper considers a stochastic control problem with Epstein-Zin recursive utility under partial information (unknown market price of risk), in which an investor is constrained to a liability at the end of the investment period. Introducing liabilities is the main novelty of the model and appears for the first time in the literature of recursive utilities. Such constraint leads to a coupled forward-backward stochastic differential equation (FBSDE), which wellposedness has not been addressed in the literature. We derive an explicit solution to the FBSDE, contrasting with the existence and uniqueness results with no explicit expression of the solutions typically found in most related literature. Moreover, under minimal additional assumptions, we obtain the Malliavin differentiability of the solution of the FBSDE. We solve the problem completely and find the expression of the controls and the value function. Finally, we determine the utility loss that investors suffer from ignoring the fact that they can learn about the market price of risk.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".