Firm foreign activity and the geography of exchange rate risk [update of 2022-02]
Bibliographic record
Abstract
Globally-focused firms are the key drivers of foreign exchange rate (FX) risk. These firms have higher FX exposure to the risk from the currency of a closer country, in line with the gravity effect, and during the home currency depreciation. Furthermore, those in countries more dependent on the export sector and in the periphery of the global trade network are more exposed to FX risk. Exposure across the distinct FX factors is relatively larger with respect to the risk from currencies of the most distant countries. The extent of firms’ foreign activity most strongly explains their risk exposure, controlling for other firm-level characteristics. Overall, our results highlight the importance of the trade channel over the investment channel to understand the economic origins of countries’ FX risk pricing. Valuation Insight: Valuation of firms with substantial foreign trade depends importantly on exchange rate movements and exchange risk. Such firms are found to be more exposed to exchange risk when they are in countries with a large export sector and in countries on the periphery of the global trade network. Typically, such firms would have higher cost of equity capital which would affect value negatively. However, the paper uncovers that the exchange risk premia may be negative so that higher exchange risk exposure in fact lowers the cost of equity capital and enhances value.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.038 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.009 |
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".