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REVIEW 5 major objections 6 minor 30 references

What events matter for exchange rate volatility ?

T0 review · 5 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read A spike-and-slab stochastic volatility model applied to five-minute Australian dollar returns finds that only nine of 117 U.S.

desk verdict Data-driven event selection is a real step forward, but the paper overclaims the identity of the nine events and under-reports MCMC validation. read the letter →

arxiv 2411.16244 v1 pith:B3B7O6F6 submitted 2024-11-25 q-fin.ST econ.EM

Igor Martins , Hedibert Freitas Lopes This is my paper · ORCID
classification q-fin.STecon.EM MSC 62F1562M1091B8462P20
keywords stochasticvolatilityexchangeratemacroeconomicannouncementsspike-and-slabpriorsintradayseasonalityrealizedforecastingportfolioallocationcarrytrade
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tries to show that the macroeconomic events that truly drive exchange-rate volatility can be discovered from data instead of being chosen from experience. It fits a Bayesian stochastic volatility model to five-minute Australian dollar returns, letting every one of 117 U.S. and Australian announcements enter through spike-and-slab priors that assign each event a posterior probability of inclusion and an effect size. The model reports that only nine events, all plausibly related to Taylor-rule fundamentals, pass the 95% inclusion threshold, and that they raise volatility for up to thirty minutes after release. It also recovers a W-shaped intraday seasonal pattern linked to market openings and trading volume, and shows that including events and seasonality improves realized-volatility forecasts and portfolio allocation relative to standard SV and GARCH benchmarks. If the paper is right, researchers and traders no longer need hand-picked event lists, because the release calendar itself can tell which events matter.

What carries the argument

The key machinery is a spike-and-slab prior on announcement coefficients inside a multiplicative stochastic volatility model. Log-variance is decomposed into level, persistent stochastic volatility, intraday seasonality, and announcement effects: $h_t = \mu_h + x_t + s_t + e_t$, with $e_t = I_t'\alpha$ and $s_t = H_t'\beta$. Each announcement coefficient comes from either a Dirac spike at zero or a Gaussian slab, so the model learns which events are included and how large their effects are. Estimation uses MCMC, including the seven-component Gaussian mixture approximation of Kim et al. for the latent volatility states and Geweke's method for sampling inclusion indicators despite the Dirac mass.

What would settle it

Re-estimate the model replacing each release dummy with the standardized surprise, defined as the announced value minus the consensus forecast divided by its standard deviation, and compare the inclusion probabilities and effect sizes; if surprise size rather than mere release is what drives volatility, the binary model's event rankings should shift and its out-of-sample forecast advantage should shrink.

Watch

Extended reading notes

Core claim

The central discovery is that data-driven event selection, rather than prior experience, identifies a small set of announcements that reliably move Australian dollar volatility: FOMC rate decisions, U.S. nonfarm payrolls, U.S. CPI, FOMC meeting minutes, U.S. retail sales, the RBA cash rate target, Australian employment change, Australian GDP, and Australian retail sales. These nine events all connect to the interest-rate rule that central banks follow, linking them to exchange-rate determination through interest differentials. The paper also finds that the estimated intraday seasonal component has a W shape, with peaks at the openings of Asian, European, and U.S. markets, and that this component tracks average traded volume closely, with a simple regression yielding an R-squared of 0.88. Finally, the model out-forecasts standard SV and GARCH specifications in out-of-sample realized-volatility horse races, with Diebold-Mariano p-values near zero and b1 coefficients close to one, and it delivers the smallest portfolio volatility and highest Sharpe ratio among the compared specifications.

Load-bearing premise

A scheduled announcement moves volatility just by being released at a set time, with the same multiplicative effect every time it occurs, regardless of whether the number announced matches what markets expected.

