Bibliographic record
Abstract
Introduction Nicholas Dunbar Risk. SECTION 1: GENERIC OPTION PRICING. 1 Assets with Jumps Alexander Lipton Citadel Investment Group. 2 Why Be Backward? Peter Carr Ali Hirsa New York University Caspian Capital Management. 3 Corridor Variance Swaps Peter Carr Keith A. Lewis New York University Independent Consultant. 4 What's a Basket Worth? Peter Laurence Tai-Ho Wang University of Rome National Chung Cheng University. 5 Unifying Volatility Models Claudio Albanese Alexey Kuznetsov University of London McMaster University. 6 Smile at the Uncertainty Damiano Brigo, Fabio Mercurio, Francesco Rapisarda Banca IMI. 7 Local Cross-entropy David Edelman University College Dublin. SECTION 2: PRICING PROBLEMS IN CREDIT, EQUITIES AND INTEREST RATES. 8 I Will Survive Jon Gregory, Jean-Paul Laurent BNP Paribas. 9 All Your Hedges in One Basket Leif Andersen, Jakob Sidenius Susanta Basu Banc of America Securities Och-Ziff Capital Management. 10 A Measure of Survival Philipp J. Schonbucher ETH Zurich. 11 Market Models for CDS Options and Callable Floaters Damiano Brigo Banca IMI. 12 Index Volatility Surface via Moment-Matching Techniques Peter Lee Limin Wang Abdelkerim Karim Lehman Brothers Credit Suisse First Boston Lehman Brothers. 13 Smile Dynamics Lorenzo Bergomi Societe Generale. 14 Volatile Volatilities Leif Andersen Jesper Andreasen Banc of America Securities Nordea Markets. 15 Swap Vega in BGM: Pitfalls and Alternatives Raoul Pietersz Antoon Pelsser ABN Amro ING Group Risk Management. 16 Black Smirks Fei Zhou Lehman Brothers. 17 Correlating Market Models Bruce Choy Tim Dun Erik Schlogl Commonwealth Bank of Australia ANZ Risk Management University of Technology. SECTION 3: MARKET ANALYSIS AND QUANTITATIVE TRADING. 18 Bidding Principles Robert Almgren Neil Chriss University of Toronto SAC Capital. 19 Practical Relative-value Volatility Trading Stephen Blyth Deutsche Bank. 20 Arbitrage Under Power Michael Boguslavsky Elena Boguslavskaya ABN Amro University of Amsterdam. 21 Component Proponents II Christophe Perignon Christophe Villa Simon Fraser University ENSAI. 22 Excess Yields in Bond Hedging Haim Reisman, Gady Zohar Technion. Age of Reason or Age of Procedure? Stephen Blyth Deutsche Bank
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".