Lessons from the financial crisis : insights from the defining economic event of our lifetime
Bibliographic record
Abstract
Introduction Arthur M. Berd The Roots of the Crisis 1. The Credit Crunch of 2007: What Went Wrong? Why? What Lessons Can be Learned? John C. Hull [University of Toronto] 2. Underwriting versus Economy: A New Approach to Decomposing Mortgage Losses Ashish Das, Roger M. Stein [Moody's Research Labs] 3. Credit Expansion, Leverage and the Shadow Banking System Paul McCulley [PIMCO] 4. The Collapse of the Icelandic Banking System David Lando, Rene Kallestrup [Copenhagen Business School] 5. The Quant Crunch Experience and the Future of Quantitative Investing Robert Litterman [GSAM, retired] The Impact on the Markets 6. The Impact of the Crisis on the OTC Derivatives Markets Jeff Rosenberg [Bank of America Merrill Lynch] 7. The Re-Emergence of Distressed Exchanges in Corporate Restructurings Edward I. Altman, Brenda Karlin [NYU] Risk Management and Regulation 8. Modeling Systemic and Sovereign Risks Dale F. Gray, Andreas A. Jobst [IMF] 9. Measuring and Managing Risk in Innovative Financial Instruments Stuart M. Turnbull [University of Houston] 10. Forecasting Extreme Risk of Equity Portfolios with Fundamental Factors Vladislav Dubikovsky, Michael Y. Hayes, Lisa R. Goldberg, Ming Liu [MSCI Barra] Quantitative Modelling 11. Limits of Implied Credit Correlation Metrics Before and During the Crisis Damiano Brigo [King's College], Andrea Pallavicini [Banca Leonardo], Roberto Torre-setti [QCM] 12. Another View on the Pricing of MBS, CMOs, CDOs of ABS Jean-David Fermanian [CREST-ENSAE] 13. Pricing of Credit Derivatives with or without Counterparty and Collateral Adjustments Alexander Lipton, David Shelton [Bank of America Merrill Lynch] 14. A Practical Guide to Monte Carlo CVA Alexander Sokol [CompatibL] Market Efficiency and (In)Stability 15. The Endogenous Dynamics of Markets: Price Impact, Feedback Loops and Instabilities Jean-Philippe Bouchaud [CFM] 16. Market Panics: Correlation Dynamics, Dispersion, and Tails Lisa Borland [Evnine and Assoc.] 17. Financial Complexity and Systemic Stability in Trading Markets Matteo Marsili, Kartik Anand [ICTP] 18. The Martingale Theory of Bubbles: Implication for the Valuation of Derivatives and Detecting Bubbles Robert A. Jarrow, Philip Protter [Cornell University] Lessons for Investors 19. Managing through a Crisis: Practical Insights and Lessons Learned for Quantitatively Managed Equity Portfolios Peter J. Zangari [GSAM] 20. Active Risk Management: a Credit Investor's Perspective Vineer Bhansali [PIMCO] 21. Investment Strategy Returns: Volatility, Asymmetry, Fat Tails and the Nature of Alpha Arthur M. Berd [CFM]
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".