Bibliographic record
Abstract
General Introduction PART I: MODEL BUILDING 1. A Subordinated Stochastic Process Model with Finite Variance for Speculative Prices 2. Financial Returns Modelled by the Product of Two Stochastic Processes: A Study of Daily Sugar Prices, 1961-79 3. The Behavior of Random Variables with Nonstationary Variance and the Distribution of Security Prices 4. The Pricing of Options on Assets with Stochastic Volatilities 5. The Dynamics of Exchange Rate Volatility: A Multivariate Latent Factor ARCH Model 6. Multivariate Stochastic Variance Models 7. Stochastic Autoregressive Volatility: A Framework for Volatility Modelling 8. Long Memory in Continuous-time Stochastic Volatility Models PART II: INFERENCE 9. Bayesian Analysis of Stochastic Volatility Models 10. Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models 11. Estimation of Stochastic Volatility Models with Diagnostics PART III: OPTION PRICING 12. Pricing Foreign Currency Options with Stochastic Volatility 13. A Closed-Form Solution for Options with Stochastic Volatility, with Applications to Bond and Currency Options 14. A Study Towards a Unified Approach to the Joint Estimation of Objective and Risk Neutral Measures for the Purpose of Options Valuation PART IV: REALISED VARIATION 15. The Distribution of Exchange Rate Volatility 16. Econometric Analysis of Realized Volatility and its use in Estimating Stochastic Volatility Models Index
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.040 | 0.021 |
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".