Editorial extensions

If this is right

  • Macroeconomic event lists for volatility modeling can be built directly from release calendars, avoiding the cherry-picking of events that plagues hand-selected lists.
  • The selected events all line up with Taylor-rule fundamentals, giving a coherent economic interpretation: news about interest-rate-setting variables moves exchange rates through expected interest differentials.
  • Intraday volatility is not simply U-shaped; the estimated W-shaped seasonality reflects the global sequence of market openings and is strongly associated with trading volume, so volume and volatility are linked at high frequency.
  • Ignoring announcement effects and seasonality costs forecast accuracy: the full model dominates SV, GARCH, HAR, and related benchmarks in out-of-sample realized-volatility prediction, with competitor models adding almost no information once the proposal is included.
  • The model's volatility forecasts translate into economic gains, producing the lowest variance and highest Sharpe ratio in an Australian dollar/Swiss franc global minimum variance portfolio.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Because the model represents each announcement as a binary release indicator with a constant multiplicative effect, it implicitly assumes that a scheduled release with a trivial surprise moves volatility as much as one with a large surprise; an extension replacing dummies with standardized surprise magnitudes could change the event ranking and the estimated effect sizes.
  • The same sparse event-selection approach could be applied to other liquid currencies and asset classes with long event calendars, such as bond yields or equity index volatility, where the combination of release dummies, intraday seasonality, and persistent stochastic volatility could be reused directly.
  • The W-shaped seasonality and its strong link to trading volume suggest a testable labor-supply story: if market openings drive volatility because traders concentrate work at the start of the day, the seasonal peaks should shift with daylight-saving time changes or holiday schedules that alter opening hours relative to Greenwich Mean Time.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

5 major / 6 minor

Summary. This paper proposes a Bayesian stochastic volatility model for 5-minute Australian Dollar returns that jointly captures volatility persistence, intraday seasonality, and the effects of a large set of scheduled macroeconomic announcements from Australia and the US. Event effects are modeled with spike-and-slab priors, which lets the data select a sparse set of relevant announcements. The authors report that only nine events have posterior inclusion probability above 95%, that the seasonal component follows a W-shaped pattern correlated with trading volume, and that the model outperforms standard SV and GARCH alternatives in out-of-sample realized volatility forecasting and in a global minimum variance portfolio allocation with the Swiss Franc.

Significance. If the results are robust, the paper offers a useful data-driven alternative to hand-picked event lists in intraday FX volatility modeling, and it provides concrete out-of-sample forecast gains and portfolio improvements over standard benchmarks. The authors ship a transparent decomposition of log-variance into persistence, seasonality, and announcement components, and they connect the seasonal component to trading volume with a high R-squared. The out-of-sample design and the horse-race comparisons are strengths.

