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on Open Economy Macroeconomics |
| By: | Swapan-Kumar Pradhan; Eswar S. Prasad; Judit Temesvary |
| Abstract: | We investigate how the U.S. dollar's prominence in the denomination of international debt securities has evolved in recent decades, using a comprehensive global dataset with far more extensive coverage than datasets used in prior literature. We find no monotonic dollarization or de-dollarization trend; instead, the dollar's share exhibits a wavelike pattern. We document three dollarization waves since the 1960s. The last wave, following the global financial crisis, lifted the dollar's share nearly back to its level at the euro's launch in 2000. Our findings are robust to composition and currency valuation effects as well as alternative data definitions. |
| Keywords: | International debt securities; Currency denomination; Nationality and residence basis; Reserve currencies; Banks; Nonbank financial institutions; Nonfinancial corporations |
| JEL: | F30 F41 G15 |
| Date: | 2025–12–16 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedgif:1429 |
| By: | Jorge Miranda-Pinto; Eugenio Rojas; Felipe Saffie; Alvaro Silva |
| Abstract: | We study how production networks shape the severity of Sudden Stops. We build a small open economy model with collateral constraints and input–output linkages, derive a sufficient statistic that maps network structure onto the amplification of tradable shocks, and show that a planner optimally introduces sectoral wedges to reduce amplification. Using OECD input-output data and Sudden Stop episodes, we document systematic network differences between emerging and advanced economies and show they predict crisis severity. A calibrated three-sector DSGE model disciplined by these differences reveals that endowing an advanced economy with an emerging-market production network moves most of the way toward the observed emerging–advanced Sudden Stop gap. |
| Keywords: | networks; financial crises; sudden stops; macroprudential policy |
| JEL: | D85 D57 E32 |
| Date: | 2025–12–01 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedbwp:102336 |
| By: | Dinggao Liu; Robert \'Slepaczuk; Zhenpeng Tang |
| Abstract: | Accurately forecasting daily exchange rate returns represents a longstanding challenge in international finance, as the exchange rate returns are driven by a multitude of correlated market factors and exhibit high-frequency fluctuations. This paper proposes EXFormer, a novel Transformer-based architecture specifically designed for forecasting the daily exchange rate returns. We introduce a multi-scale trend-aware self-attention mechanism that employs parallel convolutional branches with differing receptive fields to align observations on the basis of local slopes, preserving long-range dependencies while remaining sensitive to regime shifts. A dynamic variable selector assigns time-varying importance weights to 28 exogenous covariates related to exchange rate returns, providing pre-hoc interpretability. An embedded squeeze-and-excitation block recalibrates channel responses to emphasize informative features and depress noise in the forecasting. Using the daily data for EUR/USD, USD/JPY, and GBP/USD, we conduct out-of-sample evaluations across five different sliding windows. EXFormer consistently outperforms the random walk and other baselines, improving directional accuracy by a statistically significant margin of up to 8.5--22.8%. In nearly one year of trading backtests, the model converts these gains into cumulative returns of 18%, 25%, and 18% for the three pairs, with Sharpe ratios exceeding 1.8. When conservative transaction costs and slippage are accounted for, EXFormer retains cumulative returns of 7%, 19%, and 9%, while other baselines achieve negative. The robustness checks further confirm the model's superiority under high-volatility and bear-market regimes. EXFormer furnishes both economically valuable forecasts and transparent, time-varying insights into the drivers of exchange rate dynamics for international investors, corporations, and central bank practitioners. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.12727 |
| By: | Katharina Bergant; Andrés Fernández; Ken Teoh; Martín Uribe |
| Abstract: | Employing large language models to analyze official documents, we construct a comprehensive record of daily changes in de jure restrictions on cross-border flows worldwide since the 1950s. Our analysis uncovers the wide array of instruments used to regulate cross-border financial flows and documents their evolving prevalence over the past seven decades. The fine granularity of the new measures allows us to characterize cross-country and time-series variation across eight categories of restrictions, further distinguishing by flow, direction, instrument type, and overall policy stance. We exploit the high frequency nature of the new data to document novel patterns in the use of these restrictions, as well as their relationship to crises, and political economy determinants. We validate our measures against established indicators of capital account regulation and show that our LLM-based classifications both replicate and substantially extend these benchmarks along multiple dimensions. Finally, we examine policymakers’ stated motivations for adopting these restrictions and account for the intensive margin of these policy actions. |
| JEL: | F32 F38 F41 |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34615 |
| By: | Gern, Klaus-Jürgen; Kooths, Stefan; Krohn, Johanna; Liu, Wan-Hsin; Reents, Jan |
| Abstract: | Global output growth slowed down only slightly and proved resilient amid trade conflicts and the resulting increase in uncertainty. World trade has even risen sharply. Both trade and investment continue to be buoyed by the boom in AI-related technologies. Monetary policy in the United States is expected to be eased further, while in the euro area policy rates are likely to remain at their current level for the time being. At the same time, fiscal policy will be expansionary on aggregate-driven in part by substantial increases in defense spending in many countries in response to the altered geopolitical environment. However, the dampening effects of US tariff policy are likely to become increasingly apparent, especially since US tariffs can be expected to remain permanently high. Against this backdrop, we expect the global economic expansion to gradually slow down further over the coming months, as growth in the United States and the euro area is likely to temporarily weaken and the business outlook in China has recently deteriorated. Given the robust developments so far, we have nevertheless revised our forecast for global output - based on purchasing power parities - markedly upwards compared with our autumn forecast, by 0.3 percentage points each for both this year and next, to 3.3 percent and 3.1 percent, respectively. For 2027, we now expect global growth to accelerate to 3.2 percent. The decline in inflation came to a halt in 2025; in the United States, prices have even increased more strongly due to tariffs, and there remains a risk that monetary policy will eventually have to tighten in response. |
| Keywords: | China, Europe, Business Cycle World, European Union & Euro, Fiscal Policy & National Budgets, International Finance, International Trade, Labor Market, Migration, Monetary Policy |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:ifwkeo:334589 |
| By: | Uluc Aysun (University of Central Florida, Orlando, FL); Melanie Guldi (University of Central Florida, Orlando, FL) |
| Abstract: | We revisit the exchange-rate predictability puzzle by asking whether standard, widely used machine-learning (ML) algorithms convincingly improve exchange rate forecasting once evaluation is disciplined and implementation is made robust. Using monthly data from January 1986 to February 2025, we study US dollar to British pound as the baseline case (in both levels and monthly percent changes). We compare five ML methods -- random forests, neural networks, LASSO, gradient boosting, and linear support-vector classification -- against canonical benchmarks (random walk and ARIMA) in a rolling one-step-ahead out-of-sample forecasting design. To mitigate sensitivity to stochastic estimation, we average forecasts across multiple random seeds and assess performance using RMSE and Diebold-Mariano tests. We find that ML does not improve level forecasts and typically underperforms ARIMA. For exchange-rate changes, ML methods consistently outperform the random-walk benchmark, but only neural networks -- under a specific design -- reliably beat ARIMA. A theory-based UIP/PPP filtering approach improves accuracy for both ML and univariate methods, yet does not change the overall ranking. Extensive robustness checks across windows, currencies, frequencies, and tuning choices confirm that ML’s advantages are limited and fragile relative to conventional univariate benchmarks. |
| Keywords: | Machine learning, exchange rates, forecasting, theoretical filtering, random walk, ARIMA. |
| JEL: | C53 F31 F37 G17 |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:cfl:wpaper:2026-01ua |