# Convolutional neural networks to signal currency crises: From the Asian financial crisis to the Covid crisis

Sylvain Barthelemy, Virginie Gautier, Fabien Rondeau

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Sylvain Barthelemy, Virginie Gautier, Fabien Rondeau. Convolutional neural networks to signal currency crises: From the Asian financial crisis to the Covid crisis. International Review of Economics and Finance, 2025, 105, pp.104789. 10.1016/j.iref.2025.104789. hal-05454627

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# Convolutional neural networks to signal currency crises: From the Asian financial crisis to the Covid crisis☆

Sylvain Barthélémy<sup>a</sup>, Virginie Gautier<sup>b,c,∗</sup>, Fabien Rondeau<sup>c,†</sup>

## ARTICLE INFO

Dataset link: Datastream, IMF International Financial Statistics, Organisation for Economic Co-operation and Development, World Bank data bases, here

JEL classification:

F14

F31

F47

Keywords:

Currency crises

Early warning system

Neural network

Convolutional neural network

SHAP values

## ABSTRACT

Currency crises are recurrent events in economic history. They were particularly frequent during the 1980s and 1990s, reflecting diverse underlying causes, and have continued to occur in the early decades of the 21st century. This paper proposes a unified model to examine recent crises across 60 countries between the Asian crisis and the Covid-19 pandemic, including the 2008 global financial crisis and the 2014–2016 commodity-related tensions. The objective is to develop a robust early warning system capable of identifying potential currency crises within a two-year horizon, regardless of their origins. We assess several state-of-the-art machine-learning architectures used in financial forecasting, going beyond conventional econometric benchmarks. For the first time in this literature, particular attention is given to convolutional neural networks, originally designed for image recognition, offering an innovative perspective for the analysis of macro-financial vulnerabilities. The results indicate that CNNs generate more accurate warning signals than other competitive models, such as long short-term memory networks, detecting 24 out of 27 crises in the sample. Moreover, the convolutional-based analysis replicates well-established empirical regularities, assigning varying importance to indicators across subperiods. While the collapses observed between 2014 and 2016 appear primarily driven by domestic macro-financial deterioration, the 2008 and Covid-19 crises are more closely linked to global or US factors.

## 1. Introduction

Currency crises are recurrent phenomena in economic history. They correspond to a sudden and sharp drop in the value of one currency against another (usually the US dollar), with repercussions that extend beyond the affected economy, impacting partner countries through contagion effects. The consequences are numerous and can affect both the financial and real spheres (loss of central bank credibility, debt unsustainability, capital shortages, imported inflation, bankruptcy, GDP losses, etc.). To limit the occurrences of currency crises and other financial crises (banking and debt crises) or mitigate their impact in the future, we need to be able to detect them accurately and well in advance. This can be achieved by setting up an early warning system (EWS) that will send out a warning signal as soon as an economy's vulnerability to currency risk increases. By focusing on the pre-crisis period, EWS models

https://doi.org/10.1016/j.iref.2025.104789

Received 24 November 2024; Received in revised form 21 October 2025; Accepted 27 November 2025

Available online 8 December 2025

1059-0560/© 2025 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

<footnote><sup>a</sup> Gwenlake, Rennes, France</footnote>

<footnote><sup>b</sup> TAC Economics and CREM, University of Rennes, Rennes, France</footnote>

<footnote><sup>c</sup> CREM, University of Rennes, Rennes, France</footnote>

<footnote>☆ We would like to thank the participants of the Forecasting Financial Markets (2023, University of Rennes) and the IMAC workshop (2023, Rennes) for their helpful remarks. We would also like to thank Mr. Chartier Fabien, English teacher at the University of Rennes 1, for proofreading.</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail addresses: sylvain.barthelemy@gwenlake.com (S. Barthélémy), virginie.gautier@univ-rennes.fr (V. Gautier), fabien.rondeau@univ-rennes.fr (F. Rondeau).</footnote>
may be of substantial value to policymakers, enabling them to detect underlying economic weaknesses and vulnerabilities and to implement preventive actions to reduce the risks of crisis.

Following the numerous crises of the 1980s and 1990s, a rich body of literature has emerged seeking to identify their determinants and to predict their onset. Several generations of models have emerged, highlighting different empirical regularities depending on the period analyzed. EWS approaches, such as the “signals” methodology of Kaminsky et al. (1998) [KLR] or the econometric frameworks of Berg and Pattillo (1999b) and Frankel and Rose (1996), represented a decisive step toward systematic modeling of vulnerabilities. These early EWS methods showed that linear probabilistic models generally outperform threshold-based indicators in predictive accuracy and country ranking but also revealed structural limitations, most notably instability around fixed thresholds and the inability to capture contagion effects and non-linear relationships. These limitations paved the way for more flexible data-driven approaches.

In recent years, machine learning and deep learning methods have expanded the empirical toolbox for financial crisis forecasting. Tree-based algorithms (random forests, decision trees and gradient boosting) and support vector machines (SVM) have improved accuracy by modeling non-linearities and complex interactions among macro-financial indicators (Liu et al., 2022; Reimann, 2024; Xu et al., 2018). Yet, these approaches often treat observations as independent, overlooking temporal dependencies that are central to crisis formation. To address this, recurrent architectures such as long short-term memory (LSTM) and gated recurrent unit (GRU) networks have been developed to capture sequential and cumulative dynamics (Barthélémy et al., 2024; Tölö, 2020). These models have achieved higher recall and greater signal continuity than traditional econometric and machine-learning frameworks, confirming the importance of temporal persistence in macro-financial data. However, they remain less suited to detecting simultaneous structural deteriorations across indicator groups or economies, a key feature of crisis contagion.

Convolutional neural networks (CNNs) offer a complementary perspective. Originally designed for image recognition. CNNs can extract local dependencies and hierarchical structures from high-dimensional data, making them well-suited to identify spatial or cross-sectional patterns among correlated indicators. Their growing use in financial forecasting, ranging from asset prices to exchange rates and systemic banking crises (Chen et al., 2024; Loh et al., 2022; Lv et al., 2023), illustrates their potential for detecting non-linear co-movements that precede financial instability. Yet, despite these promising applications, no study to date has examined the use of CNNs for currency-crisis early warning systems. This constitutes a key methodological gap that this paper aims to address.

The present study introduces a convolutional neural network specifically adapted for currency-crisis prediction, designed to capture both cross-indicator interactions and temporal dynamics. CNNs are deep learning architectures originally designed for image recognition, which operate by applying convolutional filters to automatically extract relevant local patterns and hierarchical features from high-dimensional data. Unlike purely sequential models such as LSTM or GRU, the CNN identifies structural co-movements, such as simultaneous reserve depletion, credit expansion, and exchange-rate misalignment, that may precede crises without following smooth temporal trends. Combined with an LSTM layer, the hybrid CNN–LSTM architecture leverages the spatial sensitivity of convolutional filters and the memory capabilities of recurrent networks, offering an integrated view of crisis formation. Compared to traditional econometric models, which often rely on linearity assumptions and limited functional flexibility, convolutional neural networks provide a data-driven framework capable of capturing complex, non-linear relationships among macro-financial indicators (see Section 2 for a detailed discussion).

The aim is to produce accurate warning signals within a two-year warning window before a currency collapse, using a single model for 60 countries simultaneously. The study covers developed, emerging, and developing economies, using monthly data from January 1992 to March 2021. Using a criterion for identifying currency crises inspired by that of Frankel and Rose (1996), this study proposes several candidates for the implementation of an EWS. Our main objective was to test whether predictive gains are achievable using convolutional models for currency crises. We examined historical performance as well as how well these models perform on recent crises. We show that CNNs and CNN–LSTM hybrids outperform traditional econometric and machine learning benchmarks. In out-of-sample tests, the CNN achieves an F1 score of 0.74 and correctly anticipates 24 out of 27 crises within a two-year warning window. The hybrid CNN–LSTM model provides similarly strong and more stable performance, highlighting the complementarity of spatial and sequential modeling. Beyond predictive accuracy, interpretability tools reveal that our models assign high importance to classical crisis predictors (credit growth, unemployment, external imbalances and exchange rate misalignments) while also capturing global contagion channels, particularly during systemic shocks like the 2008 Global Financial Crisis and the Covid-19 pandemic.

By combining high predictive performance with interpretability, this paper makes three main contributions to the literature on early warning systems for currency crises. First, it applies convolution-based deep learning architectures to the prediction of currency crises. While convolutional models have recently been explored for systemic banking crises (Chen et al., 2024), their predictive gains remain to be demonstrated in the case of currency crises. This study goes one step further by combining convolutional and recurrent layers within hybrid CNN–LSTM architectures. The CNN component enhances feature extraction by identifying early deteriorations in macro-financial indicators, while the LSTM component captures the sequential dynamics that determine whether these deteriorations intensify or subside before the crisis. Second, the paper conducts a comprehensive cross-model comparison of econometric, machine learning, and deep learning approaches, assessing their predictive power and robustness within a cross-country panel of 60 developed, emerging, and developing economies over an extended period from 1992 to 2021. The selected hybrid CNN–LSTM model achieves the best trade-off between accuracy and reliability, thereby providing policymakers with a unified and operational early warning tool. Third, by relying on SHAP-based interpretability techniques, the study extends the empirical narrative of currency crises to more recent decades, less covered in the existing literature, notably the Covid period. It provides novel evidence on the interplay between domestic vulnerabilities and global contagion mechanisms, showing how deteriorations in
international risk sentiment and US financial conditions amplify crisis risks beyond country-specific fundamentals. Together, these
contributions extend the scope of currency-crisis research to the most recent global shocks and pave the way for next-generation
early warning frameworks that are both explainable and policy-relevant in an increasingly complex international environment.

This paper is organized as follows. Section 2 reviews the recent literature on the use of deep learning models to forecast financial
crises. Section 3 describes the methodology used to set up an early warning system. Section 4 details the process that guided the
establishment of a neural network based on image analysis. The empirical results are presented and discussed in Section 5. Finally,
Section 6 presents the conclusions of the paper.

## 2. Literature review

The objective of this section is to present the recent developments of the literature in forecasting financial and currency crises.

The literature on early warning systems for currency crises has evolved substantially since the foundational works of Flood and
Garber (1984) and Krugman (1979). These early theoretical models, commonly referred to as first-generation models, explained
crises through unsustainable fiscal and monetary policies that inevitably led to reserve depletion and parity abandonment.
Subsequent second- and third-generation models incorporated self-fulfilling expectations Obstfeld (1995) and financial sector
fragilities Eichengreen et al. (1994), marking a shift from purely fundamental mechanisms toward models integrating expectations,
credit cycles, and contagion effects. While these frameworks provided valuable economic intuition, they remained limited in
empirical prediction. As financial crises became more frequent and heterogeneous, attention turned toward empirical early warning
systems capable of synthesizing multiple indicators into operational policy tools. Two major methodological families emerged: (i)
indicator-based and econometric models and (ii) machine-learning and deep-learning models.

Early empirical research largely followed the signals approach of Kaminsky et al. (1998), which monitored abnormal behavior of
key indicators such as real exchange rate misalignment, reserve losses, and domestic credit growth. Their seminal study established
a systematic framework to detect potential vulnerabilities by associating “warning signals” with deviations of key macroeconomic
variables from their historical norms. The survey conducted by these authors identified a list of 16 indicators considered to herald
future currency collapses when their values crossed specific thresholds, typically defined between the 10th and 20th percentiles of
their distributions. This methodology provided one of the first operational early warning systems for policy use. Its main appeal
resided in its transparency, simplicity, and policy relevance, allowing policymakers to monitor single indicators without requiring
complex estimation or data transformations. Several studies extended this framework to broader time periods and country samples,
including Berg et al. (2004), Berg and Pattillo (1999a, 1999b), Furman and Stiglitz (1998), and Mulder and Bussière (1999).
These works confirmed that indicators such as overvalued real exchange rates, declining reserves, and excessive domestic credit growth
often preceded currency collapses by several quarters. Nevertheless, results varied across studies: while the method performed
well for certain episodes (the Mexican and Asian crises), its predictive accuracy tended to deteriorate when applied to different
time periods or alternative definitions of crisis events. The principal weaknesses stemmed from the arbitrary choice of thresholds,
redundancy among correlated indicators, and the high false-alarm rate that often undermined credibility in real-time settings.

The shift to probabilistic econometric models, particularly logit and probit specifications, represented a major methodological
advancement (Berg & Pattillo, 1999a; Chamon et al., 2012; Eichengreen et al., 1996; Frankel & Rose, 1996; Fratzscher & Bussière,
2002; Gourinchas & Obstfeld, 2011). These models allowed researchers to estimate the conditional probability of a crisis as a
function of key macro-financial fundamentals while testing for the statistical significance of individual predictors. This approach
provided a more rigorous statistical framework, enabling the construction of aggregate crisis probability indices that could be
updated regularly and used for stress testing. Gourinchas and Obstfeld (2011), for instance, combined the KLR method with a
panel logit model to predict currency crises. Their findings revealed that in developed economies, the likelihood of a currency
crisis increased sharply when domestic credit expansion exceeded its long-term trend and the real exchange rate appreciated
substantially. In emerging markets, a sharp decline in international reserves was the most powerful signal, lending support to the
“reserves as insurance” hypothesis. Fratzscher and Bussière (2002) further refined the framework by integrating crisis severity and
duration into a multinomial logit model (post-crisis bias considerations), reporting in-sample accuracies above 80%. This confirmed
the strong explanatory power of fundamentals such as current account deficits, external debt levels, and reserve adequacy. More
recent extensions have sought to address the limitations of static coefficients and regime invariance by adopting Markov-switching
or regime-dependent logistic models. These models allow coefficients to vary across latent states corresponding to tranquil and
turbulent regimes, thereby partially mitigating parameter instability and improving adaptability across time. In Du et al. (2020), the
Markov-switching model shows evidence of state switching before crisis in many different currency markets and performs better than
the probit model used as a benchmark. Overall, the econometric approach, especially logistic regression, remains an indispensable
benchmark for empirical crisis forecasting. Its key strengths include theoretical grounding, interpretability, policy relevance, and
robustness in small samples, making it particularly valuable in macroeconomic applications where data availability is limited.
Nonetheless, the class suffers from structural limitations: linear functional forms constrain non-linear feedback representation,
while assumptions of independence and homogeneity limit the modeling of contagion, cross-country spillovers, and regime shifts.
Moreover, the inclusion of many correlated indicators can generate multicollinearity and overfitting, and the fixed lag structures
often fail to capture gradual buildup or delayed transmission mechanisms observed in real crises. These limitations have motivated
the growing use of flexible and non-linear frameworks.

Recognizing the limits of linear econometric models, the literature progressively adopted data-driven machine learning ap-
proaches to capture non-linear relationships and threshold effects. Early work includes Ghosh and Ghosh (2002), who employed
a classification tree to identify non-linear interactions among macro-financial indicators such as reserves, current accounts, and
short-term debt. Their model provided interpretable decision rules and achieved higher recall than their probit model, especially when crises stemmed from multiple vulnerabilities. However, its predictive stability was weak, as small data changes could alter tree structure. Frankel and Wei (2004) further demonstrated the value of flexible, nonparametric models by applying a regression tree to assess external vulnerability. Their results revealed strong non-linear and asymmetric effects: for instance, reserve depletion sharply increased crisis probability when short-term debt was already high. These models improved out-of-sample performance compared to linear regressions. Later studies such as de Carvalho Filho et al. (2020) and the work of Xu et al. (2018) advanced this line of research through ensemble techniques, like random forests, that aggregate multiple weak learners to enhance robustness and reduce overfitting. Xu et al. (2018) go further by transforming predictors via wavelet analysis, extracting features across multiple time scales, and combining them with standard macro indicators. Their ensemble model dominates logistic regression benchmarks in AUC and classification accuracy and shows that wavelet-based predictors like exchange rate appreciation and overvaluation over long horizons carry strong predictive weight. Liu et al. (2022), Ramli et al. (2015) and Reimann (2024) extend the conclusions regarding the outperformance of machine learning models to a broader set of algorithms, including decision trees, random forests, gradient boosting and extremely randomized trees. Still, like many ensemble models, it treats observations largely as independent and does not capture temporal dynamics or sequential buildup of stress very explicitly.

