Bibliographic record
Abstract
This paper develops a simple but general methodology to estimate the expected intertemporal marginal rate of substitution or “EMRS”, using only data on asset prices and returns. Our empirical strategy is general, and allows the EMRS to vary arbitrarily over time. A novel feature of our technique is that it relies upon exploiting idiosyncratic risk, since theory dictates that idiosyncratic shocks earn the EMRS. We apply our methodology to two different data sets: monthly data from 1994 through 2003, and daily data for 2003. Both data sets include assets from three different markets: the New York Stock Exchange, the NASDAQ, and the Toronto Stock Exchange. For both monthly and daily frequencies, we find plausible estimates of EMRS with considerable precision and time-series volatility. We then use these estimates to test for asset integration, both within and between stock markets. We find that all three markets seem to be internally integrated in the sense that different assets traded on a given market share the same EMRS. The technique is also powerful enough to reject integration between the three stock markets, and between stock and money markets.
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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 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.669 | 0.453 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".