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 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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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; both teacher heads agree on what is shown here.
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".