Because financial crises evolve gradually through cumulative imbalances and complex feedback loops, models capable of capturing sequence dependencies and non-linear dynamics are natural candidates for early warning applications. Early efforts to incorporate non-linear and temporal structures into EWS date back to Nag and Mitra (1999), who pioneered such an approach by applying a simple recurrent neural network to macro-financial data for a panel of emerging markets. Their study demonstrated that crises are not triggered by isolated shocks but rather by persistent deterioration across variables such as reserves, real exchange rates, and domestic credit. However, due to the computational and methodological constraints of the late 1990s, their architecture remained shallow and sensitive to noise, with limited capacity for regularization or interpretation. Later studies such as Sevim et al. (2014) confirmed the predictive potential of neural networks for exchange-rate crises: focusing on Turkey, they demonstrated that standard feedforward networks outperform logistic regression and decision trees in both accuracy and sensitivity, emphasizing the value of non-linear feature extraction. A few years later, advances in deep learning reactivated interest in recurrent models. The development of long short-term memory and gated recurrent unit architectures introduced gating mechanisms that can retain long-term dependencies while mitigating vanishing gradient problems. Tölö (2020) applied both LSTM and GRU networks to the prediction of systemic banking crises in a cross-country dataset, finding that these architectures significantly outperformed logit and random forest benchmarks in terms of recall and signal stability. Building on these advances, Barthélémy et al. (2024) developed an LSTM/GRU-based early warning system for currency crises across 68 countries between 1995 and 2020. Their model detected over 90% of crisis episodes within a two-year forecast window while reducing false alarms. The sequential nature of the LSTM architecture allowed it to internalize both persistence and non-linear threshold effects, key weaknesses of traditional econometric models. Although the LSTM network appears to be the most effective EWS in their study (detecting crises with fewer false signals), logistic regression proves to be a more robust EWS than the random forest or standard recurrent networks, maintaining its benchmark position in the literature. This finding echoes the conclusions of Kareem and Husien (2025), who provide a comprehensive review of crisis prediction models spanning econometric, machine learning, and deep learning frameworks. They conclude that no single approach dominates across contexts but emphasize that the complex nature of crises, which is characterized by interactions among multiple variables, propagation effects, non-linearities, and regime shifts, requires models capable of capturing these non-linear dynamics, such as sophisticated neural networks and ensemble methods. These recurrent models succeed in modeling persistence, lagged feedback, and non-linearity. But their limitations include difficulty capturing synchronous multivariate shocks across indicator groups (for example, abrupt joint deviations in reserves, credit and exchange rate), sensitivity to hyperparameters, overfitting in small-sample contexts, and limited capacity to detect spatial structure across indicators.

More recently, deep learning architectures have been developed to jointly capture spatial patterns (across indicators or across countries) and temporal dynamics in macro-financial data. Convolutional neural networks, though originally designed for image recognition, can be adapted to extract local dependencies and cross-sectional configurations from multivariate time series organized as two-dimensional matrices or “heatmaps”. This representation allows the model to learn complex co-movements, such as simultaneous reserve depletion, credit expansion, and exchange-rate misalignment, that often precede crises but remain invisible to purely sequential models. Temporal convolutional networks (TCNs) extend CNNs with causal dilations to capture long-range sequences. Chen et al. (2024) propose one of the first TCN-based early warning systems for systemic banking crises. Using quarterly macro-financial data covering more than 40 economies over three decades, they compare TCNs with conventional logit, feedforward neural networks, and LSTM architectures. Their results show that TCNs outperform all benchmarks in terms of accuracy, recall, and forecast stability, achieving an AUC around 0.76 versus 0.65 for LSTM and 0.50 for logistic regression. Importantly, the TCN’s hierarchical convolutional structure allows it to identify multiscale dependencies, detecting both gradual imbalances and abrupt shifts, while maintaining a lower false-alarm rate. The authors also incorporate explainability tools (SHAP values), revealing that stock price and GDP evolutions are consistently the strongest contributors to predicted crisis probabilities. These findings underline the potential of convolutional architectures to capture cross-indicator interactions but also temporal persistence when combined with recurrent mechanisms in early warning applications. Convolutional neural networks are also increasingly being applied to financial asset price forecasting, including exchange rates (Loh et al., 2022; Lv et al., 2023). They are emerging as strong competitors to recurrent neural networks, thereby reinforcing their relevance for detecting currency crises. The functioning of CNNs and LSTM neural networks is detailed in Section 3.

Building on these developments, CNNs address several fundamental limitations of both traditional econometric models and standard recurrent architectures. Classical econometric approaches, such as logistic regression or probit, rely on linearity and
distributional assumptions, restricting their ability to capture the non-linear, interacting, and high-dimensional dynamics that
typically characterize pre-crisis environments. They also require a priori variable selection and are sensitive to multicollinearity,
making them less effective when multiple indicators co-evolve in complex ways. In contrast, CNNs provide a data-driven alternative
capable of autonomously extracting relevant features and recognizing latent spatial patterns from large macro-financial datasets
without explicit functional specification. When multivariate time series are transformed into two-dimensional heatmaps, CNNs
can uncover multi-indicator configurations that are often invisible to linear or purely sequential models. This capacity for
autonomous feature extraction proves particularly valuable in our setting, which involves 124 indicators across 60 countries, where
interdependencies and non-linear propagation mechanisms are pervasive. Beyond their advantages over econometric models, CNNs
also bring complementary strengths relative to recurrent neural networks (RNNs) such as LSTM and GRU architectures. While LSTMs
are specifically designed to capture temporal dependencies and long-term memory effects, they may overlook localized structural
deteriorations across indicator groups that do not follow smooth or gradual dynamics. CNNs, by contrast, excel at identifying
spatially correlated deteriorations without requiring continuous temporal trends. This ability is especially relevant for addressing
the “post-crisis bias”, where models relying solely on sequential information may fail to detect early structural breaks that precede
a crisis. In this sense, CNNs and LSTMs are complementary: the CNN captures complex multivariate relationships, while the LSTM
preserves temporal coherence. This complementarity underpins our CNN–LSTM hybrid model, which leverages both spatial and
sequential information for early crisis detection.

Nonetheless, CNNs are not without their limitations. First, their interpretability remains more limited than that of econometric
models, which can hinder their acceptance in policy-oriented contexts. Second, CNNs are sensitive to input representation: their
performance depends on how macro-financial variables are preprocessed and structured into spatial formats. Third, due to their
large number of parameters, CNNs may overfit high-dimensional data, especially in relatively small samples. Finally, CNNs alone
do not inherently capture long-term dependencies. Their convolutional kernels focus on local neighborhoods, which may neglect
gradual imbalances building up over time. To address these challenges, we implemented a series of methodological safeguards. We
first used SHAP (SHapley Additive exPlanations) values to improve interpretability and to identify the most influential indicators
contributing to crisis predictions, thereby bridging the gap between black-box learning and economic reasoning. We also conducted
extensive cross-validation and grid search optimization to mitigate overfitting and ensure robustness across temporal and regional
subsamples. Most importantly, we integrated an LSTM layer after the convolutional blocks, allowing the model to retain sequential
memory and capture the temporal accumulation of vulnerabilities. This hybrid design helps overcome the CNN’s main shortcoming,
its lack of explicit temporal awareness, while preserving its superior capacity for multidimensional pattern recognition.

3. EWS methodology

This section outlines the procedure for setting up an early warning system and reviews its key steps: dating currency crises,
constructing the target variable, selecting explanatory variables and models, and conducting training and evaluation, as illustrated
in Fig. 1.

3.1. Currency crises and target variable

Many criteria have been proposed in the literature to define and identify currency collapses. There are two main approaches for retrospectively dating currency crises: those based solely on the loss in value of the currency and those incorporating defense mechanisms against depreciation pressures (see the exchange market pressure index proposed by Eichengreen et al., 1996). In this study, we define currency crises as episodes of sharp depreciation. Specifically, we draw on the Frankel and Rose (1996) criterion, according to which a currency crisis occurs when the annual nominal depreciation against the dollar exceeds 25% and is at least 10% higher than the depreciation observed in the previous year, if a prior depreciation had occurred.

We have chosen a depreciation threshold of 20%, lower than the 25% proposed by Frankel and Rose and the 30% used in Laeven and Valencia (2020). This threshold was chosen based on a sensitivity analysis (see Fig. B.1), following the approach of the reference papers. The 10% increase criterion, comparing the current depreciation to that of the previous year, was retained to avoid counting multiple depreciations belonging to the same crisis. Months meeting these criteria were assigned a value of 1; all others were assigned 0. To limit the impact of post-crisis bias,<fnref n="1" /> a recurring problem in the related literature, only the first depreciation that met the criterion was assigned a value of 1 when multiple similar depreciations occurred within a 24-month period.

For this study, monthly nominal exchange rates against the dollar were obtained from the IMF International Financial Statistics
database. While monthly rates are more volatile than quarterly or annual aggregates, this frequency provides a suitable balance
between precision and stability. The monthly granularity improves the accuracy of crisis dating and allows the model to capture
gradual pre-crisis trends. It also increases the effective sample size, enabling convolutional filters to extract richer temporal and
cross-indicator patterns.

Once the binary crisis-dating variable was constructed, we defined the target variable by setting an alert window. The goal is to
predict the likelihood of a currency crisis within a defined time horizon. We selected a two-year warning window, consistent with
standard practice in the literature (Berg & Pattillo, 1999b and KLR). This window was also motivated by the observation that many
leading indicators decline in the 24 months preceding a currency collapse (see Fig. B.2). Within this window, months were assigned
a value of 1 to indicate pre-crisis vulnerability and 0 otherwise, allowing the models to learn the patterns preceding a crisis.

<footnote n="1">¹ A crisis can last for several years, keeping macroeconomic and financial indicators at deteriorated levels, which, because they behave in the same way as the pre-crisis period, can be mistakenly considered as a signal of a future crisis.</footnote>
<figure><img src="image_4.png" /><figcaption>Fig. 1. Early warning system procedure.</figcaption></figure>

3.2. Leading indicators

The selection of input variables is a central element in the design of early warning systems for currency crises. In line with the presented literature, a broad set of indicators has been assessed in this study. The sample runs from January 1992 to March 2021 and encompasses a varied set of currency crises whose origins may differ. The aim of this study is to identify the most relevant variables according to time and context, compare these findings with established theories, and build a robust early warning system for currency crises.

Foundational approaches such as Berg and Pattillo (1999b) and Kaminsky et al. (1998) relied on a compact but coherent set of monthly macroeconomic indicators, such as international reserves, real exchange rate misalignment, domestic credit growth, current account balance, terms of trade, monetary aggregates, interest rates, and stock indices, to identify vulnerable fundamentals preceding speculative attacks. Later studies expanded this set to incorporate broader financial and institutional variables. For example, Nag and Mitra (1999) emphasized monetary and interest rate series to capture sequential dynamics; Sevim et al. (2014) applied a rich indicator set tailored to Turkey, allowing neural networks to discriminate relevant variables without strict theoretical priors; and Ramli et al. (2015) demonstrated that combining heterogeneous indicators (external, monetary, and financial) improves predictive robustness. Kareem and Husien (2025) confirm this result, showing that regardless of methodology, four recurrent groups of indicators consistently emerge: (i) external imbalances (current account, reserves), (ii) real exchange rate misalignment, (iii) internal and external debt, and (iv) financial market and contagion variables.

Building on these insights, our variable selection strategy follows the theoretical foundations of the first, second, and third generations of crisis models while integrating insights from the recent empirical literature. First-generation models (Flood & Garber, 1984; Krugman, 1979) link crises to unsustainable macroeconomic policies and reserve depletion under fixed exchange rates, motivating the inclusion of international reserves, monetary aggregates, fiscal balance and inflation. These indicators directly measure pressures on the central bank's ability to defend the currency, reflecting the inherent constraints of the Mundell–Fleming trilemma.<fnref n="2" /> Second-generation models (Obstfeld, 1995) introduce political and credibility dimensions, where the cost of maintaining a peg increases with rising interest rates, unemployment, or slowing growth. These factors can undermine the government's commitment to a fixed parity. These models incorporate the concept of multiple equilibria, recognizing that fundamental indicators alone may not adequately explain all currency collapses, as observed in the 1990s. Third-generation models emphasize financial fragility arising from excessive foreign-currency borrowing and balance-sheet mismatches, a phenomenon often described as the “original sin” (Eichengreen et al., 2003). This structural dependence on external debt denominated in foreign currency exposes domestic firms and governments to valuation shocks during depreciation episodes, amplifying the severity of crises. Accordingly, our model includes indicators of credit expansion, public and private debt, and capital flows to capture these vulnerabilities.

<footnote n="2">² It is impossible for a country to achieve all three policy objectives simultaneously (fixed exchange rate, free capital movements and monetary policy autonomy). A country can only have two out of the three at any given time.</footnote>
<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Currency crises (1970–2023) by level of development. Classifications of countries based on their level of development according to the International Monetary Fund. Dated on monthly data, then aggregated annually. Use of the Frankel and Rose criterion with thresholds of 20% for annual depreciation and 10% for the change in the rate of depreciation compared with the previous year.</figcaption></figure>

Beyond domestic fundamentals, recent crises have underscored the importance of global and cross-country linkages. Since currencies are quoted against the US dollar, we include relevant US macroeconomic and financial indicators. The rationale is twofold. First, the United States plays a central role in shaping the global economic environment by influencing risk perceptions and liquidity, making certain US indicators comparable to global variables. Second, the macroeconomic and financial situation in the United States affects exchange rates mechanically through bilateral parities. In particular, US interest rates, inflation, stock market indices, and the VIX index<fnref n="3" /> capture shifts in liquidity and investor sentiment that can amplify vulnerabilities in the studied economies, even though US developments alone cannot trigger a crisis.

To continue, Fig. 2 shows that crises often occur in successive waves, a pattern consistent with the presence of contagion across economies. In this study, contagion is defined as a mechanism that triggers a crisis in a specific economy for reasons unrelated to its domestic fundamentals. Following Masson (1999), contagion can arise from the deterioration of the global environment, influenced by policies of industrialized nations, trade connections, competition, and self-fulfilling expectations. It can also depend on geography (Glick & Rose, 1999), development levels, and investor perceptions of both the global and domestic economic environments.

From this theoretical and empirical grounding, the indicators used in our EWS are organized into eight blocks, each corresponding to a major channel of crisis transmission:

* **Exchange rates:** Real and nominal exchange rates are central to detecting currency misalignments. Persistent overvaluation can signal pressures toward correction, consistent with first-generation models, while sharp depreciation or abnormal volatility may indicate speculative pressures and market stress.

* **External trade:** Variables such as current account balances, trade deficits, and terms-of-trade shocks capture external imbalances that can undermine currency stability. Deteriorating trade fundamentals signal pressures on reserves and potential vulnerability to speculative attacks.

* **Activity and debt:** Public and private debt levels, as well as credit growth, reflect financial fragility. High leverage or rapid credit expansion increases vulnerability to shocks, particularly when debt is denominated in foreign currency, as emphasized in third-generation models. Accelerating debt or credit issuance often precedes crises and serves as a predictive signal.

* **Interest rates:** Domestic and policy interest rates measure the cost of maintaining currency pegs and the stance of monetary policy. Rising rates may reflect attempts to defend a fixed parity or contain inflation and are associated with increasing costs for authorities, consistent with second-generation models.

<footnote n="3">³ The Chicago Board Options Exchange Volatility Index (VIX) is a widely recognized measure of the stock market's anticipated volatility based on S&P 500 index options.</footnote>
* **Money:** Monetary aggregates and related variables indicate liquidity conditions and potential overheating of the economy. Rapid expansion of money supply can increase inflationary pressures and signal growing vulnerability under fixed exchange rate regimes.

* **Oil price/Commodity shocks:** For commodity-dependent economies, sudden changes in oil or other key commodity prices affect fiscal balances, inflation, and the current account, generating external pressures that may precipitate currency crises.

* **Financial markets:** Equity indices (US and MSCI Emerging Markets) and the VIX capture shifts in investor sentiment and global risk perception. Heightened volatility or declining stock markets often coincide with international portfolio rebalancing, liquidity shocks, and contagion effects, providing early warning signals of external pressures.

* **Contagion:** Variables capturing spillover effects across countries, such as global financial indicators or regional crisis patterns, reflect the risk of crises spreading beyond domestic fundamentals. This block operationalizes the transmission channels highlighted in the contagion literature, including trade, finance, and investor expectations.

The full list of 124 variables tested is presented in Table B.2, and descriptive statistics for non-categorical variables are provided in Table B.4. A discussion of the treatment of multicollinearity, endogeneity, and autocorrelation issues is provided in the dedicated Appendix.