major comments (5)
  1. [Section 3.1 and Appendix D] The headline selection result ('only nine events are included more than 95%') is based on pointwise posterior means of the inclusion indicators π_i, but the paper reports no credible intervals for these inclusion probabilities, no MCMC convergence diagnostics, and no sensitivity analysis to the Beta prior on γ or the slab variance σ_α^2. With 702 event-related dummies, the posterior of a single inclusion probability can be highly uncertain; a posterior mean of 0.95 does not by itself establish that the event is selected with high probability. This is load-bearing for the central claim, so the authors should add interval estimates and prior sensitivity checks.
  2. [Section 2.2, Equation (5)] The announcement effect is modeled with binary time-of-release indicators, so a release with a negligible surprise is treated identically to one with a large surprise. The FX announcement literature typically models volatility responses to standardized surprises relative to expectations. Under surprise-driven volatility, the estimated α_i and inclusion probabilities pool over heterogeneous surprise realizations, and the selected list may reflect which events happened to have large surprises in 2017–2023 (e.g., the COVID-era inflation surge) rather than intrinsic importance. The paper neither tests this assumption nor acknowledges it as a limitation. The authors should at least discuss this issue and, ideally, include a robustness check using surprise measures to show the selection is not an artifact of the sample window.
  3. [Section 4.1 and Appendix C] The forecasting exercise does not describe how one-step-ahead volatility forecasts are generated from the SV model. The statement that forecasts are the 'average volatility forecast by our proposal when parameters are fixed at their posterior mean' is insufficient, because the latent SV state x_t must be filtered or integrated out in a nonlinear state space model. Without a precise algorithm (e.g., particle filter or a Kalman filter on the linearized mixture), the out-of-sample comparison is not reproducible and the Diebold-Mariano tests cannot be verified. Please provide the exact forecasting procedure.
  4. [Section 2.2 and Appendix C] The MCMC scheme is described algorithmically but lacks essential implementation details: number of iterations, burn-in, thinning, starting values, and convergence diagnostics are not reported. Since the posterior inclusion probabilities are MCMC estimates, evidence of convergence is needed to trust the 95% threshold claims. This is a reproducibility issue that should be fixed.
  5. [Section 4.2, Table 2] The portfolio application reports annualized volatilities and Sharpe ratios without any measure of uncertainty. The difference between the proposal (Sharpe 0.81) and SSV (0.69) may be within sampling variation, yet the paper concludes that the proposed model yields the highest Sharpe ratio. Add confidence intervals or bootstrap standard errors for the performance metrics to support this claim.
minor comments (6)
  1. [Figure 3 and text] The text refers to 'Sidney' instead of 'Sydney', and the dashed-line colors in the caption (pink, red, light and dark blue, green, orange) do not match the colors described in the main text (red, light and dark blue, green, orange). Please harmonize the figure caption and text.
  2. [Appendix B] The sentence 'Figures 7 and show' is incomplete; it should refer to Figures 7 and 8.
  3. [Equations (4) and (8)] The symbol s_t is used both for the seasonal component in Equation (4) and for the nominal exchange rate in Equation (8). This notation conflict may confuse readers; please use a different symbol for one of them.
  4. [Introduction] The paper does not cite the classic announcement-surprise literature (e.g., Andersen, Bollerslev, Diebold, Vega 2003; Balduzzi, Elton, Green 2001) or discuss why it departs from that framework by using binary indicators. Adding this context would help position the contribution.
  5. [Table 1] The table layout and the regression equation contain spacing artifacts (e.g., 'P roposal dV olt|t−1' and 'Competitor dV olt|t−1'). These should be typeset cleanly.
  6. [Section 4.1] The horse-race regression in Equation (9) restricts b1 to [0,1]; the paper does not explain why this restriction is imposed or how the t-statistics are computed. A brief note would improve clarity.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: event selection, seasonality, and forecasts are estimated from returns and validated out-of-sample; the Taylor-rule narrative is post hoc interpretation, not a model input.

full rationale

The paper's central claim—that only nine macro announcements have over 95% posterior inclusion probability—is an empirical output of the spike-and-slab stochastic volatility model in Equations (2)–(6), where the event dummies I_t are exogenous release-time indicators and the inclusion probabilities π_i are posterior quantities. The list of nine events is not imposed or defined in terms of the result; it is estimated from 5-minute AUD returns. The forecasting application uses an out-of-sample period after June 29, 2022, so the reported forecast improvements are not in-sample fits renamed as predictions. The seasonality–volume relationship is descriptive: the seasonal component is estimated from returns alone and then regressed on separately observed traded volume, yielding an R² of 0.88; this is a comparison of model output to external data, not a fitted parameter called a prediction. The Taylor-rule and labor-leisure explanations are post-hoc narratives attached to the selected events and seasonal peaks, not parts of the estimation, so they do not create circularity. The only author self-citation (Abanto-Valle et al. 2010, coauthored by Lopes) is background literature on volume–volatility links and is not load-bearing for any derivation in the paper. The potential concern that binary announcement indicators ignore surprise magnitude is a modeling/identification issue, not a circularity: the paper defines the object of interest as release-driven volatility, and the model is not constructed to force the nine-event selection. Overall, the derivation chain is self-contained and the central empirical claims are not equivalent to their inputs by construction.

Assumptions & free parameters 4 free parameters · 6 assumptions · 0 invented entities

The central claims do not introduce new physical or economic entities; they rest on standard Bayesian machinery and on modeling choices, binary event dummies, additive log-variance, and fixed seasonal dummies, that are not derived from data. The most consequential choices are the inclusion prior and the non-surprise-scaled event dummies.

