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Standard asset pricing models reconcile high equity premia with smooth risk-free rates by inducing an inverse functional relationship between the mean and the variance of the stochastic discount factor. This highly successful resolution to closed-economy asset pricing puzzles is fundamentally problematic when applied to open economies: It requires that differences in currency returns arise almost exclusively from predictable appreciations, not from interest rate differentials. In the data, by contrast, exchange rates are largely unpredictable, and currency returns arise from persistent interest rate differentials. We show currency risk premia arising in canonical long-run risk and habit preferences cannot match this fact. We argue this tension between canonical asset pricing and international macroeconomic models is a key reason researchers have struggled to reconcile the observed behavior of exchange rates, interest rates, and capital flows across countries. The lack of such a unifying model is a major impediment to understanding the effect of risk premia on international markets.
quantitative asset pricing models, built to address the equity premium and risk-free rate puzzles, systematically fail when applied to open economies: they cannot generate the large and persistent interest rate differentials we observe between risky and safe currencies.
analytical restrictions and published-model calculations support the canonical-model contrast, conditional on the maintained preference and pricing structure
In canonical asset pricing models, heterogeneity in risk therefore cannot meaningfully move interest rates, while the non-risk forces that move interest rates in canonical macroeconomic models cannot generate currency returns.
the derived restrictions support the conclusion within the canonical mechanisms analyzed, rather than across all possible international models
The puzzle is robust to stochastic volatility, disaster risk, and other departures from log-normality
non-normal robustness rests on two analytical special cases and a disaster-model example rather than unrestricted higher-moment heterogeneity
Derived from the full evaluation — not a separate score.
Strengths
The analysis connects the mean–variance offset used to stabilize risk-free rates with the predicted composition of currency premia. This provides a shared diagnostic for otherwise distinct preference-based models.
↳ Propositions 1–3; equations (12), (16), and (22)
Replications and calibration-based calculations show how the diagnostic operates in long-run-risk, habit, and disaster settings. Table notes distinguish simulated quantities from theoretical slopes.
↳ Tables 2–4 and A1–A3
The discussion considers incomplete markets, bond-pricing wedges, and combinations of risk and non-risk heterogeneity. It explains what these approaches would need to deliver rather than asserting that no solution exists.
↳ Sections 4–7; footnote 36
Limitations
Proposition 3 assumes the common offset relation that generates its interest-rate restriction. Examples of models without that offset do not establish a general theorem that every such model must have counterfactual risk-free rates.
↳ Proposition 3; Section 5
The full-text abstract describes the generalized proof as requiring only appropriately risk-compensated bond pricing. Proposition 3 additionally requires the common offset relation, so the abstract overstates the proof's unconditional reach.
↳ Full-text Abstract; Proposition 3 condition (iii)
The baseline static-trade FX-share interval is −1.16 to 0.23, and updated static-trade intervals can be unbounded. These estimates, together with the limited real–nominal reconciliation, warrant caution about exact quantitative thresholds without overturning the central model contrast.
↳ Table 1; Property 1; Table A4; Appendix A.4
Propositions 1–2 and the published-model calculations establish a common reason canonical preferences produce currency premia with the wrong composition. Proposition 3 extends the diagnostic to incomplete markets, but its common offset relation is an assumption, not a universal consequence of successful asset pricing. The full-text abstract understates that dependence, while Section 4's non-normal analysis establishes selected extensions rather than unrestricted robustness. Empirical uncertainty and the limited reconciliation of real models with nominal evidence temper the quantitative interpretation, leaving a useful research diagnostic rather than an operational solution.
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Strong3.9
Confidence mediumThe score reflects a unifying diagnostic connecting closed-economy risk-free-rate stabilization to the composition of currency premia across established models. The general result is conditional on a shared offset relation and does not establish that every alternative mechanism must fail.
Explicit derivations, stated assumptions, and published-model replications support the analytical result. Restricted non-normal extensions, uncertain empirical thresholds, and the limited real–nominal comparison constrain the breadth of its quantitative interpretation.
The SDF-space presentation makes the sequence from mechanism to currency-return decomposition inspectable. The full-text abstract's description of the generalized proof omits the load-bearing offset condition, reducing clarity about its scope.
The paper engages both canonical models and mechanisms that could escape the diagnostic, including wedges and combined risk–growth heterogeneity. Limitations are dispersed rather than consistently connected to the broadest claims.
Caveats4 of 4 checks
The reported numbers and statistics are consistent with the stated results.
Two descriptive reporting notes concern an appendix FX-share and a calibration attribution. Neither changes the direction of the modeling contrast.
88 references in manuscript 76 of 80 checkable references found in an index 8 are books, websites or datasets — counted, but not index-checkable 1 mistyped DOI matched to the cited work by its details
No retraction or expression of concern found in Crossref (includes Retraction Watch).
Sources: Crossref (incl. Retraction Watch) ✓
Where this paper’s evidence sits on the path from initial observation to real-world use.
The paper supplies a diagnostic for candidate models using their offset slope and feasible variance differences. It does not provide a replacement model, deployable tool, or policy decision rule.
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