In recent years, concerns over global financial fragmentation have grown amid rising geopolitical tensions. This column integrates a network structure in a multi-country model with imperfect financial markets to study how currency risk premia are connected to financiers’ risk bearing capacity. Guided by the theory, it constructs a centrality-based characteristic that gives a direct role to the centrality of countries’ trade imbalance network in determining their riskiness. The authors find that this centrality-based characteristic embeds strong predictive power in the cross-section of currency movements in the data, reflecting a novel source of exchange rate predictability.
International trade and trade imbalances significantly influence global macroeconomic and financial outcomes. In recent years, concerns over global financial fragmentation have grown amid rising geopolitical tensions, shifting trade alliances, and the weaponisation of financial flows. The Ukraine war, US–China rivalry, and moves toward ‘de-risking’ supply chains have exposed the fragility of global trade and financial networks. In this context, understanding how trade imbalances affect currency dynamics is more urgent than ever.
Global trade drives exchange rate fluctuations and currency risk premia, as highlighted in various exchange rate determination theories (e.g. Gourinchas and Rey 2007, Gabaix and Maggiori 2015, Colacito et al. 2018, Maggiori 2022, Clayton et al. 2024). Empirical studies have established a predictive link between external imbalances and currency excess returns across countries (e.g. Della Corte et al. 2012, 2016, Riddiough et al. 2016, Della Corte and Krecetovs 2021). However, a key challenge arises when applying two-country theoretical models to multi-country empirical settings, as bilateral relationships may not generalise without additional assumptions. In Hou et al. (2025), we extend the theoretical framework of Gabaix and Maggiori (2015) to a multi-country context, incorporating the global trade imbalance network to uncover new insights into currency risk premia. The study provides a framework to capture the indirect, network-driven channels through which trade imbalances shape currency risk premia — especially under financial market imperfections.
Theoretical framework
The Gabaix and Maggiori (2015) model posits that countries with trade imbalances operate in imperfect financial markets, where financiers bear currency risk by buying the currency of deficit countries and shorting that of surplus countries. The financiers’ risk-bearing capacity, limited by the riskiness of their balance sheets, influences exchange rates. Deficit countries’ currencies must offer high returns in the form of currency appreciations to compensate financiers for the risk they bear. The model identifies two critical factors for currency premia: financiers’ risk-bearing capacity and individual countries’ external imbalances.
Extending this to a multi-country setting, we incorporate the global trade imbalance network, which captures bilateral imbalances and interdependencies among countries. This network provides richer information about financiers’ balance sheets than a two-country model. We use trade imbalance network centrality to measure financiers’ risk-bearing capacity, reflecting the complexity of their global balance sheets. The centrality of a country in this network indicates the investment options available to financiers, as a country’s net deficit or surplus creates opportunities for long or short positions in its currency, balanced by opposite positions in other currencies.
The complexity of these balance sheets increases with a country’s network centrality, as it offers more investment options. A country’s net deficit only partially reflects these options, as it induces deficits or surpluses in other countries, further shaping investment opportunities based on network proximity. Centrality quantifies this complexity, providing a more comprehensive measure of financiers’ risk-bearing capacity.
This mechanism predicts that expected currency returns are positively associated with the trade imbalance network centrality.
Empirical analysis
Using data for 41 currencies from 1995 to 2021, we empirically test the model, using a centrality-based characteristic (CBC) for each currency that combines network centrality and exchange rate covariances. Portfolio sorts based on CBC reveal that currency returns increase monotonically from low- to high-CBC countries. A high-minus-low portfolio (long high-CBC, short low-CBC) yields an annualised Sharpe ratio of 0.65, outperforming investment strategies based on total trade network centrality (Richmond 2019), global imbalance measures (Della Corte et al. 2016), and carry trades.
The CBC factor, defined as the time series of excess returns from the CBC-sorted strategy, captures unique information not explained by standard currency factors (e.g. Lustig et al. 2011) or intermediary asset pricing factors (e.g. Adrian et al. 2014, He et al. 2017). Figure 1 plots the long-short portfolios’ cumulative returns from 2003 to 2021. Several characteristics have strong performance before 2008, but after 2008, CBC clearly dominates relative to other sorting variables.