Fig. B.2 illustrates the empirical patterns observed in both pre-crisis and post-crisis periods for some of the non-categorical variables. The analysis of macroeconomic and financial indicators across the 60 countries of the study reveals several empirical regularities consistent with the literature. Spanning crises of the 1990s, 2000s, and 2010s, the sample exhibits variation in crisis origins, enabling the identification of patterns associated with different generations of models. Examining the cyclical component of the real exchange rate, extracted via the Hodrick-Prescott filter, indicates a gradual overvaluation in the months preceding a crisis, suggesting pressures toward realignment. International reserves serve as a defensive buffer against speculative attacks, with notable depletion observed in the 18 months leading up to a crisis. Similarly, the growth of public debt and domestic credit relative to GDP often accelerates prior to a collapse, reflecting potentially unsustainable indebtedness, particularly under fixed exchange rate regimes. Global financial conditions also provide timely signals: heightened correlations between the VIX and exchange rates precede crises, while declines in US stock indices and the MSCI Emerging Markets index often accompany these periods of stress. Together, these domestic and international indicators offer a coherent set of early warning signals, providing the empirical foundation for constructing a predictive model of currency crises. By monitoring these indicators, the EWS can detect early signs of pressure on the currency, providing timely alerts before full-blown crises occur.

### 3.3. Models

The effectiveness of an early warning system hinges on the development and fine-tuning of efficient classification models. The main contribution of this study lies in the innovative application of a convolutional neural network for implementing an early warning system for currency crises. To assess its performance, we simultaneously constructed five alternative models: a logistic regression, the standard in the literature on financial crises; a decision tree, a machine learning model known for its robustness and interpretability; a random forest and a support vector machine (SVM), two powerful machine learning models; and a long short-term memory neural network, one of the most advanced recurrent neural networks. In existing studies of financial crises, the LSTM has shown more stable performance and higher predictive power than the literature standard and the GRU, making it a relevant benchmark. Finally, we introduce a more elaborate variant of CNN called CNN-LSTM, which incorporates an LSTM layer for classification. This design aligns with the evolving field of temporal convolutional networks mentioned previously.

Artificial neural networks are models originally inspired by the biological neural system, which combines statistical and optimization techniques to enable machine learning and prediction. Two types of networks can be distinguished: feed-forward and recurrent neural networks. This study considers both categories. Conventional CNNs are feed-forward networks, where information flows unidirectionally from the input layer to the output layer. In contrast, the LSTM, our benchmark neural network, is a recurrent network tailored for sequential data, including time series.

LSTM neural networks (Hochreiter & Schmidhuber, 1997) are designed to adapt to sequential data due to their feedback loops (a specificity of recurrent neural networks). The input variables are combined with information stored in memory, distinguishing between short- and long-term memory, to formulate predictions that account for sequence order and dependencies. LSTM neural networks retain information across iterations, using their own output as input for subsequent steps. Consequently, the model does not treat individual points independently but perceives them as a sequence. Standard recurrent networks have evolved to include a memory vector updated via a gating mechanism, enabling information to be retained over extended periods. Three gates (input gate, forget gate, and output gate) filter pertinent information and contribute to forming the memory vector. The incorporation of an additive gradient, combined with the gating mechanism, allows LSTM neural networks to effectively address the vanishing gradient problem commonly encountered by standard RNNs.

Convolutional networks (Le Cun et al., 1990) are designed for image analysis, traditionally taking two- or three-dimensional data as input. As discussed in Section 4, multivariate time series can be represented in the form of images called heatmaps. CNNs operate in two key phases: feature extraction using convolution and pooling layers, followed by classification with fully-connected layers. The strength of CNNs lies in the feature extraction phase, during which filters create feature maps, i.e., distinct representations of input images that highlight patterns crucial for prediction. The networks autonomously learn to focus on relevant areas in the images, reducing the need for manual feature selection. Once decisive patterns are identified, they are passed to the classification network to generate predictions.
While CNNs excel at identifying spatial or cross-sectional patterns, they do not explicitly account for temporal dependencies, an essential aspect in the dynamics of financial crises. To overcome this, we integrate a recurrent layer capable of modeling temporal memory, thereby allowing the network to capture how vulnerabilities evolve and accumulate over time. In the CNN-LSTM, the CNN feature extraction structure is retained and combined with one or more LSTM layers for classification. This hybrid architecture leverages the strengths of both networks: the CNN identifies features indicating deteriorations, while the LSTM retains previously recognized patterns to determine whether they are intensifying or attenuating. Incorporating LSTM layers addresses the artificial sequential nature of input images by introducing a genuine memory mechanism. This approach is widely used in text and video analysis (Zatsarynna et al., 2021) and in multivariate time series analysis (Lv et al., 2023), forming part of the development of integrated temporal convolutional networks (Chen et al., 2024; Lea et al., 2017).

The detailed functioning of LSTM neural networks and CNNs is described in the dedicated Appendix.

## 3.4. Training and performance measurement

Once the database has been created, the target variable defined, and the appropriate models selected, it is essential to optimize their parameters to enhance overall performance.

The database spans January 1992 to March 2021 and includes 60 countries (unbalanced panel). To identify the set of hyperparameters ensuring the highest and most consistent performance, we used time series cross-validation via the expanding window method. The models were trained and evaluated over 11 training/validation sets, covering the entire in-sample period from January 1992 to December 2017. Each validation sample spans two years, with the first beginning in January 1996. For each training/validation set, the training sample runs from January 1992 to December of the year preceding the start of the validation sample, thus preventing any data leakage. Hyperparameters of all models were optimized using the grid search algorithm in Python. Subsequently, the 11 optimized architectures were tested on all validation samples to identify the configuration with the highest and most stable performance for each model and then tested out-of-sample. The out-of-sample assessment period covers January 2018 to March 2021.

To evaluate model performance in this classification problem, we used precision, recall, F1 score, and area under the ROC curve (AUC). The unbalanced nature of the target variable was accounted for using metrics derived from the confusion matrix. Notably, the F1 score emerged as the preferred metric for comparing models because it combines precision and recall, providing a comprehensive measure of prediction quality.

# 4. Preliminary steps in image analysis

This section presents the methodology used to construct heatmaps, which serve as inputs to the CNN, and details the empirical rationale for choosing a convolutional network to implement an early warning system for currency crises.

## 4.1. Data collection

This study relies on monthly data from January 1992 to March 2021, covering 60 currencies quoted against the US dollar (base currency). These currencies represent a wide range of economies, including those classified as developed, emerging, and developing according to the IMF classification (see Table B.1 for the complete list). Data were sourced from the IMF's International Financial Statistics, the Organization for Economic Co-operation and Development (OECD), the World Bank database, Datastream, the Gourinchas and Obstfeld database and the Chinn-Ito database.

## 4.2. Image analysis

Multivariate time series can be represented in a format compatible with convolutional networks, that is, as two-dimensional images. Here, the height corresponds to time steps and the width to features. By moving the window forward one time step while keeping its size constant, many two-dimensional inputs can be generated.

It should be noted that, at this stage, these adjacent series do not constitute a traditional image in the conventional or computer sense of the term.<fnref n="4" /> Time series must be scaled to a comparable range to ensure proper CNN functioning and to facilitate visualization of degradation areas on heatmaps. A heatmap is a graphic representation of statistical data that maps the intensity of a variable onto a color chart in a two-dimensional matrix. In this case, values near 1 appear red, indicating severe deterioration, while values near -1 appear green, indicating improvement. Before analyzing these heatmaps and feeding them into the CNN, it is crucial to arrange indicators to maximize the visibility of heat zones, which we achieved using hierarchical clustering based on Ward's algorithm.

The heatmap in Fig. 3 illustrates changes in macro-financial indicators for Colombia, the United States, and selected global factors between 2018 and 2021. The Colombian peso depreciated by over 20% against the dollar in March 2020, marking a currency crisis. The heatmap shows an increase in heat zones in the months preceding the collapse, followed by a further intensification after the crisis. Visually, distinct patterns appear to precede the currency collapse, supporting the use of image-based analysis.

<footnote n="4">⁴ Two-dimensional images with pixels scaled between 0 and 1 for black and white images or three-dimensional images with pixels scaled between 0 and 255 for color images using the red-green-blue convention.</footnote>
<figure><img src="image_6.png" /><figcaption>Fig. 3. USD/COP heatmap during the Covid crisis.</figcaption></figure>

<table><caption>**Table 1**
K-means clustering outcomes for a 24-month post-crisis period.</caption><thead><tr><th></th><th></th><th>cluster %</th><th>period %</th></tr></thead><tbody><tr><td rowspan="3">Tranquil</td><td>cluster 1</td><td>68%</td><td>61%</td></tr><tr><td>cluster 2</td><td>51%</td><td>8%</td></tr><tr><td>cluster 3</td><td>42%</td><td>31%</td></tr><tr><td rowspan="3">Pre-crisis</td><td>cluster 1</td><td>24%</td><td>56%</td></tr><tr><td>cluster 2</td><td>8%</td><td>3%</td></tr><tr><td>cluster 3</td><td>22%</td><td>41%</td></tr><tr><td rowspan="3">Crisis/post-crisis (24 months)</td><td>cluster 1</td><td>8%</td><td>19%</td></tr><tr><td>cluster 2</td><td>41%</td><td>16%</td></tr><tr><td>cluster 3</td><td>36%</td><td>65%</td></tr></tbody></table>

To further validate this intuition, we applied an unsupervised classification model using k-means clustering. The goal was to determine whether the input images, comprising 124 explanatory variables over a 12-month window, arranged according to the clustering algorithm, could intuitively separate periods into tranquil, pre-crisis (24-month window), and crisis/post-crisis (12-, 18-, and 24-month windows after a currency collapse were tested) without specifying labeled responses. The objective was to replicate the method of Barthélémy et al. (2019). The k-means algorithm classifies observations into groups with similar characteristics, requiring only the specification of the number of clusters. Several specifications regarding the number of clusters (k = 2, 3 or 4) were tested. Based on elbow and silhouette criteria, two clusters proved optimal. Table 1 shows the assignment of observations to clusters for the 24-month crisis/post-crisis window, while results for other windows and cluster specifications are reported in Table C.1.

The algorithm effectively separates crisis periods, as 65% of crisis/post-crisis observations are assigned to cluster 3. However, most tranquil and pre-crisis observations are grouped in cluster 1, indicating a potential lack of sophistication in the k-means algorithm. These results suggest that macro-financial image analysis is promising for distinguishing crisis and post-crisis periods, a common challenge known as post-crisis bias (one to two years after the collapse). However, differentiating tranquil from pre-crisis periods remains difficult, validating the need for a more sophisticated model such as a convolutional network.

# 5. Results and discussion

Heatmap analysis and k-means classification showed encouraging results, suggesting that macro-financial image analysis could be a promising approach for studying currency crises. Building on this intuition, we focus on implementing an early warning system using a convolutional network, which can take image-like inputs similar to the heatmaps and identify more complex patterns than k-means. The CNN was also supplemented with an LSTM layer to incorporate a sequential mechanism. To validate this approach, the performance of the CNN-based EWS was compared with several standard models from the literature and models known for strong performance on time series data. The following section evaluates the performance of these models.

## 5.1. Performance

During training, each model was tested on 11 distinct validation samples to determine the hyperparameter set yielding the best average performance and to promote generalization to unseen data. Neural networks, especially those with convolutional layers,
<figure><img src="image_7.png" /><figcaption>Fig. 4. Box plot of F1 scores during cross-validation (1992–2017).</figcaption></figure>

<table><caption>**Table 2**
Comparison of out-of-sample performance.</caption><thead><tr><th></th><th>F1</th><th>Precision</th><th>Recall</th><th>AUC</th><th>Crises detected<sup>a</sup></th><th>False signals<sup>b</sup></th><th>Post-crisis bias<sup>c</sup></th></tr></thead><tbody><tr><td colspan="8"><strong>All variables</strong></td></tr><tr><td>Logistic regression</td><td>0.42</td><td>0.35</td><td>0.53</td><td>0.66</td><td>18</td><td>65%</td><td>1%</td></tr><tr><td>Decision tree</td><td>0.42</td><td>0.27</td><td>0.98</td><td>0.71</td><td>27</td><td>73%</td><td>9%</td></tr><tr><td>Random forest</td><td>0.52</td><td>0.43</td><td>0.68</td><td>0.74</td><td>20</td><td>57%</td><td>5%</td></tr><tr><td>SVM</td><td>0.45</td><td>0.45</td><td>0.45</td><td>0.67</td><td>19</td><td>55%</td><td>0%</td></tr><tr><td>LSTM</td><td>0.52</td><td>0.44</td><td>0.63</td><td>0.73</td><td>22</td><td>56%</td><td>6%</td></tr><tr><td>CNN</td><td>0.44</td><td>0.41</td><td>0.48</td><td>0.67</td><td>20</td><td>59%</td><td>1%</td></tr><tr><td>CNN-LSTM</td><td>0.50</td><td>0.42</td><td>0.62</td><td>0.72</td><td>20</td><td>58%</td><td>8%</td></tr><tr><td colspan="8"><strong>Subset of variables</strong></td></tr><tr><td>Logistic regression</td><td>0.58</td><td>0.59</td><td>0.57</td><td>0.74</td><td>23</td><td>41%</td><td>17%</td></tr><tr><td>Decision tree</td><td>0.67</td><td>0.60</td><td>0.75</td><td>0.82</td><td>21</td><td>40%</td><td>37%</td></tr><tr><td>Random forest</td><td>0.68</td><td>0.69</td><td>0.67</td><td>0.80</td><td>24</td><td>31%</td><td>30%</td></tr><tr><td>SVM</td><td>0.60</td><td>0.47</td><td>0.85</td><td>0.82</td><td>26</td><td>53%</td><td>11%</td></tr><tr><td>LSTM</td><td>0.66</td><td>0.61</td><td>0.74</td><td>0.82</td><td>23</td><td>40%</td><td>23%</td></tr><tr><td>CNN</td><td>0.74</td><td>0.72</td><td>0.76</td><td>0.85</td><td>24</td><td>28%</td><td>48%</td></tr><tr><td>CNN-LSTM</td><td>0.70</td><td>0.66</td><td>0.74</td><td>0.83</td><td>24</td><td>34%</td><td>40%</td></tr></tbody></table>

exhibited more stable performance than other models, with the exception of SVM, as illustrated by the tighter box plots in Fig. 4. This suggests superior generalization capacity regardless of the period considered.

For each model (logistic regression, decision tree, random forest, SVM, LSTM, CNN and CNN-LSTM), two specifications were tested on the panel dataset. The first specification used all explanatory variables, while the second relied on a subset selected via variable selection algorithms on validation samples, the list of which is available in Table B.3. The neural network structures are shown in Fig. A.4, including the number of lags used to construct the input vectors (6 for LSTMs, 9 for CNNs and CNN-LSTMs). Performance metrics on the test sample (January 2018–March 2021) are presented in Table 2, with additional metrics from the confusion matrix in Table C.2.

A key observation is the disparity in performance between models, which depends on the composition of the explanatory variable dataset. Forecasting the 2018–2021 period was particularly challenging due to the inclusion of the Covid crisis, which caused currency collapses not directly linked to shifts in macroeconomic fundamentals and financial indicators. Models using the subset of explanatory variables demonstrated better out-of-sample performance, particularly in terms of the F1 score, whereas models with all variables showed stronger performance during validation and provided greater explanatory power given the diversity of crisis origins.

<footnote><sup>a</sup> The test sample includes 28 currency crises to be detected. This column shows the number of crises detected by each model, i.e., for which at least one alert signal was sent during the alert window.</footnote>

<footnote><sup>b</sup> Percentage of emitted signals identified as false signals.</footnote>

<footnote><sup>c</sup> Percentage of false signals issued during the post-crisis period (12 months).</footnote>
Traditional models performed as expected. Logistic regression exhibited the poorest performance, likely due to the large number of indicators included. The decision tree performed slightly better but remained unstable, as indicated by validation results in Fig. 4. The random forest showed the highest out-of-sample performance among non-neural-network models, even surpassing LSTM in terms of F1 score when using the subset of explanatory variables, in line with the conclusions of Barthélémy et al. (2024). However, the observed instability during validation suggests that random forests may not be the most reliable choice for EWS implementation. SVM demonstrated stable validation performance comparable to neural networks, though its out-of-sample performance was modest, potentially affected by the unique challenges of the Covid period. Neural networks outperformed traditional models, consistent with the literature. LSTM networks achieved the highest F1 score for the specification using all indicators, highlighting their suitability for capturing sequential dynamics. CNNs, on the other hand, outperformed all models when using the subset of variables, achieving an out-of-sample F1 score of 74%. Using a subset is practical for generalization and real-time implementation, justifying slightly lower validation performance. Consequently, the model combining the CNN and LSTM layers appears to be a good compromise, offering more stable performance regardless of the dataset composition.

