An Algorithm Exploiting Episodes of Inefficient Asset Pricing to Derive a Macro-Foundation Scaled Metric for Systemic Risk: A Time-Series Martingale Representation
Bibliographic record
Abstract
This paper employs an event study, the Global Financial Crisis. Episodes of inefficient pricing, the externality, are exploited as a measure of systemic risk. The theoretical asset pricing model, the martingale representation, is shown to be a valid algorithm to identify episodes of efficient and inefficient pricing in time series. Systemic risk metrics are derived from episodes of inefficient pricing, utilizing a shadow volatility metric. The algorithm is forward looking, deriving macro-foundation metrics from actual agent market behavior. The algorithm provides precise risk metrics for magnitude and diffusion using US and Canadian treasury markets. Given the US dollar’s role as the de-facto world reserve currency, scaled metrics derived from the US treasury market provide a globalized systemic benchmark. The risk metrics signal the crisis buildup and calibrate around the crisis epicenter date of September 2008. The risk metrics are heuristically consistent with the stylized facts of financial crises and support the extraordinary US policy response to the crisis. The algorithm output is validated by time-series analysis.JEL classification numbers: C22, C61, G01, G12, G14Keywords: Systemic Risk, Time Series, Optimization, Financial Crises, Asset Pricing.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".