Figure 1 Cumulative returns over time in portfolio sorting comparison
Figure 2 plots average currency excess returns against CBCs for different subsample periods and shows a clear positive relationship. High-CBC countries like Mexico and New Zealand exhibit higher currency premia, while low-CBC countries like Japan and Thailand show lower premia. The figure nicely summarises the insight of this exploration on the global imbalance trade network.
Figure 2 Centrality-based characteristic (CBC) versus currency risk premia
Currency risk premia can be decomposed into three components: total imbalance, individual country importance, and neighbourhood importance. A variance decomposition indicates that the neighbourhood component accounts for approximately 68% of the variation in cross-sectional currency premia, underscoring the trade imbalance network’s critical role. Figure 3 illustrates the decomposition in various parameter settings and the dominance of the neighbourhood importance component in the figure highlights the essential role of trade imbalance network centrality.
Figure 3 Variance decomposition
Applications
In Hou et al. (2025), we also investigate the dynamic behaviour of the CBC around major geopolitical and economic shocks, including the 2018–2019 US–China trade war and the 2022 sanctions imposed on Russia following its invasion of Ukraine. These case studies provide important insights into the endogenous response of trade networks to shocks and how such responses affect currency pricing.
Figure 4 Counterfactual analysis: The effect of US-China trade war
A counterfactual analysis shows that during the 2018 US-China trade war, there was a marked shift in CBC rankings: the effect of the US-China trade war clearly goes beyond impacting these two countries, as bystander countries increased their trade with the US and China. We find that the rank of CNY has not changed, and the nine currencies noticeably affected are THB (3% rise in its ranking), RUB (3% drop), GBP (3% rise), IDR (3% drop), PHP (3% rise), SEK (3% drop), ZAR (3% rise), SAR (3% rise), and MXN (6% drop). Figure 4 shows the significant impact of the 2018 US–China trade war on global currency premia via the model’s counterfactual analysis.
The Russia sanctions episode provides another compelling illustration as shown in Figure 5. As Western countries imposed sweeping trade and financial sanctions on Russia, the country’s CBC plummeted. This sharp decline in centrality coincided with a collapse in the rouble’s value and a dramatic tightening of capital controls. Interestingly, DKK (3% drop), THB (3% drop), and ZAR (3% rise) are not in the group of countries that imposed severe sanctions on Russia but display noticeable changes, while GBP, AUD, JPY, CAD, CHF, and PLN, which are in the group, do not display much of a change. This analysis again shows that the global trade imbalance network brings complexity in assessing the effect of significant international events on currency premia.
Figure 5 Counterfactual analysis: The effect of collective sanctions on Russia
Contributions and conclusions
The research discussed here advances the literature on exchange rate determination by integrating the trade imbalance network into a multi-country model with imperfect financial markets. By introducing CBC, we have a theoretically grounded characteristic for designing profitable currency investment strategies and for understanding the impact of trade shocks on currency movements. The outcome is a refined understanding of the link between international trade and currency risk that describes how the trade imbalance network generates both direct and indirect impacts on currency premia.
Implications for investors and policymakers
The above findings carry important implications for both currency investors and policymakers. For investors, CBC offers a novel and effective predictor of currency risk premia. Incorporating CBC into currency allocation models may enhance return forecasting and improve risk-adjusted performance by capturing information about a country’s indirect exposure within the global trade imbalance network — something that traditional metrics generally omit. For policymakers, CBC provides a powerful tool for assessing external vulnerability beyond bilateral trade statistics. Countries with high trade network centrality tend to bear greater systemic exposure, and may therefore require stronger buffers, such as larger foreign exchange reserves, regional swap lines, or targeted macroprudential tools.
The counterfactual analyses illustrate how geopolitical shocks can propagate through trade networks and affect third-party countries’ currencies. This underscores the need for policymakers to consider indirect spillovers when designing sanctions, tariffs, or supply chain interventions.
Finally, regulators should be aware that network-driven risks can be amplified when intermediaries face balance sheet constraints. A network-informed approach to stress testing and systemic risk monitoring can help mitigate the transmission of shocks across borders.
Source: cepr.org