Performance assessment based on the F1 score should be considered alongside the number of crises detected, i.e., cases for which at least one signal was emitted during the alert period. The sixth column of Table 2 displays the number of crises detected by each model out of the 28 crises in the test sample. All models detected at least 20 of the 28 crises in the test sample, except logistic regression and SVM using all explanatory variables. One of the decision tree specifications identified 27 crises but generated noisy signals (73% false positives) and a low F1 score (42%), rendering it unsuitable for EWS. Models involving convolutional layers (CNNs and CNN-LSTM networks) emerged as the more robust candidates, striking an optimal balance between performance based on F1 score and the number of crises detected, achieving 24 detections for the best specifications. Note that for the two best specifications (CNN and CNN-LSTM using a subset of variables), nearly half of the false signals occurred during post-crisis periods, reflecting post-crisis bias. While this remains a limitation, it is preferable that false signals occur in post-crisis periods rather than tranquil periods.

## 5.2. Global explanation

As with traditional models, it is possible to identify the variables that exert the greatest influence on neural network predictions, as well as the magnitude and direction of their impact. This task was accomplished using Shapley values from game theory, a concept introduced by Shapley (1953), further developed by Lundberg and Lee (2017) and implemented through the SHAP Python package. Lundberg and Lee's SHAP framework allows identification of the main determinants of a prediction for any type of model, thus facilitating comparison across methods. Despite their predictive power, deep learning models are notoriously difficult to interpret due to their complex architecture, which involves multiple parameters and several hidden layers including activation functions (bringing non-linearity). The use of SHAP values mitigates this limitation by pinpointing the key determinants of predicted crises, thereby providing insights that could help policymakers implement appropriate measures.

The base value is an essential concept in the Shapley framework. In game theory, it corresponds to the average payoff derived from all possible coalitions of players. Analogously, in econometric, machine learning, or deep learning models, the base value represents the average predicted probability across the entire sample, calculated from the average contribution of each explanatory variable. Consequently, the SHAP value associated with a variable for a given observation measures the difference between that variable's contribution to the model's prediction and its average contribution across all observations. The model's predicted probability for a specific observation is therefore equal to the base value plus the sum of SHAP values for all explanatory variables.

There have been many waves of financial crises over the years. By examining the main ones within our sample, we reconstructed the list of key determinants specific to each episode. Five distinct periods of financial stress and crisis were selected to test and compare the explanatory power of the models:

* *The Asian financial crisis.* This crisis struck unexpectedly and severely, affecting several East Asian countries that had previously been celebrated for their rapid growth and improved living standards. These nations' success had led foreign investors to underestimate their economic weaknesses, with large-scale financial inflows placing heavy demands on policies and institutions that failed to keep pace. Contributing factors included overheating pressures, unsustainable exchange rate pegs, inadequate supervision, and governance weaknesses. To address the economic damage, the affected countries implemented corrective measures, and the IMF provided $36 billion in support of reform programs in Indonesia, Korea, and Thailand. However, initial hesitations in introducing reforms exacerbated the crisis, leading to currency and stock market declines beyond what fundamentals could justify and contributing to difficulties in the corporate and financial sectors. The Asian crisis highlights how weak fundamentals and shifts in investor risk perception can jointly fuel currency collapses.

* *The early 2000s recession.* This period was characterized by a global economic downturn driven by various factors. The burst of the dot-com bubble in the early 2000s led to a sharp decline in equity markets, especially in the technology sector, contributing to a slowdown in economic growth. Additionally, the aftermath of the terrorist attacks of September 11, 2001, heightened geopolitical uncertainty, further undermining investor confidence and economic activity. The manufacturing sector faced significant challenges, and many countries experienced recessions. Central banks implemented monetary policy measures to stimulate their economies, notably lowering interest rates to encourage spending. In foreign exchange markets, the US dollar strengthened against other currencies during this period, partly due to its status as a safe-haven currency amid global uncertainty. This episode of tension occurred shortly after the Asian crisis, leaving a lasting mark on investor risk aversion and the economic health of several countries.
<figure><img src="image_8.png" /><figcaption>Fig. 5. Main determinants of CNN predictions over time.</figcaption></figure>

* *The Global financial crisis.* The origins of this crisis lie in a downturn in the US housing market. Prior to the crisis, favorable economic conditions, including strong growth and low inflation, encouraged imprudent borrowing and speculative investments in real estate. In the United States, rising house prices fueled excessive borrowing, particularly by subprime borrowers and investors seeking short-term profits. Around the world, banks borrowed heavily to expand lending and invest in mortgage-backed securities (MBS). As US house prices fell, borrowers defaulted, causing significant losses for banks and investors alike. The crisis intensified in mid-2007 when financial institutions faced difficulties selling MBS, triggering a domino effect. Foreign banks' involvement in the US housing market and the interconnected nature of the global financial system amplified the crisis. The collapse of Lehman Brothers in September 2008 further deepened financial tensions, leading to global panic, widespread bank withdrawals, and market dysfunction. The Global Financial Crisis began as a banking crisis but spread worldwide, both through financial channels and via heightened perceptions of risk, with significant repercussions on foreign exchange markets.

* *Tensions of 2014–2016.* This period was marked by a complex interplay of global economic challenges that reverberated across multiple areas of the financial landscape. A sharp decline in commodity prices, particularly oil, had a negative impact on resource-dependent economies, contributing to heightened market volatility. At the same time, China's economic slowdown added to global financial stress, affecting trade and commodity markets. The anticipation of US Federal Reserve monetary tightening, symbolized by the first interest rate hike in nearly a decade in December 2015, created additional turbulence. Emerging markets, burdened by high levels of debt and capital outflows, faced particular strain. This combination of factors underscored the interconnectedness of the global economy and affected currencies, stock markets, and financial stability. Exchange rates were notably influenced by falling commodity prices, capital outflows from emerging markets, and policy divergences among major central banks, which collectively contributed to the depreciation of many currencies against the US dollar. This period differed from past crises in the heterogeneity of its sources.

* *The Covid crisis.* Triggered by the global spread of the coronavirus in 2019 and 2020, this crisis led to unprecedented health, social, and economic disruptions. The pandemic prompted widespread lockdowns, supply chain breakdowns, and a sharp global economic contraction, resulting in job losses and business closures. It also exposed vulnerabilities in healthcare systems, social safety nets, and economic structures. As governments implemented lockdown measures, financial markets experienced extreme volatility and uncertainty. In foreign exchange markets, the crisis manifested in abrupt currency movements. Heightened uncertainty surrounding the pandemic led to increased demand for safe-haven currencies such as the US dollar, causing it to strengthen significantly. Meanwhile, concerns about the pandemic's economic impact led to currency depreciation in countries facing severe outbreaks or structural weaknesses. To mitigate the crisis's effects, many economies resorted to massive borrowing, which weakened fiscal positions and increased the risk of default. The sudden and unprecedented nature of this crisis fueled volatility in financial markets and sharp currency depreciations.

Fig. 5 shows the evolution of the main determinants based on SHAP values computed across the two-year validation samples (1992–2017) and the test sample (2020–2021). Results are presented for the CNN model incorporating all explanatory variables to avoid omitting potential crisis drivers. Tables C.3, C.4, and C.5 rank the six most influential variables for the CNN, the LSTM (its neural network benchmark), and logistic regression (the traditional benchmark), respectively.

Despite the model's complexity, the principal determinants include several variables previously identified by earlier generations of models, such as the unemployment rate, inflation, trade balance, and interest rate differential. The unemployment rate, central to second-generation crisis models, consistently appears among the strongest predictors of crises across all models (with a positive sign). The central bank policy rate, or its differential with the US rate, also recurs, suggesting its continued role as a defense mechanism against speculative attacks and/or that crisis periods are associated with periods of high inflation justifying a rapid rise in policy
<figure><img src="image_9.png" /><figcaption>Fig. 6. Main drivers of the signal sent by the CNN on the Colombian peso in March 2020.</figcaption></figure>

rates. Conversely, the second defense mechanism embedded in the exchange market pressure index, namely the liquidation of reserve assets to buy back the depreciating currency, does not appear among the key variables. As expected, US variables play an equally important, though ambivalent, role: inflation, policy rate, debt-to-GDP ratio, unemployment, domestic credit, and share prices all influence outcomes. Paradoxically, adverse US indicators sometimes coincide with USD appreciation, reflecting the dollar's dual role as both a domestic currency and a global safe haven. Finally, one of our pseudo-contagion indicators (the correlation between domestic stock prices and the MSCI Emerging Markets Index) emerges as a recurring factor. An increase in this correlation raises the probability of a crisis, underscoring a contagion channel linked to portfolio flow dependencies, as noted by Fratzscher (2002).

A subset of relevant variables thus appears consistently influential across models, although the precise ranking of the six most important varies by period. Based on the frieze shown in Fig. 5, different explanations can be advanced depending on the period. The Asian crisis was driven by financial interdependence (correlation between the domestic equity index and the MSCI Emerging Index) and investor distrust without any systematic deterioration in fundamentals (cumulative crisis variable over the previous 12 months), although the presence of the interest rate differential with the US and CPI volatility in the ranking also suggests a role for fundamentals in the collapse of currencies. In the case of the Global Financial Crisis, all the main determinants are linked to the evolution of both global (Brent) and US indicators, given that the crisis was triggered by the collapse of the US housing market and Lehman Brothers before spreading worldwide. The model highlights the well-documented causes of the 2008 financial crisis, namely, excessive credit issuance and a speculative bubble whose bursting spread globally through banking interconnections. Finally, with regard to the Covid crisis, fundamentals played a minor role compared with variables related to exchange rate dynamics and US public debt, implying a sudden change in risk perception toward most currencies without any shift in fundamentals, reinforced by the sharp rise in US public debt as a share of GDP. The appearance of the US import growth variable (with a negative sign) further illustrates how lockdowns disrupted trade and weakened externally dependent currencies.

### 5.3. Local explanation

SHAP values can also be used to explain individual predictions. Taking the Colombian peso at the onset of its currency crisis during the Covid period (March 2020) as an example, it was possible to generate a heatmap highlighting the areas that prompted the CNN to issue a warning signal. Fig. 6 shows the contributions of current (February 2020) and past values (up to 8 months prior) to the issuance of a warning signal in February 2020, one month before the Colombian peso's collapse. The base value stands at 0.28, while the probability predicted by the model for this date is 0.6. The sum of the contributions of the variables over the 9-month period, together with the base value, corresponds to the predicted probability. In this representation, red areas indicate an increase in predicted probability, whereas blue areas indicate a decrease.

According to Fig. 6, the outbreak of the crisis cannot be attributed to a single variable; rather, it resulted from the simultaneous deterioration of multiple indicators, as discussed in the previous section. The recent evolution of the exchange rate (1), US foreign trade variables (2), global indicators related to MSCI indexes and the VIX (3,4), and domestic indicators related to public debt and credit (5) all contributed to the issuance of a warning signal. The Covid crisis affected a large number of currencies, distorting risk perceptions and causing many currencies to collapse, particularly in the presence of fragile fundamentals such as high credit issuance and elevated public debt.

## 6. Conclusions and future work

This study examined the implementation of an early warning system for currency crises over a two-year horizon across 60 countries, including developed, emerging, and developing economies, from 1992 to 2021. Its objective was to assess the contribution of advanced neural networks, particularly convolutional neural networks, to the design of effective EWS frameworks. Originally developed for image analysis, CNNs offer an innovative way to capture complex, multidimensional patterns in macro-financial data while remaining interpretable through techniques such as SHAP values. The construction and analysis of macro-financial “images” highlight the critical role of the simultaneous deterioration of multiple indicators in amplifying vulnerability to currency risk.

For the first time, this study tested both a traditional CNN and a hybrid CNN-LSTM architecture to establish an EWS for currency crises across multiple economies simultaneously. All tested models achieved satisfactory predictive performance, suggesting that
calibration on historical data provides a credible basis for forecasting future crises. Among them, models incorporating convolutional layers proved the most reliable, combining robustness and consistency. The hybrid CNN-LSTM model, in particular, leveraged the spatial feature extraction capacity of CNNs and the temporal memory of LSTMs to deliver strong and stable performance. Even under the disruptive conditions of the Covid pandemic, an event characterized by exceptional uncertainty and global contagion, these convolution-based models successfully detected 24 of the 28 crises in the test sample, while maintaining a low rate of false or noisy alerts, as reflected in their F1 scores.

Beyond their predictive power, the results demonstrate that neural networks, despite their complexity, can yield interpretable insights consistent with established economic theory. By analyzing periods from the Asian financial crisis to the Covid crisis, this study highlights the combined influence of domestic vulnerabilities (such as excessive debt accumulation, credit expansion, or high unemployment) and global conditions (such as the collapse of the US financial market and excessive credit issuance in the US in 2008, and the increase in risk perception, the fall in international trade and domestic activity during the Covid period). While the traditional literature has primarily emphasized domestic fundamentals as triggers of currency crises, our findings underscore the growing importance of global factors. Deterioration in these international indicators can magnify trade disruptions and contagion effects, precipitating currency collapses even in economies that appear resilient based on domestic fundamentals alone.

In sum, this study extends the literature on early warning systems by introducing convolution-based deep learning architectures to currency-crisis prediction and demonstrating their interpretability and robustness in a complex global environment. Future research could explore refinements in model architecture, incorporate higher-frequency or real-time financial data, and examine the transmission mechanisms of crises across countries. These developments could further enhance the practical value of early warning systems as decision-support tools for policymakers seeking to mitigate the impact of future financial shocks.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Funding sources

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

## Appendix A. Deep learning

### A.1. Recurrent neural networks

The LSTM introduced by Hochreiter and Schmidhuber (1997) and the GRU, a simplified variant proposed by Cho et al. (2014), are sophisticated recurrent neural networks. They incorporate a gating mechanism involving a memory vector and an additive gradient to address the vanishing gradient problem, a key limitation of standard RNNs. When the network weights are very small (less than one), their successive multiplication at each time step causes the gradient to decrease exponentially until it effectively reaches zero. Conversely, when the weights are excessively large, the gradient can explode. Both phenomena impede learning by preventing the network from updating its parameters properly. As the gradient decreases, parameter updates become negligible, leading the model to retain only information from the near past.

All recurrent networks are structured as a chain of identical cells. In a standard RNN, each cell is relatively simple: two inputs are required to compute the output, as illustrated in Fig. A.1. These are the current input variables ($x_t$) and the previous hidden state ($h_{t-1}$), which corresponds to the last cell output. These components are weighted, summed, and passed through an activation function (typically tanh). The process is slightly more complex in LSTM and GRU architectures. In a LSTM cell, three inputs are used: the current explanatory variables ($x_t$), the output of the cell from the previous time step (i.e., the previous hidden state, $h_{t-1}$), the latter corresponding to the short-term memory, and the long-term memory ($C_t$), which captures persistent dependencies among model variables. The long-term memory is updated through the gating mechanism, which also exists in the GRU, albeit in a simplified form.

LSTM and GRU cells aim to preserve relevant past information while discarding what is no longer useful, through a memory vector known as the cell state. The strength of these networks lies in this mechanism: the cell state passes through all cells in the sequence with only minor transformations (represented by the horizontal line across the top of the LSTM and GRU diagrams in Fig. A.1). This allows information from distant time steps to be stored in an almost unaltered form. Information is added to or removed from the memory vector via the gate control system. The LSTM contains three gates: the forget gate, input gate, and output gate. The forget gate determines which information from the previous long-term memory ($C_{t-1}$) should be discarded based on the new inputs ($x_t$ and $h_{t-1}$). The input gate identifies the new relevant information from the explanatory variables ($x_t$) and the short-term memory ($h_{t-1}$) that should be stored in the updated long-term memory ($C_t$). Together, these two gates update the cell state from $C_{t-1}$ to $C_t$. Finally, the output gate filters the available information ($x_t$, $h_{t-1}$, $C_t$) to produce the cell output ($h_t$), which then becomes the short-term memory for the next time step. The GRU serves as a compromise between standard RNNs and LSTM networks. It mitigates the vanishing gradient problem through a gating mechanism but retains only a single memory vector, without distinguishing between short- and long-term memory. The GRU includes two gates: the reset gate and the update gate. The reset gate controls how much of the previous hidden state ($h_{t-1}$) should be combined with the current input ($x_t$) to produce a candidate
<figure><img src="image_10.png" /><figcaption>Fig. A.1. Standard RNN, LSTM and GRU cells.</figcaption></figure>

hidden state ($h_t'$). The update gate then determines how much of the previous hidden state ($h_{t-1}$) and the candidate state ($h'_t$) should be retained to form the final hidden state ($h_t$). Consequently, the network can capture long-term dependencies whenever the update gate preserves a substantial portion of the previous hidden state.

