Modelling Risk Premiums in Equity and Foreign Exchange Markets
Bibliographic record
Abstract
The observed predictability of excess returns in equity and foreign exchange markets has largely been attributed to the presence of time-varying risk premiums in these markets. For example, excess equity returns were found to be explained by various financial and economic variables. Similarly, in the foreign exchange market, the forward rate was found not to be an unbiased predictor of the future spot rate, and excess foreign exchange returns were shown to be partially explained by other variables of the foreign exchange market, notably the forward premium. However, notwithstanding the extensive empirical evidence on the above, theoretical models of international asset pricing have not been entirely successful in producing equilibrium conditions that replicate the actual behaviour of the different asset moments in empirical tests for reasonable parameter values. In fact, these models had limited success despite either rich preference structures or general driving processes for the exogenous environment of the model. In this paper, we evaluate excess asset returns in equity and foreign exchange markets by combining generalized preferences to a heteroscedastic driving process in the same model. We do so by extending the international asset-pricing model of Bekaert, Hodrick, and Marshall (1997) in which the authors adopt disappointment-aversion-type preferences and a homoscedastic exogenous environment. We show that our very general framework, with plausible parameter values, is fairly successful in generating predictability and moment levels of excess returns that are consistent with the sample data.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".