International Asset Pricing Under Segmentation and PPP Deviations
Bibliographic record
Abstract
We analyze the impact of both Purchasing Power Parity (PPP) deviations and barriers to international investment on asset pricing and investor’s portfolio holdings. The freely traded securities are priced similarly to Adler and Dumas (1983) and command two premiums: a global market risk premium and an inflation risk premium. The securities that can be held by only a subset of the investors command two additional premiums; a conditional market risk premium in the vein of Errunza and Losq (1985) and a segflation risk premium from bearing inflation risk in the presence of barriers. Our model nests several existing international asset pricing models and thus provides a framework to distinguish empirically between competing models. We test the conditional version of our model for eight major emerging markets. We use global market and industry portfolios, US and UK traded closed-end country funds, American Depository Receipts and Global Depository Receipts to replicate the returns on unattainable securities. We find that the global market, the conditional market and the global exchange risks are significantly priced as in previous research. Our results also point to the importance of the segflation risk which is statistically and economically significant.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".