Bibliographic record
Abstract
Two problems are considered in this thesis. The first is concerned with correlation model risk and the second with non-Gaussian factor modelling of asset returns. A fundamental problem in the application of mathematical finance results in a real world setting, is the dependence of mathematical models on parameters that are hard to observe in markets. The common term for this problem is model risk. The first part of this thesis studies the sensitivity of mathematical objects (prices) to correlation inputs. In high dimensions, computational complexities increase faster than exponentially. A typical way to deal with this problem is to introduce a principal component approach for dimension reduction. We consider the price of portfolios of options and approximations obtained by modifying the eigenvalues of the covariance matrix, then proceed to find analytical upper bounds on the magnitude of the difference between the price and the approximation, under different assumptions. In the second part of this thesis, the assumptions and estimation methods of four different factor models with time-varying parameters are discussed. These models are based on Sharpe’s single index model. The first model assumes that residuals follow a Gaussian white noise process, while the other three approaches combine the structure of a single factor model with time-varying parameters, with dynamic volatility (GARCH) assumptions on the model components. The four approaches then are used to estimate the time-varying alphas and betas of three different hedge fund strategies. Results are compared.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".