free parameters (4)
  • Beta prior on event inclusion probability gamma = Hyperparameters not reported
    The Bernoulli inclusion probability has a beta prior; the prior directly influences which events are declared relevant, and its shape parameters are never stated.
  • Slab variance sigma_alpha^2 = Not reported
    Inverse-Gamma prior on the slab variance; hyperparameters omitted, and this variance controls the scale of event effects.
  • Number of event lags = 6 lags = 30 minutes
    The paper assumes events affect volatility for up to 30 minutes and treats each lag as a new announcement; the choice is not data-driven.
  • Event selection threshold = 95% posterior inclusion probability
    The paper declares an event relevant if its posterior inclusion probability exceeds 95%; the threshold is chosen by the authors.
assumptions (6)
  • domain assumption 5-minute log-returns have zero mean and Gaussian innovations scaled by volatility
    Equation (1) sets y_t = v_t epsilon_t with epsilon_t ~ N(0,1); zero mean is asserted from Figure 1 and not formally tested.
  • domain assumption Log-variance decomposes additively into level, SV, seasonal, and event components with no interactions
    Equation (2) ht = mu_h + x_t + s_t + e_t; event effects do not interact with seasonality or persistence.
  • ad hoc to paper Event effects are constant per event type and independent of surprise size
    Equation (5) uses binary indicators I_t; a scheduled release with a tiny surprise is treated identically to one with a huge surprise.
  • domain assumption Seasonal component is fixed across days and identical for all weekdays over 2017-2023
    Equation (4) st = H_t' beta with 288 constant 5-minute dummies; no weekday effects or long-term changes in seasonality.
  • domain assumption Only US and home-country announcements affect each currency
    Section 2.1 restricts events to US and Australia for AUD and US and Switzerland for CHF; announcements from other countries are omitted.
  • standard math Kim et al. (1998) seven-component Gaussian mixture approximates the log-chi-square innovation distribution
    Used in MCMC step 3 to sample latent states; this is a standard approximation but still an approximation.

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Cite this review

Pith. "Pith review of What events matter for exchange rate volatility ?." pith.science (2026). https://pith.science/paper/B3B7O6F6

@misc{pith2026241116244,
  author       = {Pith},
  title        = {Pith review of: What events matter for exchange rate volatility ?},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/B3B7O6F6}},
  note         = {Machine review of arXiv:2411.16244}
}
read the original abstract

This paper expands on stochastic volatility models by proposing a data-driven method to select the macroeconomic events most likely to impact volatility. The paper identifies and quantifies the effects of macroeconomic events across multiple countries on exchange rate volatility using high-frequency currency returns, while accounting for persistent stochastic volatility effects and seasonal components capturing time-of-day patterns. Given the hundreds of macroeconomic announcements and their lags, we rely on sparsity-based methods to select relevant events for the model. We contribute to the exchange rate literature in four ways: First, we identify the macroeconomic events that drive currency volatility, estimate their effects and connect them to macroeconomic fundamentals. Second, we find a link between intraday seasonality, trading volume, and the opening hours of major markets across the globe. We provide a simple labor-based explanation for this observed pattern. Third, we show that including macroeconomic events and seasonal components is crucial for forecasting exchange rate volatility. Fourth, our proposed model yields the lowest volatility and highest Sharpe ratio in portfolio allocations when compared to standard SV and GARCH models.

Figures

Figures reproduced from arXiv: 2411.16244 by the authors.

Figure 1
Figure 1. 24-hour window of Australian Dollar returns in % around the FOMC announcement on May 2, 2018. [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Heatmap containing the announcement effect, the posterior mean of [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. Estimated seasonal effect, posterior mean of [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: Average traded volume for the Australian Dollar, in number of contracts, for each 5-minute interval [PITH_FULL_IMAGE:figures/full_fig_p011_4.png]
Figure 5
Figure 5. Figure 5: Scatter plot showing the Australian Dollar average traded volume, in number of contracts, for each [PITH_FULL_IMAGE:figures/full_fig_p011_5.png]
Figure 6
Figure 6. Figure 6: Effect of the level and stochastic volatility components represented by the posterior mean of [PITH_FULL_IMAGE:figures/full_fig_p012_6.png]
Figure 7
Figure 7. Figure 7: 24-hour window of Australian Dollar returns in % around the Nonfarm Payroll announcement on June [PITH_FULL_IMAGE:figures/full_fig_p020_7.png]
Figure 8
Figure 8. Figure 8: 24-hour window of Australian Dollar returns in % around the CPI announcement on July 14, 2017. [PITH_FULL_IMAGE:figures/full_fig_p021_8.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

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