Bibliographic record
Abstract
Developing the concepts of risk management discussed in the first volume in this set, Mastering Risk Volume 2: Applications examines the application of some of the most important recent research into financial products to the risk management of financial institutions. Building on the discussion of risk management concepts in the first volume, it provides a comprehensive overview of how to put market, credit and operational risk controls into practice. As with the first volume, the contributors are risk experts; leading academic specialists and practitioners in the day-to-day environment of risk management. They provide a balanced analysis of risk management applications including: - Monte Carlo methods for Value-at-Risk - The orthogonal GARCH model for generating large covariance matrices - The valuation of equity options using strike-adjusted spread - Models of portfolio credit risk, and of default correlation in bond portfolios - Techniques for measuring and managing operational risk - The management of model risk. Mastering Risk Volume 2: Applications gathers an impressive cast of 17 contributors, including Mark Davis (Imperial College), Emanuel Derman (Goldman Sachs), Paul Glasserman (University of Columbia Graduate School), Michael Gordy (Federal Reserve Board of Governors), John Hull and Alan White (University of Toronto), Dilip Madan (University of Maryland) and Riccardo Rebonato (Group Head of Market Risk, Royal Bank of Scotland Group). Mastering Risk Volume 2: Applications takes a detailed look at the theory of risk management and illustrates how to apply the concepts to your business, supported by recent examples and short case studies. It is an invaluable follow-on from the first volume and an equally comprehensive source in its own right. Mastering Risk Volume 2: Applications has been produced in association with the ISMA Centre, The Business School for Financial Markets at the University of Reading, UK
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.098 | 0.051 |
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".