Bibliographic record
Abstract
Stochastic volatility is the main concept used in the fields of financial economics and mathematical finance to deal with time-varying volatility in financial markets. This book brings together some of the main papers that have influenced the field of the econometrics of stochastic volatility, and shows that the development of this subject has been highly multidisciplinary, with results drawn from financial economics, probability theory, and econometrics, blending to produce methods and models that have aided our understanding of the realistic pricing of options, efficient asset allocation, and accurate risk assessment. A lengthy introduction by the editor connects the papers with the literature. Contributors to this volume - Contributors: Torben Andersen, Northwestern University; Ole E. Barndorff-Nielsen, University of Aarhus; Tim Bollerslev, Duke University; Mikhail Chernov; Siddhartha Chib, Washington University in St. Louis; Peter Clark, University of California, Davis; Fabienne Comte, Universite Rene Descartes- Paris 5; Frank Diebold, University of Pennsylvania; Dean Foster, University of Pennsylvania; A Ronald Gallant, Duke University; Eric Ghysels, University of North Carolina - Chapel Hill; Andrew Harvey, University of Cambridge; Steven Heston, University of Maryland; David Hsieh, Duke University; John Hull, University of Toronto; Eric Jacquier, H.E.C. MONTREAL; Sangjoon Kim, RBS Securities Japan Limited; Paul Labys, Charles River Associates; Angelo Melino, University of Toronto; Daniel Nelson; Marc Nerlove, University of Maryland; Nicholas Polson, University of Chicago; Eric Renault, University of Montreal; Peter Rossi, University of Chicago; Esther Ruiz, Universidad Carlos III de Madrid; Barr Rosenberg, AXA Rosenberg Investment Management; Neil Shephard, Nuffield College, University of Oxford; Stephen Taylor, Lancaster University; George Tauchen, Duke University; Stuart Turnbull, University of Houston; Alan White, University of Toronto.
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".