Bibliographic record
Abstract
This thesis applies deep reinforcement learning (DRL) to hedge financial options. Chapter 2 reviews 17 studies in the field, noting a literature gap pertaining to the hedging of American style options. Thus, Chapter 3 uses DRL to hedge American put options, revealing that agents trained with market-calibrated stochastic volatility models outperform the BS Delta in hedging empirical asset paths. Chapter 3 uses a model-agnostic Chebyshev interpolation method for computing option prices, required for the DRL agent reward when the underlying follows a stochastic volatility model. Chapter 4 conducts a hyperparameter analysis, highlighting the suboptimal outcomes of high (low) learning rates used with high (low) training episodes, while demonstrating the superiority of a quadratic transaction cost penalty function over a linear version. Chapter 4 enhances the Chapter 3 methodology by retraining agents with weekly recalibrated stochastic volatility models to new market data, showing improved performance compared to agents trained only once.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".