The efficacy of these networks hinges on the efficiency of the filters applied to the data and their associated weights. During the learning phase, optimal weights are determined through backpropagation to enhance predictive performance. The removal and addition of information in the cell state, as well as the cell output, result from successive filtering processes performed by the activation functions of each gate.

## A.2. Convolutional neural networks

Networks that include one or more convolutional layers are generally comparable to feed-forward networks, in which information flows unidirectionally. The development of CNNs trained using gradient descent is attributed to Le Cun et al. (1990). Originally designed for image recognition due to their input structure, CNNs can also be applied to time series data when such data are transformed into an image-like format, aligning multiple time series in a structured grid. In their simplest form, CNN architectures consist of two main stages, as illustrated in Fig. A.2. The first stage corresponds to feature extraction, carried out through convolutional layers, while the second stage involves classification, performed by fully-connected feed-forward layers.

The architecture shown in Fig. A.2 takes as input three-dimensional data defined by height, width, and depth (with a depth of 1 for grayscale images and 3 for color images under the RGB convention). Assuming the input is a grayscale image, a filter (or kernel) of predefined size, for instance with a height and width of 3 (and a depth of 1), scans the image from left to right and top to bottom. At each position, it multiplies the underlying pixel values by the corresponding filter weights to produce a feature map, a representation of the image emphasizing particular patterns useful for prediction. Convolutional neural networks learn multiple filters to detect different features, such as edges, vertical lines or other geometric shapes deemed relevant by the model. This operation is called convolution. Because the structures to be detected can be complex, a single convolution step may be insufficient. To introduce non-linearity and improve representational capacity, an activation function such as the ReLU function is typically applied. Each element of every feature map passes through the activation function, producing activated feature maps. Next, a pooling operation is usually performed to reduce the spatial dimensions of the activated feature maps and the total number of parameters, thereby accelerating training and limiting overfitting. Pooling layers downsample each feature map independently, reducing its height and width while preserving depth. The most common pooling method is max pooling, which retains the maximum value within each pooling window. In summary, each input image successively passes through several convolutional and pooling filters to generate new images called feature maps. These are then concatenated to form a CNN code, which serves as the input to the classification stage. The entire process is illustrated in Fig. A.3, assuming a grayscale input image.

The classification stage is generally based on one or more fully-connected layers. The input data are the pooled-activated feature maps resulting from the preceding feature extraction stage, which are then flattened. This flattening process is necessary to transform
<figure><img src="image_11.png" /><figcaption>Fig. A.2. Convolutional neural network operation.</figcaption></figure>

<figure><img src="image_12.png" /><figcaption>Fig. A.3. Convolution and pooling operations.</figcaption></figure>

the multi-multidimensional output of the convolution/pooling layers into a one-dimensional vector (CNN code), a format that can be understood by fully-connected layers. Each element of this vector is multiplied by a corresponding set of weights linked to the neurons of the next layer and then passed through an activation function. The final layer produces an output vector whose size corresponds to the number of classes (in classification tasks), with each element representing the probability that the input image belongs to a given class.

During the training phase, the weights related to the convolution filters and classification network are optimized to reduce the loss function according to the backpropagation algorithm.

### A.3. Architectures

See Fig. A.4.
<figure><img src="image_13.png" /><figcaption>Fig. A.4. LSTM, CNN, and CNN-LSTM architectures.</figcaption></figure>
<table><caption>**Table B.1**
UNCTAD country classification.</caption><thead><tr><th>Regions<sup>a</sup></th><th>Countries</th></tr></thead><tbody><tr><td>Africa</td><td>Egypt, Mauritius, Namibia, Nigeria, South Africa, Swaziland, Tanzania, Tunisia, Uganda, and Zambia.</td></tr><tr><td>Americas</td><td>Brazil, Canada, Chile, Colombia, Costa Rica, Guyana, Jamaica, Mexico, Peru, and Trinidad and Tobago.</td></tr><tr><td>Asia</td><td>China, India, Indonesia, Israel, Japan, Jordan, Kuwait, Malaysia, Mongolia, Nepal, Philippines, Singapore, South Korea, Thailand, Turkey, and Vietnam.</td></tr><tr><td>Europe</td><td>Bulgaria, Czechia, Denmark, Eurozone, Finland, France, Germany, Hungary, Iceland, Ireland, Italy, Netherlands, Poland, Portugal, Romania, Russia, Slovakia, Slovenia, Spain, Switzerland, and United Kingdom.</td></tr><tr><td>Oceania</td><td>Australia, New Zealand, and Papua New Guinea.</td></tr></tbody></table>

<table><caption>**Table B.2**
Initial set of variables.</caption><thead><tr><th>Category</th><th>Feature</th><th>Source</th></tr></thead><tbody><tr><td rowspan="20">Exchange rates</td><td>Nominal exchange rate: level</td><td>IMF IFS, Datastream</td></tr><tr><td>Nominal exchange rate: average level (3, 6, 12, 18, 24 months)</td><td>Calculated variable</td></tr><tr><td>Nominal exchange rate: volatility (3, 6, 12, 18, 24 months)</td><td>Calculated variable</td></tr><tr><td>Nominal exchange rate: moving average convergence/divergence (3–6 m, 6–12 m, 12–24 m)</td><td>Calculated variable</td></tr><tr><td>Nominal exchange rate: moving average convergence/divergence signal</td><td>Calculated variable</td></tr><tr><td>Nominal exchange rate: relative strength index score</td><td>Calculated variable</td></tr><tr><td>Nominal exchange rate: relative strength index upper and lower bounds (dummies)</td><td>Calculated variable</td></tr><tr><td>Nominal exchange rate: upper and lower Bollinger bands</td><td>Calculated variable</td></tr><tr><td>Nominal exchange rate: Bollinger band width</td><td>Calculated variable</td></tr><tr><td>Nominal exchange rate: m/m growth</td><td>Calculated variable</td></tr><tr><td>Nominal exchange rate: y/y growth</td><td>Calculated variable</td></tr><tr><td>Nominal exchange rate: growth differential with previous year</td><td>Calculated variable</td></tr><tr><td>Nominal exchange rate: Frankel and Rose criterion</td><td>Calculated variable</td></tr><tr><td>Real exchange rate: volatility (24 months)</td><td>Calculated variable</td></tr><tr><td>Real exchange rate: m/m growth</td><td>Calculated variable</td></tr><tr><td>Real exchange rate: y/y growth</td><td>Calculated variable</td></tr><tr><td>Real exchange rate: overvaluation</td><td>Calculated variable (Hodrick–Prescott filter)</td></tr><tr><td rowspan="5">External trade</td><td>International reserves (m/m and y/y growth)<sup>a</sup></td><td>IMF IFS</td></tr><tr><td>Terms of trade (level and y/y growth)<sup>a</sup></td><td>IMF IFS, World Bank, OECD</td></tr><tr><td>Exports (y/y growth)<sup>a</sup></td><td>IMF IFS, World Bank</td></tr><tr><td>Imports (y/y growth)<sup>a</sup></td><td>IMF IFS, World Bank</td></tr><tr><td>Current account to GDP (level and y/y growth)<sup>a</sup></td><td>IMF IFS, World Bank, Datastream, Gourinchas &amp; Obstfeld database</td></tr><tr><td rowspan="5">Activity and debt</td><td>Chinn–Ito index</td><td>Chinn–Ito database</td></tr><tr><td>Public debt to GDP (level and y/y growth)<sup>a</sup></td><td>World Bank, Eurostat (EUZ)</td></tr><tr><td>Real GDP (y/y growth)<sup>a</sup></td><td>IMF IFS, World Bank, Datastream</td></tr><tr><td>Output gap<sup>a</sup></td><td>Calculated variable (Hodrick–Prescott filter)</td></tr><tr><td>Nominal GDP (y/y growth)<sup>a</sup></td><td>IMF IFS, World Bank, Datastream, Gourinchas &amp; Obstfeld database</td></tr><tr><td rowspan="2">Interest rate</td><td>Unemployment rate<sup>a</sup></td><td>IMF IFS, World Bank</td></tr><tr><td>Central Bank policy rate<sup>a</sup></td><td>IMF IFS, Datastream, Gourinchas &amp; Obstfeld database</td></tr><tr><td rowspan="5">Money</td><td>Policy rate differential with the US</td><td>Calculated variable</td></tr><tr><td>Broad money to international reserves (level and y/y growth)<sup>a</sup></td><td>Calculated variable</td></tr><tr><td>Broad money (y/y growth)<sup>a</sup></td><td>IMF IFS, World Bank, Datastream</td></tr><tr><td>Domestic credit to GDP (level and y/y growth)<sup>a</sup></td><td>IMF IFS, World Bank, Datastream, Gourinchas &amp; Obstfeld database</td></tr><tr><td>Inflation rate<sup>a</sup></td><td>IMF IFS, World Bank, Datastream</td></tr><tr><td rowspan="3">Oil price</td><td>Inflation differential with the US</td><td>Calculated variable</td></tr><tr><td>CPI volatility<sup>a</sup></td><td>Calculated variable</td></tr><tr><td>Brent (level, y/y growth, volatility)</td><td>Datastream</td></tr></tbody></table>

(continued on next page)

<footnote><sup>a</sup> United Nations Conference on Trade and Development (UNCTAD) regions.</footnote>
<table><caption>**Table B.2 (continued).**</caption><tbody><tr><td rowspan="10">Financial markets</td><td>VIX</td><td>Datastream</td></tr><tr><td>Share price (y/y growth)<sup>a</sup></td><td>OECD, Datastream</td></tr><tr><td>MSCI Pacific index (y/y growth and volatility)</td><td>Datastream</td></tr><tr><td>MSCI Europe index (y/y growth and volatility)</td><td>Datastream</td></tr><tr><td>MSCI Emerging index (y/y growth and volatility)</td><td>Datastream</td></tr><tr><td>MSCI Developed index (y/y growth and volatility)</td><td>Datastream</td></tr><tr><td>MSCI World index (y/y growth and volatility)</td><td>Datastream</td></tr><tr><td>Contagion: exchange rate/share price correlation</td><td>Calculated variable</td></tr><tr><td>Contagion: share price/US share price correlation</td><td>Calculated variable</td></tr><tr><td>Contagion: share price/MSCI correlation</td><td>Calculated variable</td></tr><tr><td>Contagion: share price/VIX correlation</td><td>Calculated variable</td></tr><tr><td>Contagion: exchange rate/VIX correlation</td><td>Calculated variable</td></tr><tr><td rowspan="7">Other forms of contagion</td><td>Level of development (3-level categorical variable)</td><td>IMF</td></tr><tr><td>Region (5-level categorical variable)</td><td>World Economic Outlook</td></tr><tr><td>Cumulative currency crises over the last 12 months (all countries combined)</td><td>Calculated variable</td></tr><tr><td>Cumulative currency crises over the last 24 months (all countries combined)</td><td>Calculated variable</td></tr><tr><td>Cumulative currency crises over the last 12 months (by region)</td><td>Calculated variable</td></tr><tr><td>Cumulative currency crises over the last 12 months (by level of development)</td><td>Calculated variable</td></tr><tr><td>Cumulative currency crises over the history of the country (by country)</td><td>Calculated variable</td></tr></tbody></table>

a Indicators included for the countries or monetary unions under review and the United States (USD as base currency).

The real exchange rate overvaluation and the output gap are expressed as percentage of their trend, obtained using the Hodrick and Prescott filter (one-sided) with a lambda of 14400 for monthly series and 1600 for quarterly series.

See Chinn and Ito (2006) and Gourinchas and Obstfeld (2011) to access the mentioned databases.

<table><caption>**Table B.3**
Subset of variables.</caption><thead><tr><td>Category</td><td>Features</td></tr></thead><tbody><tr><td>Exchange rates</td><td>Nominal exchange rate (y/y and m/m growth) and overvaluation of the real exchange rate.</td></tr><tr><td>External trade</td><td>International reserves (y/y and m/m growth), current account to GDP (y/y growth), exports (y/y growth), and imports (y/y growth).</td></tr><tr><td>Activity and debt</td><td>Public debt to GDP (level, y/y growth),<sup>a</sup> output gap, and unemployment rate.</td></tr><tr><td>Interest rate</td><td>Central Bank policy rate.<sup>a</sup></td></tr><tr><td>Money</td><td>Domestic credit to GDP (level, y/y growth),<sup>a</sup> inflation rate, and inflation differential with the US.</td></tr><tr><td>Oil price/Commodity shocks</td><td>Brent (level).</td></tr><tr><td>Financial markets</td><td>US share price (y/y growth), MSCI Pacific Index volatility, exchange rate/share price correlation, share price/MSCI Emerging Market Index correlation, and share price/VIX correlation.</td></tr><tr><td>Other forms of contagion</td><td>Cumulative crises over the history of the country (by country), cumulative currency crises over the last 12 months (low-income developing economies), and cumulative currency crises over the last 12 months (all countries combined).</td></tr></tbody></table>

a Indicators included for the countries or monetary unions under review and the United States (USD as base currency).

## Appendix B. Database

Multicollinearity and endogeneity are well-known challenges in linear econometric models, where correlated regressors can lead to unstable or biased coefficient estimates. In theory, deep learning architectures such as convolutional neural networks are less affected by these issues because they do not rely on fixed coefficient estimation. Instead, they learn non-linear data representations by allocating adaptive weights across neurons. This flexibility enables the network to distribute importance among correlated inputs or to suppress redundant signals when they contribute little additional information. Regularization techniques, including L1/L2 penalties, dropout, and batch normalization, further constrain weight magnitudes and help prevent overfitting caused by feature redundancy. Empirically, our results confirm that filtering out highly correlated indicators improves both generalization and predictive accuracy. This suggests that even within a deep learning framework, where theoretical robustness to multicollinearity exists, the exclusion of redundant variables enhances model efficiency and interpretability.

Regarding endogeneity and autocorrelation, our methodological design effectively mitigates these concerns. Endogeneity is less problematic in a forecasting context because the model is trained solely on information available prior to the crisis window. Autocorrelation is naturally captured through the use of lagged input windows, allowing the network to learn temporal dependencies without violating the chronological order of observations. Moreover, the expanding-window cross-validation procedure prevents any leakage of future information into the training sets, ensuring that predictive performance is evaluated under realistic out-of-sample forecasting conditions.

In this study, currency crises were identified by combining two criteria focused on the extent of depreciation. A crisis is considered to occur when the annual nominal depreciation against the US dollar exceeds 20% and is at least 10% greater than the depreciation observed in the previous year, if a depreciation had already taken place. This definition is inspired by Frankel and Rose (1996),
<table><caption>**Table B.4**

Descriptive statistics.</caption><thead><tr><th></th><th>count</th><th>mean</th><th>std</th><th>min</th><th>25%</th><th>50%</th><th>75%</th><th>max</th></tr></thead><tbody><tr><td>Exchange rate (level)</td><td>16169</td><td>518,0</td><td>2393,3</td><td>0,0</td><td>1,2</td><td>4,8</td><td>65,3</td><td>23256,5</td></tr><tr><td>Mean exchange rate (3 months)</td><td>16169</td><td>516,7</td><td>2388,3</td><td>0,0</td><td>1,2</td><td>4,8</td><td>65,2</td><td>23244,6</td></tr><tr><td>Exchange rate volatility (3 months)</td><td>16169</td><td>6,0</td><td>38,1</td><td>0,0</td><td>0,0</td><td>0,0</td><td>0,6</td><td>1295,3</td></tr><tr><td>Mean exchange rate (6 months)</td><td>16169</td><td>514,6</td><td>2380,1</td><td>0,0</td><td>1,2</td><td>4,8</td><td>65,1</td><td>23233,0</td></tr><tr><td>Exchange rate volatility (6 months)</td><td>16169</td><td>9,3</td><td>51,7</td><td>0,0</td><td>0,0</td><td>0,1</td><td>0,9</td><td>1153,8</td></tr><tr><td>MACD (3-6 months)</td><td>16169</td><td>2,1</td><td>25,1</td><td>-343,8</td><td>0,0</td><td>0,0</td><td>0,1</td><td>598,7</td></tr><tr><td>Mean exchange rate (12 months)</td><td>16169</td><td>510,3</td><td>2363,1</td><td>0,0</td><td>1,2</td><td>4,7</td><td>65,2</td><td>23209,7</td></tr><tr><td>Exchange rate volatility (12 months)</td><td>16169</td><td>14,2</td><td>70,1</td><td>0,0</td><td>0,0</td><td>0,1</td><td>1,5</td><td>1192,5</td></tr><tr><td>MACD (6-12 months)</td><td>16169</td><td>4,1</td><td>35,4</td><td>-282,6</td><td>0,0</td><td>0,0</td><td>0,2</td><td>682,6</td></tr><tr><td>Mean exchange rate (18 months)</td><td>16169</td><td>506,2</td><td>2346,2</td><td>0,0</td><td>1,2</td><td>4,7</td><td>65,3</td><td>23186,4</td></tr><tr><td>Exchange rate volatility (18 months)</td><td>16169</td><td>18,6</td><td>85,9</td><td>0,0</td><td>0,0</td><td>0,2</td><td>2,0</td><td>1275,8</td></tr><tr><td>Mean exchange rate (24 months)</td><td>16169</td><td>502,4</td><td>2329,8</td><td>0,0</td><td>1,3</td><td>4,8</td><td>65,7</td><td>23141,2</td></tr><tr><td>Exchange rate volatility (24 months)</td><td>16169</td><td>22,9</td><td>103,7</td><td>0,0</td><td>0,0</td><td>0,2</td><td>2,4</td><td>1983,6</td></tr><tr><td>Real exchange rate volatility (24 months)</td><td>16169</td><td>0,3</td><td>1,6</td><td>0,0</td><td>0,0</td><td>0,0</td><td>0,0</td><td>65,0</td></tr><tr><td>MACD (12-24 months)</td><td>16169</td><td>8,0</td><td>54,0</td><td>-379,5</td><td>0,0</td><td>0,0</td><td>0,4</td><td>803,7</td></tr><tr><td>Relative Strength Index score</td><td>16169</td><td>55,7</td><td>19,2</td><td>0,3</td><td>43,6</td><td>54,7</td><td>66,0</td><td>100,0</td></tr><tr><td>Upper bollinger band</td><td>16169</td><td>548,1</td><td>2475,3</td><td>0,0</td><td>1,4</td><td>5,5</td><td>72,3</td><td>23421,2</td></tr><tr><td>Lower bollinger band</td><td>16169</td><td>456,6</td><td>2194,2</td><td>-1083,9</td><td>1,1</td><td>4,0</td><td>57,9</td><td>22954,9</td></tr><tr><td>Bollinger bands width</td><td>16169</td><td>91,5</td><td>414,6</td><td>0,0</td><td>0,2</td><td>0,9</td><td>9,7</td><td>7934,6</td></tr><tr><td>Exchange rate growth (m/m %)</td><td>16169</td><td>0,2</td><td>2,7</td><td>-18,1</td><td>-0,9</td><td>0,0</td><td>1,2</td><td>78,5</td></tr><tr><td>Exchange rate growth (y/y %)</td><td>16169</td><td>3,5</td><td>14,6</td><td>-32,7</td><td>-4,1</td><td>1,0</td><td>8,3</td><td>237,8</td></tr><tr><td>Real exchange rate growth (m/m %)</td><td>16169</td><td>-0,1</td><td>2,6</td><td>-19,5</td><td>-1,4</td><td>-0,3</td><td>0,9</td><td>70,3</td></tr><tr><td>Real exchange rate growth (y/y %)</td><td>16169</td><td>-1,3</td><td>10,9</td><td>-37,6</td><td>-7,7</td><td>-2,2</td><td>3,6</td><td>92,8</td></tr><tr><td>FX reserves growth (m/m %)</td><td>16169</td><td>0,9</td><td>7,4</td><td>-99,4</td><td>-1,4</td><td>0,4</td><td>2,6</td><td>149,6</td></tr><tr><td>FX reserves growth (y/y %)</td><td>16169</td><td>11,8</td><td>41,7</td><td>-99,5</td><td>-2,7</td><td>6,0</td><td>19,4</td><td>2423,7</td></tr><tr><td>Terms of trade</td><td>16169</td><td>100,0</td><td>24,5</td><td>14,8</td><td>88,5</td><td>99,1</td><td>111,0</td><td>284,2</td></tr><tr><td>Terms of trade growth (y/y %)</td><td>16169</td><td>0,4</td><td>9,3</td><td>-44,8</td><td>-3,0</td><td>0,0</td><td>3,0</td><td>122,3</td></tr><tr><td>Current account growth (y/y %)</td><td>16169</td><td>13,0</td><td>700,1</td><td>-8517,3</td><td>-38,8</td><td>-0,5</td><td>40,6</td><td>12857,8</td></tr><tr><td>Current account to GDP</td><td>16169</td><td>0,0</td><td>7,7</td><td>-52,5</td><td>-3,8</td><td>-0,9</td><td>2,8</td><td>45,5</td></tr><tr><td>Current account to GDP growth (y/y %)</td><td>16169</td><td>8,5</td><td>631,0</td><td>-7624,2</td><td>-40,8</td><td>-5,1</td><td>29,5</td><td>10565,0</td></tr><tr><td>Public debt to GDP</td><td>16169</td><td>50,8</td><td>32,2</td><td>3,1</td><td>28,9</td><td>42,9</td><td>66,3</td><td>221,4</td></tr><tr><td>Public debt to GDP growth (y/y %)</td><td>16169</td><td>2,7</td><td>15,1</td><td>-67,0</td><td>-4,7</td><td>0,7</td><td>7,9</td><td>129,9</td></tr><tr><td>Real GDP growth (y/y %)</td><td>16169</td><td>3,4</td><td>5,7</td><td>-37,8</td><td>0,8</td><td>3,2</td><td>5,9</td><td>62,8</td></tr><tr><td>Output gap (% of trend)</td><td>16169</td><td>0,1</td><td>5,5</td><td>-70,7</td><td>-1,4</td><td>0,0</td><td>1,5</td><td>76,5</td></tr><tr><td>Central bank policy rate (%)</td><td>16169</td><td>6,4</td><td>9,6</td><td>-0,8</td><td>2,0</td><td>4,8</td><td>7,5</td><td>400,3</td></tr><tr><td>Fed policy rate (%)</td><td>16169</td><td>2,1</td><td>2,1</td><td>0,1</td><td>0,1</td><td>1,3</td><td>3,8</td><td>6,5</td></tr><tr><td>Policy rate differential with the US (pps)</td><td>16169</td><td>4,3</td><td>9,3</td><td>-6,5</td><td>0,1</td><td>2,4</td><td>5,9</td><td>394,8</td></tr><tr><td>Broad money growth (y/y %)</td><td>16169</td><td>11,1</td><td>12,7</td><td>-45,5</td><td>4,9</td><td>8,8</td><td>14,7</td><td>154,4</td></tr><tr><td>Domestic credit to GDP</td><td>16169</td><td>70,9</td><td>42,7</td><td>5,4</td><td>36,3</td><td>62,8</td><td>100,0</td><td>389,5</td></tr><tr><td>Domestic credit to GDP growth (y/y %)</td><td>16169</td><td>2,6</td><td>9,3</td><td>-60,3</td><td>-2,0</td><td>2,0</td><td>6,2</td><td>69,5</td></tr><tr><td>Inflation rate (%)</td><td>16169</td><td>4,9</td><td>8,2</td><td>-6,5</td><td>1,5</td><td>3,0</td><td>5,8</td><td>125,9</td></tr><tr><td>Inflation differential with the US (pps)</td><td>16169</td><td>2,8</td><td>8,1</td><td>-8,5</td><td>-0,7</td><td>0,8</td><td>3,7</td><td>123,1</td></tr><tr><td>CPI volatility</td><td>16169</td><td>2,6</td><td>2,7</td><td>0,0</td><td>1,0</td><td>1,8</td><td>3,1</td><td>29,4</td></tr><tr><td>Brent</td><td>16169</td><td>58,6</td><td>32,1</td><td>10,2</td><td>28,9</td><td>55,7</td><td>76,9</td><td>133,6</td></tr><tr><td>Brent growth (y/y %)</td><td>16169</td><td>8,3</td><td>35,7</td><td>-62,4</td><td>-14,5</td><td>3,1</td><td>32,4</td><td>168,7</td></tr><tr><td>Brent volatility</td><td>16169</td><td>1,9</td><td>1,4</td><td>0,1</td><td>1,0</td><td>1,6</td><td>2,4</td><td>10,9</td></tr><tr><td>VIX</td><td>16169</td><td>19,6</td><td>8,2</td><td>10,1</td><td>13,7</td><td>17,4</td><td>23,1</td><td>62,3</td></tr><tr><td>Share price/VIX cor.</td><td>16169</td><td>-0,3</td><td>0,5</td><td>-1,0</td><td>-0,7</td><td>-0,4</td><td>0,0</td><td>1,0</td></tr><tr><td>Exchange rate/VIX cor.</td><td>16169</td><td>0,1</td><td>0,5</td><td>-1,0</td><td>-0,3</td><td>0,1</td><td>0,6</td><td>1,0</td></tr><tr><td>Share price growth (y/y %)</td><td>16169</td><td>11,3</td><td>34,3</td><td>-90,5</td><td>-6,6</td><td>7,5</td><td>22,1</td><td>443,7</td></tr><tr><td>Exchange rate/share price cor.</td><td>16169</td><td>-0,1</td><td>0,6</td><td>-1,0</td><td>-0,7</td><td>-0,2</td><td>0,4</td><td>1,0</td></tr><tr><td>US share price growth (y/y %)</td><td>16169</td><td>6,5</td><td>14,8</td><td>-43,4</td><td>-2,2</td><td>9,0</td><td>15,6</td><td>41,1</td></tr><tr><td>share price/US share price cor.</td><td>16169</td><td>0,4</td><td>0,5</td><td>-1,0</td><td>0,1</td><td>0,6</td><td>0,9</td><td>1,0</td></tr><tr><td>MSCI Pacific Index growth (y/y %)</td><td>16169</td><td>3,2</td><td>19,0</td><td>-42,9</td><td>-9,3</td><td>3,3</td><td>13,9</td><td>62,1</td></tr><tr><td>MSCI EAFE Index growth (y/y %)</td><td>16169</td><td>3,9</td><td>17,5</td><td>-49,7</td><td>-7,3</td><td>5,6</td><td>16,3</td><td>54,9</td></tr><tr><td>MSCI Europe Index growth (y/y %)</td><td>16169</td><td>4,9</td><td>18,9</td><td>-53,1</td><td>-7,9</td><td>7,3</td><td>18,6</td><td>57,2</td></tr><tr><td>MSCI Emerging Markets Index growth (y/y %)</td><td>16169</td><td>8,0</td><td>26,2</td><td>-57,4</td><td>-9,8</td><td>6,3</td><td>22,2</td><td>84,6</td></tr><tr><td>MSCI World Index growth (y/y %)</td><td>16169</td><td>6,2</td><td>15,8</td><td>-46,1</td><td>-2,6</td><td>10,0</td><td>16,3</td><td>54,4</td></tr><tr><td>MSCI Pacific Index volatility</td><td>16169</td><td>39,3</td><td>22,6</td><td>7,5</td><td>23,4</td><td>34,0</td><td>48,0</td><td>205,4</td></tr><tr><td>MSCI EAFE Index volatility</td><td>16169</td><td>27,5</td><td>18,3</td><td>4,6</td><td>16,2</td><td>23,4</td><td>33,3</td><td>173,3</td></tr><tr><td>MSCI Europe Index volatility</td><td>16169</td><td>26,5</td><td>18,3</td><td>3,3</td><td>15,3</td><td>22,7</td><td>32,8</td><td>166,3</td></tr><tr><td>MSCI Emerging Markets Index volatility</td><td>16169</td><td>16,6</td><td>12,4</td><td>2,0</td><td>8,8</td><td>13,3</td><td>20,2</td><td>93,5</td></tr><tr><td>MSCI World Index volatility</td><td>16169</td><td>21,9</td><td>17,7</td><td>2,2</td><td>12,0</td><td>17,5</td><td>25,6</td><td>199,5</td></tr><tr><td>Share price/MSCI Pacific Index cor.</td><td>16169</td><td>0,4</td><td>0,5</td><td>-1,0</td><td>0,1</td><td>0,7</td><td>0,9</td><td>1,0</td></tr><tr><td>Share price/MSCI EAFE Index cor.</td><td>16169</td><td>0,5</td><td>0,5</td><td>-1,0</td><td>0,2</td><td>0,7</td><td>0,9</td><td>1,0</td></tr></tbody></table>

(continued on next page)
<table><caption>Table B.4 (continued).</caption><thead><tr><th></th><th>count</th><th>mean</th><th>std</th><th>min</th><th>25%</th><th>50%</th><th>75%</th><th>max</th></tr></thead><tbody><tr><td>Share price/MSCI Europe Index cor.</td><td>16169</td><td>0,4</td><td>0,5</td><td>-1,0</td><td>0,1</td><td>0,6</td><td>0,9</td><td>1,0</td></tr><tr><td>Share price/MSCI Emerging Market Index cor.</td><td>16169</td><td>0,5</td><td>0,5</td><td>-1,0</td><td>0,2</td><td>0,7</td><td>0,9</td><td>1,0</td></tr><tr><td>Share price/MSCI World Index cor.</td><td>16169</td><td>0,5</td><td>0,5</td><td>-1,0</td><td>0,2</td><td>0,7</td><td>0,9</td><td>1,0</td></tr><tr><td>Unemployment rate (%)</td><td>16169</td><td>8,3</td><td>5,3</td><td>0,4</td><td>4,6</td><td>7,3</td><td>10,4</td><td>34,0</td></tr><tr><td>GDP growth (y/y %)</td><td>16169</td><td>8,4</td><td>10,2</td><td>-31,1</td><td>3,5</td><td>6,8</td><td>11,5</td><td>139,5</td></tr><tr><td>Exports growth (y/y %)</td><td>16169</td><td>9,8</td><td>17,0</td><td>-50,1</td><td>1,5</td><td>7,9</td><td>16,0</td><td>204,9</td></tr><tr><td>Imports growth (y/y %)</td><td>16169</td><td>9,5</td><td>16,2</td><td>-55,9</td><td>1,5</td><td>8,1</td><td>16,2</td><td>139,7</td></tr><tr><td>US real GDP growth (y/y %)</td><td>16169</td><td>1,9</td><td>2,0</td><td>-8,3</td><td>1,3</td><td>2,2</td><td>3,1</td><td>5,0</td></tr><tr><td>US output gap (% of trend)</td><td>16169</td><td>0,0</td><td>1,2</td><td>-8,5</td><td>-0,5</td><td>0,1</td><td>0,7</td><td>3,1</td></tr><tr><td>US public debt to GDP</td><td>16169</td><td>68,5</td><td>21,9</td><td>40,7</td><td>45,6</td><td>65,0</td><td>86,9</td><td>119,4</td></tr><tr><td>US public debt to GDP growth (y/y %)</td><td>16169</td><td>4,1</td><td>8,4</td><td>-11,7</td><td>-0,4</td><td>2,3</td><td>5,4</td><td>28,2</td></tr><tr><td>US terms of trade</td><td>16169</td><td>78,2</td><td>7,3</td><td>64,2</td><td>72,3</td><td>80,1</td><td>82,4</td><td>93,9</td></tr><tr><td>US terms of trade growth (y/y %)</td><td>16169</td><td>-0,3</td><td>3,9</td><td>-6,9</td><td>-2,8</td><td>-0,7</td><td>1,8</td><td>10,8</td></tr><tr><td>US current account growth (y/y %)</td><td>16169</td><td>-18,9</td><td>228,6</td><td>-1910,1</td><td>-5,4</td><td>9,0</td><td>17,8</td><td>64,3</td></tr><tr><td>US current account to GDP</td><td>16169</td><td>-3,1</td><td>1,3</td><td>-5,9</td><td>-4,2</td><td>-2,6</td><td>-2,1</td><td>-0,8</td></tr><tr><td>US current account to GDP growth (y/y %)</td><td>16169</td><td>-21,8</td><td>216,1</td><td>-1809,5</td><td>-7,3</td><td>3,9</td><td>12,1</td><td>56,2</td></tr><tr><td>US FX reserves growth (y/y %)</td><td>16169</td><td>0,7</td><td>9,4</td><td>-33,0</td><td>-4,8</td><td>0,3</td><td>6,8</td><td>27,6</td></tr><tr><td>US FX reserves growth (m/m %)</td><td>16169</td><td>0,1</td><td>2,5</td><td>-15,6</td><td>-1,3</td><td>0,0</td><td>1,5</td><td>9,2</td></tr><tr><td>US broad money to reserves</td><td>16169</td><td>39,0</td><td>8,6</td><td>24,9</td><td>32,5</td><td>40,1</td><td>44,9</td><td>58,3</td></tr><tr><td>US broad money to reserves growth (y/y %)</td><td>16169</td><td>1,0</td><td>13,1</td><td>-23,2</td><td>-5,7</td><td>-1,8</td><td>8,6</td><td>33,8</td></tr><tr><td>US broad money growth (y/y %)</td><td>16169</td><td>6,3</td><td>3,9</td><td>-2,8</td><td>4,4</td><td>5,5</td><td>8,2</td><td>17,2</td></tr><tr><td>US domestic credit to GDP</td><td>16169</td><td>50,6</td><td>3,5</td><td>42,8</td><td>48,6</td><td>50,6</td><td>53,2</td><td>57,9</td></tr><tr><td>US domestic credit to GDP growth (y/y %)</td><td>16169</td><td>0,5</td><td>3,2</td><td>-9,0</td><td>-1,0</td><td>0,6</td><td>3,0</td><td>6,4</td></tr><tr><td>US CPI volatility</td><td>16169</td><td>1,3</td><td>0,4</td><td>0,6</td><td>1,1</td><td>1,2</td><td>1,5</td><td>2,7</td></tr><tr><td>US unemployment rate (%)</td><td>16169</td><td>6,0</td><td>1,9</td><td>3,3</td><td>4,5</td><td>5,4</td><td>7,0</td><td>14,4</td></tr><tr><td>US GDP growth (y/y %)</td><td>16169</td><td>4,1</td><td>2,4</td><td>-7,7</td><td>3,4</td><td>4,3</td><td>5,6</td><td>7,6</td></tr><tr><td>US exports growth (y/y %)</td><td>16169</td><td>4,4</td><td>9,1</td><td>-28,7</td><td>-0,1</td><td>5,2</td><td>11,2</td><td>19,6</td></tr><tr><td>US imports growth (y/y %)</td><td>16169</td><td>4,9</td><td>9,9</td><td>-29,9</td><td>0,1</td><td>6,6</td><td>12,0</td><td>25,7</td></tr><tr><td>US inflation rate</td><td>16169</td><td>2,1</td><td>1,2</td><td>-2,1</td><td>1,5</td><td>2,1</td><td>2,9</td><td>5,6</td></tr></tbody></table>

with the main distinction that the first threshold is set at 20%, rather than the original 25%. The 25% threshold established by Frankel and Rose was designed for crises that occurred prior to the Asian financial crisis, during a period when most currencies were not yet freely floating. Under such exchange rate regimes, deviations from equilibrium values were often larger, resulting in extreme depreciations once the parity was abandoned. Because more than two-thirds of our sample covers the post-2000 period, characterized by greater exchange rate flexibility, a more moderate 20% threshold is deemed appropriate. Depreciations exceeding 20% remain both infrequent and substantial, thus satisfying the conditions for classifying such episodes as crises. Fig. B.1 compares the crisis datings obtained in this study (for 60 countries from 1992M01 to 2021M03) using depreciation thresholds of 25% and 20%. Notably, applying the 25% threshold nearly excludes the tensions observed in 2012, a period marked by recessions and sharp slowdowns in several major economies, including Europe, the United States, and China. Using our adjusted criterion, we identified 169 crises across the sample, compared with 113 under the Frankel and Rose definition. Lowering the depreciation threshold is also justified by the use of monthly data, which allows for more precise crisis dating. Historically, when the 25% threshold was crossed, the crisis had typically begun several months earlier. For instance, during the Global Financial Crisis, the main wave of crisis onset occurred in December 2008 under the 20% criterion (12 crises), compared with January 2009 under the 25% threshold (8 crises). This adjustment improves the detection of the earliest affected currencies. Moreover, the revised threshold refines the classification of crisis causes. For example, under the original 25% threshold, the depreciations of the South African rand, Namibian dollar, and Swazi lilangeni were attributed to the outbreak of COVID-19 in 2020. However, these currencies had already experienced depreciations exceeding 20%, but below 25%, in 2019, consistent with our adjusted criterion (i.e., a depreciation at least 10% higher than in the preceding year when a depreciation had already occurred).

## Appendix C. Results

See Tables C.1–C.5.

## Appendix D. Supplementary data

Supplementary material related to this article can be found online at https://doi.org/10.1016/j.iref.2025.104789.

## Data availability

The data that support the findings of this study are available from Datastream, IMF International Financial Statistics, the Organisation for Economic Co-operation and Development, World Bank databases, the Gourinchas and Obstfeld database and the Chinn-Ito database. Restrictions apply to the availability of these data, which were used under license for this study. Data are available from the authors with permission from Datastream. The data and programs used for dating currency crises are accessible here.
<figure><img src="image_14.png" /><figcaption>Fig. B.1. Comparison of two depreciation thresholds for the crisis dating criterion.</figcaption></figure>
<figure><img src="image_15.png" /><figcaption>Fig. B.2. Empirical pre- and post-crisis regularities.</figcaption></figure>

Mean, median and quantiles of the indicators for all countries that experienced a currency crisis, over the two years preceding and following the currency collapse.
<figure><img src="image_16.png" /><figcaption>Fig. B.2. (continued).</figcaption></figure>
<figure><img src="image_17.png" /><figcaption>Fig. B.2. (continued).</figcaption></figure>

Table C.1

K-means clustering results for different post-crisis durations and number of clusters (k).

<table><thead><tr><th rowspan="2"></th><th rowspan="2"></th><th colspan="2">12 months post-crisis</th><th colspan="2">18 months post-crisis</th><th colspan="2">24 months post-crisis</th></tr><tr><th>cluster %</th><th>period %</th><th>cluster %</th><th>period %</th><th>cluster %</th><th>period %</th></tr></thead><tbody><tr><td rowspan="2">k = 2</td><td rowspan="2"></td><td>cluster 1</td><td>62%</td><td>30%</td><td>51%</td><td>27%</td><td>45%</td><td>26%</td></tr><tr><td>cluster 2</td><td>67%</td><td>70%</td><td>65%</td><td>73%</td><td>61%</td><td>74%</td></tr><tr><td rowspan="2">k = 2</td><td rowspan="2"></td><td>cluster 1</td><td>14%</td><td>19%</td><td>13%</td><td>19%</td><td>13%</td><td>19%</td></tr><tr><td>cluster 2</td><td>27%</td><td>81%</td><td>26%</td><td>81%</td><td>26%</td><td>81%</td></tr><tr><td rowspan="2">k = 2</td><td rowspan="2"></td><td>cluster 1</td><td>24%</td><td>64%</td><td>36%</td><td>65%</td><td>42%</td><td>60%</td></tr><tr><td>cluster 2</td><td>6%</td><td>36%</td><td>9%</td><td>35%</td><td>13%</td><td>40%</td></tr><tr><td rowspan="2">k = 3</td><td rowspan="2"></td><td>cluster 1</td><td>72%</td><td>57%</td><td>71%</td><td>60%</td><td>68%</td><td>61%</td></tr><tr><td>cluster 2</td><td>64%</td><td>8%</td><td>53%</td><td>8%</td><td>51%</td><td>8%</td></tr><tr><td rowspan="2">k = 3</td><td rowspan="2"></td><td>cluster 3</td><td>57%</td><td>35%</td><td>48%</td><td>32%</td><td>42%</td><td>31%</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="2">k = 3</td><td rowspan="2"></td><td>cluster 1</td><td>25%</td><td>56%</td><td>24%</td><td>56%</td><td>24%</td><td>56%</td></tr><tr><td>cluster 2</td><td>8%</td><td>3%</td><td>8%</td><td>3%</td><td>8%</td><td>3%</td></tr><tr><td rowspan="2">k = 3</td><td rowspan="2"></td><td>cluster 3</td><td>23%</td><td>41%</td><td>23%</td><td>41%</td><td>22%</td><td>41%</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="2">k = 3</td><td rowspan="2"></td><td>cluster 1</td><td>3%</td><td>14%</td><td>5%</td><td>14%</td><td>8%</td><td>19%</td></tr><tr><td>cluster 2</td><td>28%</td><td>20%</td><td>39%</td><td>19%</td><td>41%</td><td>16%</td></tr><tr><td rowspan="2">k = 3</td><td rowspan="2"></td><td>cluster 3</td><td>20%</td><td>66%</td><td>29%</td><td>67%</td><td>36%</td><td>65%</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="2">k = 4</td><td rowspan="2"></td><td>cluster 1</td><td>54%</td><td>24%</td><td>45%</td><td>22%</td><td>39%</td><td>21%</td></tr><tr><td>cluster 2</td><td>64%</td><td>9%</td><td>53%</td><td>8%</td><td>51%</td><td>8%</td></tr><tr><td rowspan="2">k = 4</td><td rowspan="2"></td><td>cluster 3</td><td>68%</td><td>41%</td><td>66%</td><td>43%</td><td>61%</td><td>43%</td></tr><tr><td>cluster 4</td><td>75%</td><td>26%</td><td>75%</td><td>27%</td><td>74%</td><td>28%</td></tr><tr><td rowspan="2">k = 4</td><td rowspan="2"></td><td>cluster 1</td><td>25%</td><td>32%</td><td>24%</td><td>32%</td><td>23%</td><td>32%</td></tr><tr><td>cluster 2</td><td>8%</td><td>3%</td><td>9%</td><td>3%</td><td>8%</td><td>3%</td></tr><tr><td rowspan="2">k = 4</td><td rowspan="2"></td><td>cluster 3</td><td>24%</td><td>43%</td><td>24%</td><td>43%</td><td>23%</td><td>43%</td></tr><tr><td>cluster 4</td><td>23%</td><td>22%</td><td>22%</td><td>22%</td><td>22%</td><td>22%</td></tr><tr><td rowspan="2">k = 4</td><td rowspan="2"></td><td>cluster 1</td><td>21%</td><td>52%</td><td>31%</td><td>54%</td><td>38%</td><td>52%</td></tr><tr><td>cluster 2</td><td>28%</td><td>20%</td><td>38%</td><td>20%</td><td>41%</td><td>16%</td></tr><tr><td rowspan="2">k = 4</td><td rowspan="2"></td><td>cluster 3</td><td>8%</td><td>24%</td><td>10%</td><td>23%</td><td>16%</td><td>28%</td></tr><tr><td>cluster 4</td><td>2%</td><td>4%</td><td>3%</td><td>3%</td><td>4%</td><td>4%</td></tr></tbody></table>
<table><caption>**Table C.2**
Confusion matrix.</caption><thead><tr><th></th><th>Accuracy</th><th>Sensitivity</th><th>Specificity</th><th>False positive<sup>a</sup></th><th>False negative<sup>a</sup></th><th>True positive<sup>a</sup></th><th>True negative<sup>a</sup></th><th>Expected signals</th></tr></thead><tbody><tr><th colspan="9" style="text-align: left;">All variables</th></tr><tr><td>Logistic regression</td><td>74%</td><td>53%</td><td>79%</td><td>17%</td><td>8%</td><td>9%</td><td>65%</td><td>18%</td></tr><tr><td>Decision tree</td><td>53%</td><td>98%</td><td>43%</td><td>47%</td><td>0%</td><td>17%</td><td>35%</td><td>18%</td></tr><tr><td>Random forest</td><td>78%</td><td>68%</td><td>81%</td><td>16%</td><td>6%</td><td>12%</td><td>66%</td><td>18%</td></tr><tr><td>SVM</td><td>81%</td><td>45%</td><td>88%</td><td>10%</td><td>10%</td><td>8%</td><td>73%</td><td>18%</td></tr><tr><td>LSTM</td><td>80%</td><td>63%</td><td>83%</td><td>14%</td><td>6%</td><td>11%</td><td>69%</td><td>18%</td></tr><tr><td>CNN</td><td>79%</td><td>48%</td><td>85%</td><td>12%</td><td>9%</td><td>8%</td><td>70%</td><td>18%</td></tr><tr><td>CNN-LSTM</td><td>78%</td><td>62%</td><td>82%</td><td>15%</td><td>7%</td><td>11%</td><td>67%</td><td>18%</td></tr><tr><th colspan="9" style="text-align: left;">Subset of variables</th></tr><tr><td>Logistic regression</td><td>86%</td><td>57%</td><td>92%</td><td>7%</td><td>7%</td><td>10%</td><td>76%</td><td>18%</td></tr><tr><td>Decision tree</td><td>87%</td><td>75%</td><td>89%</td><td>9%</td><td>4%</td><td>13%</td><td>74%</td><td>18%</td></tr><tr><td>Random forest</td><td>89%</td><td>67%</td><td>94%</td><td>5%</td><td>6%</td><td>12%</td><td>77%</td><td>18%</td></tr><tr><td>SVM</td><td>81%</td><td>85%</td><td>80%</td><td>17%</td><td>3%</td><td>15%</td><td>66%</td><td>18%</td></tr><tr><td>LSTM</td><td>87%</td><td>74%</td><td>90%</td><td>9%</td><td>5%</td><td>13%</td><td>74%</td><td>18%</td></tr><tr><td>CNN</td><td>91%</td><td>76%</td><td>94%</td><td>5%</td><td>4%</td><td>13%</td><td>77%</td><td>18%</td></tr><tr><td>CNN-LSTM</td><td>89%</td><td>74%</td><td>92%</td><td>7%</td><td>5%</td><td>13%</td><td>76%</td><td>18%</td></tr></tbody></table>

<table><caption>**Table C.3**
Average impact on model output magnitude based on mean absolute SHAP values — CNN.</caption><thead><tr><th rowspan="2"></th><th rowspan="2">Ranking</th><th colspan="3">CNN — all features</th><th colspan="3">CNN — subset of features</th></tr><tr><th>Feature</th><th>Impact<sup>a</sup></th><th>Sign<sup>b</sup></th><th>Feature</th><th>Impact<sup>a</sup></th><th>Sign<sup>b</sup></th></tr></thead><tbody><tr><td rowspan="6">1996–1998</td><td>1</td><td>Exchange rate/VIX cor.</td><td>1.88</td><td>+</td><td>MSCI Pacific Index volatility</td><td>1.12</td><td>+</td></tr><tr><td>2</td><td>Exchange rate momentum (24 months)</td><td>0.50</td><td>–</td><td>US share price growth (y/y)</td><td>0.87</td><td>+</td></tr><tr><td>3</td><td>Share price/MSCI Emerging Index cor.</td><td>0.49</td><td>+</td><td>Share price/VIX cor.</td><td>0.78</td><td>+</td></tr><tr><td>4</td><td>Central bank policy rate differential with the US</td><td>0.38</td><td>+</td><td>US central bank policy rate</td><td>0.68</td><td>+</td></tr><tr><td>5</td><td>Cumulative currency crises (12 months, developed economies)</td><td>0.36</td><td>+</td><td>Inflation differential with the US</td><td>0.62</td><td>+</td></tr><tr><td>6</td><td>CPI volatility</td><td>0.36</td><td>+</td><td>Public debt to GDP growth (y/y)</td><td>0.61</td><td>+</td></tr><tr><td rowspan="6">2000–2002</td><td>1</td><td>Share price/MSCI Emerging Index cor.</td><td>0.88</td><td>+</td><td>Unemployment rate</td><td>0.91</td><td>+</td></tr><tr><td>2</td><td>Cumulative currency crises (history of the country)</td><td>0.65</td><td>–</td><td>Share price/MSCI Emerging Index cor.</td><td>0.86</td><td>+</td></tr><tr><td>3</td><td>Nominal exchange rate growth differential with previous year</td><td>0.59</td><td>+</td><td>Cumulative currency crises (12 months, low-income developing economies)</td><td>0.80</td><td>–</td></tr><tr><td>4</td><td>MSCI Europe Index volatility</td><td>0.57</td><td>+</td><td>US share price growth (y/y)</td><td>0.70</td><td>+</td></tr><tr><td>5</td><td>Unemployment rate</td><td>0.49</td><td>+</td><td>Share price/VIX cor.</td><td>0.68</td><td>+</td></tr><tr><td>6</td><td>Exchange rate/VIX cor.</td><td>0.38</td><td>+</td><td>Domestic credit to GDP</td><td>0.61</td><td>+</td></tr><tr><td rowspan="6">2008–2010</td><td>1</td><td>US terms of trade growth (y/y)</td><td>0.63</td><td>+</td><td>US central bank policy rate</td><td>1.29</td><td>+</td></tr><tr><td>2</td><td>Brent volatility</td><td>0.59</td><td>+</td><td>US share price growth (y/y)</td><td>1.22</td><td>+</td></tr><tr><td>3</td><td>US domestic credit to GDP</td><td>0.51</td><td>–</td><td>Brent</td><td>0.80</td><td>+</td></tr><tr><td>4</td><td>US inflation rate</td><td>0.48</td><td>–</td><td>MSCI Pacific Index volatility</td><td>0.52</td><td>–</td></tr><tr><td>5</td><td>US share price growth (y/y)</td><td>0.41</td><td>+</td><td>Nominal exchange rate growth (y/y)</td><td>0.30</td><td>+</td></tr><tr><td>6</td><td>Brent</td><td>0.34</td><td>+</td><td>Nominal exchange rate growth (m/m)</td><td>0.28</td><td>+</td></tr><tr><td rowspan="6">2014–2016</td><td>1</td><td>Real exchange rate growth (m/m)</td><td>0.42</td><td>+</td><td>US central bank policy rate</td><td>0.72</td><td>–</td></tr><tr><td>2</td><td>Broad money to international reserves</td><td>0.40</td><td>–</td><td>Brent</td><td>0.71</td><td>+</td></tr><tr><td>3</td><td>Unemployment rate</td><td>0.39</td><td>+</td><td>US share price growth (y/y)</td><td>0.49</td><td>+</td></tr><tr><td>4</td><td>Share price/MSCI Emerging Index cor.</td><td>0.36</td><td>+</td><td>Cumulative currency crises (12 months, low-income developing economies)</td><td>0.42</td><td>–</td></tr><tr><td>5</td><td>Exchange rate/share price cor.</td><td>0.27</td><td>–</td><td>US public debt to GDP</td><td>0.42</td><td>–</td></tr><tr><td>6</td><td>Nominal exchange rate growth (m/m)</td><td>0.26</td><td>+</td><td>Nominal exchange rate growth (m/m)</td><td>0.40</td><td>+</td></tr></tbody></table>

(continued on next page)

<footnote><sup>a</sup> As percentage of total observations.</footnote>
<table><caption>Table C.3 (continued).</caption><thead><tr><th rowspan="2"></th><th rowspan="2">Ranking</th><th colspan="3">CNN — all features</th><th colspan="3">CNN — subset of features</th></tr><tr><th>Feature</th><th>Impact<sup>a</sup></th><th>Sign<sup>b</sup></th><th>Feature</th><th>Impact<sup>a</sup></th><th>Sign<sup>b</sup></th></tr></thead><tbody><tr><td rowspan="6">2020–2021</td><td>1</td><td>Real exchange rate growth (m/m)</td><td>0.34</td><td>+</td><td>US central bank policy rate</td><td>2.15</td><td>–</td></tr><tr><td>2</td><td>Nominal exchange rate growth (m/m)</td><td>0.32</td><td>+</td><td>US public debt to GDP</td><td>0.96</td><td>+</td></tr><tr><td>3</td><td>US import growth (y/y)</td><td>0.32</td><td>–</td><td>Brent</td><td>0.70</td><td>+</td></tr><tr><td>4</td><td>Output gap</td><td>0.30</td><td>+</td><td>Nominal exchange rate growth (m/m)</td><td>0.61</td><td>+</td></tr><tr><td>5</td><td>Exchange rate volatility (6 months)</td><td>0.28</td><td>+</td><td>Public debt to GDP growth (y/y)</td><td>0.47</td><td>+</td></tr><tr><td>6</td><td>US public debt to GDP growth (y/y)</td><td>0.24</td><td>+</td><td>Central bank policy rate</td><td>0.46</td><td>+</td></tr></tbody></table>

<caption>Table C.4</caption>

Average impact on model output magnitude based on mean absolute SHAP values — LSTM.

<table><thead><tr><th rowspan="2"></th><th rowspan="2">Ranking</th><th colspan="3">LSTM — all features</th><th colspan="3">LSTM — subset of features</th></tr><tr><th>Feature</th><th>Impact<sup>a</sup></th><th>Sign<sup>b</sup></th><th>Feature</th><th>Impact<sup>a</sup></th><th>Sign<sup>b</sup></th></tr></thead><tbody><tr><td rowspan="6">1996–1998</td><td>1</td><td>Real exchange rate overvaluation (% of trend)</td><td>2.21</td><td>+</td><td>US share price growth (y/y)</td><td>3.17</td><td>+</td></tr><tr><td>2</td><td>Central bank policy rate differential</td><td>1.05</td><td>+</td><td>Inflation differential with the US</td><td>1.51</td><td>+</td></tr><tr><td>3</td><td>Cumulative currency crises (12 months, “Americas” region)</td><td>1.04</td><td>–</td><td>Domestic credit to GDP</td><td>1.49</td><td>+</td></tr><tr><td>4</td><td>Central bank policy rate</td><td>0.98</td><td>+</td><td>Import growth (y/y)</td><td>1.32</td><td>+</td></tr><tr><td>5</td><td>Public debt to GDP growth (y/y)</td><td>0.98</td><td>+</td><td>Domestic credit to GDP growth (y/y)</td><td>1.24</td><td>–</td></tr><tr><td>6</td><td>US international reserves growth (y/y)</td><td>0.77</td><td>–</td><td>Nominal exchange rate growth (m/m)</td><td>0.99</td><td>+</td></tr><tr><td rowspan="6">2000–2002</td><td>1</td><td>US broad money to international reserves</td><td>1.38</td><td>+</td><td>Unemployment rate</td><td>1.97</td><td>+</td></tr><tr><td>2</td><td>Domestic credit to GDP</td><td>1.29</td><td>+</td><td>US share price growth (y/y)</td><td>1.89</td><td>+</td></tr><tr><td>3</td><td>Unemployment rate</td><td>1.02</td><td>+</td><td>US domestic credit to GDP</td><td>1.80</td><td>+</td></tr><tr><td>4</td><td>US current account to GDP</td><td>0.98</td><td>–</td><td>Central bank policy rate</td><td>1.51</td><td>+</td></tr><tr><td>5</td><td>Share price/MSCI Emerging Index cor.</td><td>0.92</td><td>+</td><td>Share price/MSCI Emerging Index cor.</td><td>1.47</td><td>+</td></tr><tr><td>6</td><td>Cumulative currency crises (12 months, low-income developing economies)</td><td>0.92</td><td>+</td><td>Domestic credit to GDP</td><td>1.27</td><td>+</td></tr><tr><td rowspan="6">2008–2010</td><td>1</td><td>US export growth (y/y)</td><td>0.88</td><td>+</td><td>US domestic credit to GDP</td><td>3.06</td><td>+</td></tr><tr><td>2</td><td>US terms of trade growth (y/y)</td><td>0.86</td><td>+</td><td>US central bank policy rate</td><td>2.51</td><td>+</td></tr><tr><td>3</td><td>Current account to GDP</td><td>0.82</td><td>–</td><td>US share price growth (y/y)</td><td>2.46</td><td>+</td></tr><tr><td>4</td><td>US inflation rate</td><td>0.79</td><td>–</td><td>US public debt to GDP growth (y/y)</td><td>1.75</td><td>+</td></tr><tr><td>5</td><td>Domestic credit to GDP</td><td>0.75</td><td>+</td><td>Real GDP growth (y/y)</td><td>1.51</td><td>–</td></tr><tr><td>6</td><td>US nominal GDP growth (y/y)</td><td>0.70</td><td>–</td><td>Central bank policy rate</td><td>1.09</td><td>+</td></tr><tr><td rowspan="6">2014–2016</td><td>1</td><td>Unemployment rate</td><td>0.89</td><td>+</td><td>US public debt to GDP</td><td>6.35</td><td>+</td></tr><tr><td>2</td><td>Exchange rate/VIX cor.</td><td>0.69</td><td>–</td><td>US central bank policy rate</td><td>5.01</td><td>+</td></tr><tr><td>3</td><td>Real exchange rate volatility</td><td>0.62</td><td>+</td><td>Central bank policy rate</td><td>2.00</td><td>+</td></tr><tr><td>4</td><td>Broad money to international reserves</td><td>0.60</td><td>–</td><td>Domestic credit to GDP</td><td>1.95</td><td>+</td></tr><tr><td>5</td><td>Share price/MSCI Emerging Index cor.</td><td>0.56</td><td>+</td><td>Cumulative currency crises (12 months, low-income developing economies)</td><td>1.51</td><td>–</td></tr><tr><td>6</td><td>US central bank policy rate</td><td>0.49</td><td>+</td><td>US share price growth (y/y)</td><td>1.23</td><td>+</td></tr></tbody></table>

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<footnote>a Average impact on model output magnitude (mean absolute SHAP values), in percentage points.</footnote>

<footnote>b Average sign of the influence of the explanatory variable on output.</footnote>
<table><caption>Table C.4 (continued).</caption><thead><tr><th rowspan="2"></th><th rowspan="2">Ranking</th><th colspan="3">LSTM — all features</th><th colspan="3">LSTM — subset of features</th></tr><tr><th>Feature</th><th>Impact<sup>a</sup></th><th>Sign<sup>b</sup></th><th>Feature</th><th>Impact<sup>a</sup></th><th>Sign<sup>b</sup></th></tr></thead><tbody><tr><td rowspan="6">2020–2021</td><td>1</td><td>Unemployment rate</td><td>1.21</td><td>+</td><td>US public debt to GDP</td><td>2.12</td><td>+</td></tr><tr><td>2</td><td>US unemployment rate</td><td>0.98</td><td>–</td><td>Unemployment rate</td><td>1.38</td><td>+</td></tr><tr><td>3</td><td>US broad money growth (y/y)</td><td>0.79</td><td>+</td><td>US public debt to GDP growth (y/y)</td><td>1.28</td><td>–</td></tr><tr><td>4</td><td>Current account to GDP</td><td>0.77</td><td>–</td><td>US share price growth (y/y)</td><td>1.21</td><td>+</td></tr><tr><td>5</td><td>CPI volatility</td><td>0.59</td><td>+</td><td>Central bank policy rate</td><td>1.13</td><td>+</td></tr><tr><td>6</td><td>MSCI World Index volatility</td><td>0.52</td><td>+</td><td>MSCI Pacific Index volatility</td><td>0.95</td><td>+</td></tr></tbody></table>

<caption>Table C.5</caption>

Average impact on model output magnitude based on mean absolute SHAP values — Logistic regression.

<table><thead><tr><th rowspan="2">Period</th><th rowspan="2">Ranking</th><th colspan="3">Logit — all features</th><th colspan="3">Logit — subset of features</th></tr><tr><th>Feature</th><th>Impact<sup>a</sup></th><th>Sign<sup>b</sup></th><th>Feature</th><th>Impact<sup>a</sup></th><th>Sign<sup>b</sup></th></tr></thead><tbody><tr><td rowspan="6">1996–1998</td><td>1</td><td>Exchange rate/VIX cor.</td><td>0.54</td><td>+</td><td>Inflation differential with the US</td><td>0.89</td><td>+</td></tr><tr><td>2</td><td>Unemployment rate</td><td>0.41</td><td>+</td><td>Inflation rate</td><td>0.83</td><td>–</td></tr><tr><td>3</td><td>Broad money growth (y/y)</td><td>0.41</td><td>–</td><td>US public debt to gdp growth (y/y)</td><td>0.65</td><td>+</td></tr><tr><td>4</td><td>Domestic credit to GDP</td><td>0.35</td><td>+</td><td>US public debt to GDP</td><td>0.64</td><td>+</td></tr><tr><td>5</td><td>Inflation differential with the US</td><td>0.33</td><td>+</td><td>US share price growth (y/y)</td><td>0.61</td><td>–</td></tr><tr><td>6</td><td>Share price/VIX cor.</td><td>0.33</td><td>–</td><td>Domestic credit to GDP</td><td>0.57</td><td>+</td></tr><tr><td rowspan="6">2000–2002</td><td>1</td><td>Inflation differential with the US</td><td>0.02</td><td>+</td><td>US domestic credit to GDP</td><td>0.41</td><td>–</td></tr><tr><td>2</td><td>Share price/MSCI Emerging Index cor.</td><td>0.02</td><td>+</td><td>US share price growth (y/y)</td><td>0.40</td><td>–</td></tr><tr><td>3</td><td>Inflation rate</td><td>0.02</td><td>+</td><td>Unemployment rate</td><td>0.35</td><td>+</td></tr><tr><td>4</td><td>Share price/VIX cor.</td><td>0.02</td><td>–</td><td>Share price/MSCI Emerging Index cor.</td><td>0.30</td><td>+</td></tr><tr><td>5</td><td>US broad money to international reserves</td><td>0.02</td><td>–</td><td>US public debt to GDP growth (y/y)</td><td>0.28</td><td>+</td></tr><tr><td>6</td><td>US share price growth (y/y)</td><td>0.01</td><td>+</td><td>Brent</td><td>0.26</td><td>+</td></tr><tr><td rowspan="6">2008–2010</td><td>1</td><td>US nominal GDP growth (y/y)</td><td>0.98</td><td>–</td><td>US central bank policy rate</td><td>0.72</td><td>+</td></tr><tr><td>2</td><td>US inflation rate</td><td>0.97</td><td>–</td><td>US public debt to GDP growth (y/y)</td><td>0.67</td><td>–</td></tr><tr><td>3</td><td>US public debt to GDP</td><td>0.97</td><td>+</td><td>Inflation differential with the US</td><td>0.40</td><td>+</td></tr><tr><td>4</td><td>US public debt to GDP growth (y/y)</td><td>0.90</td><td>–</td><td>Inflation rate</td><td>0.39</td><td>–</td></tr><tr><td>5</td><td>Cumulative currency crises (12 months, all countries)</td><td>0.83</td><td>–</td><td>US public debt to GDP</td><td>0.32</td><td>–</td></tr><tr><td>6</td><td>US import growth (y/y)</td><td>0.80</td><td>+</td><td>US domestic credit to GDP</td><td>0.31</td><td>+</td></tr><tr><td rowspan="6">2014–2016</td><td>1</td><td>US central bank policy rate</td><td>0.12</td><td>+</td><td>US central bank policy rate</td><td>0.18</td><td>+</td></tr><tr><td>2</td><td>US terms of trade</td><td>0.11</td><td>+</td><td>US public debt to GDP</td><td>0.16</td><td>–</td></tr><tr><td>3</td><td>US current account to GDP</td><td>0.11</td><td>+</td><td>Unemployment rate</td><td>0.09</td><td>+</td></tr><tr><td>4</td><td>US broad money to international reserves</td><td>0.09</td><td>–</td><td>Cumulative currency crises (24 months, all countries)</td><td>0.09</td><td>–</td></tr><tr><td>5</td><td>Unemployment rate</td><td>0.09</td><td>+</td><td>Share price/VIX cor.</td><td>0.09</td><td>–</td></tr><tr><td>6</td><td>Share price/MSCI Europe Index cor.</td><td>0.08</td><td>–</td><td>Brent</td><td>0.07</td><td>+</td></tr><tr><td rowspan="6">2020–2021</td><td>1</td><td>US unemployment rate</td><td>0.25</td><td>+</td><td>US public debt to GDP</td><td>0.42</td><td>–</td></tr><tr><td>2</td><td>US broad money to international reserves</td><td>0.15</td><td>–</td><td>Unemployment rate</td><td>0.16</td><td>+</td></tr><tr><td>3</td><td>US output gap</td><td>0.12</td><td>–</td><td>US central bank policy rate</td><td>0.12</td><td>+</td></tr><tr><td>4</td><td>Unemployment rate</td><td>0.11</td><td>+</td><td>Share price/VIX cor.</td><td>0.10</td><td>–</td></tr><tr><td>5</td><td>US current account to GDP</td><td>0.11</td><td>+</td><td>Public debt to GDP growth (y/y)</td><td>0.09</td><td>+</td></tr><tr><td>6</td><td>US public debt to GDP</td><td>0.10</td><td>–</td><td>MSCI Pacific Index volatility</td><td>0.01</td><td>+</td></tr></tbody></table>

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<footnote>a Average impact on model output magnitude (mean absolute SHAP values), in percentage points.</footnote>

<footnote>b Average sign of the influence of the explanatory variable on output.</footnote>

<footnote>a Average impact on model output magnitude (mean absolute SHAP values), in percentage points.</footnote>

<footnote>b Average sign of the influence of the explanatory variable on output.</footnote>
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**Sylvain Barthélémy** is the founder and CEO of Gwenlake, an associate professor at the University of Rennes, and a lecturer at HEC Paris. His research interests include machine learning, artificial intelligence, and statistical analysis in the fields of finance, economics and life science.

**Virginie Gautier** is a Ph.D. from the University of Rennes and a member of the Center of Research in Economics and Administration (CREM). She is also a junior economist and data scientist at TAC Economics. Her research interests include macroeconomics, international economics and machine learning.

**Fabien Rondeau** is an associate professor at the University of Rennes in the economics department, a member of the Center of Research in Economics and Administration, and a fellow of the Euro Area Business Cycle Network. His research interests include macroeconomics, international economics, trade and integration and the European Union.