This paper investigates the dynamic relationship among ecological footprint indicators, the frequency of financial crises, globalization and economic growth on the global level during 1970–2019. The analysis builds on the local projections methodology. A higher frequency of financial crises involves a decline in the ecological footprint indicators. In its turn, environmental degradation increases the frequency of financial crises, particularly, banking and currency ones. The financial crises driven by environmental degradation dampen global economic growth, hampering non-environmental sustainable development. The ecological footprint indicators also exhibit bidirectional linkages with global economic growth and globalization. Globalization and economic growth first invigorate ecological footprint, but then the latter starts to inhibit the former phenomena. Thus, environmental sustainability cannot be improved just by deepening globalization and accelerating global economic growth. A mix of environmental policy stringency and green macroprudential policies can improve global ecological footprint indicators and decrease the frequency of financial crises.

We investigate the time-varying role of Russia in the international causal network of financial stress. The network is derived using the factor-adjusted network estimation for high-dimensional time series (FNETS), which is applied to text-based financial stress indices for 66 countries. We run this technique for sequentially expanding time windows (ranging from 2000Q1:2013Q4 to 2000Q1:2023Q4). Based on centrality metrics, Russia is an important node in the international financial stress network. On average, the net balance of outgoing and incoming causal linkages is slightly negative, implying that Russia is a net receiver of financial stress. Nonetheless, this role of Russia in the network was reversed during the latest time windows encompassing the Russia-Ukraine conflict, as the number of outgoing linkages substantially increased. Despite pronounced geopolitical turbulences, the US and major European economies have the highest number of causal linkages with the Russian financial stress throughout the entire research period, while the density of linkages between the Russian financial stress and that of BRICS economies is strikingly limited.

We propose a novel sentiment-based index of global financial stress for the period between January 2004 and July 2025. It builds on the dictionary comprising English terms related to financial instability and selected with the aid of more than 200 large language models. The index represents the first principal component from the data series measuring the intensity of search in Google for these terms. It notably spikes with the GFC in September–October 2008, the outbreak of COVID-19 in March 2020, the onset of the Ukrainian conflict in February 2022 and the turmoil in the US banking sector in March 2023. The index is not driven by the extant financial stress or uncertainty measures, exhibiting moderate predictive power for some of them. Furthermore, it produces a detrimental effect on global real economic activity when the latter is in decline. The index Granger causes the worldwide frequency of currency crises, while exhibiting bidirectional linkages with the frequency of banking crises, triple episodes involving banking, currency and debt crises, and the implementation of macroprudential policy measures. Finally, we show that the dictionary underlying our global index can apply to elaborate country-level financial stress indices, using the USA and the UK as an example.

The paper studies the relationship between the state of world's biodiversity proxied by the Living Planet Index and the frequency of financial crises, conditional on global economic growth and the total number of biodiversity-related environmental policy instruments, during 1970-2018. We find that the increased frequency of banking crises as well as triple crises, i.e. simultaneously occurring banking, sovereign debt and currency crises, has a detrimental effect on biodiversity. Moreover, this relationship appears bi-directional. Thus, our findings call for a joint implementation of environmental and macroprudential policies to better align the goals of biodiversity conservation and financial stability worldwide.

We propose a sentiment-based financial stress index (S-FSI) for Russia from January 2018 to June 2024. The index is based on the intensity of internet searches in Russian via Google and Yandex for terms with negative connotations about financial stability. The terms in our index form a unique dictionary that captures both global and country-specific financial stress factors. The index shows the most significant increases at the onset of the COVID-19 pandemic and the Russian-Ukrainian conflict. Evidence suggests that our index precedes an alternative financial stress index for Russia based on conventional financial data and industrial production. The index also provides valuable insights not captured by existing non-financial sentiment indicators for Russia—economic policy uncertainty and geopolitical risk indices. Overall, our proposed novel index can effectively monitor financial stress in the Russian economy.

The paper investigates the lead-lag relationships among systemic risk, economic uncertainty and real economic activity for top-5 contributors to global systemic risk, the USA, China, the UK, France and Japan, during January 2010-July 2023. By decomposing these variables into a below- and above-the-median components and applying VAR models to them, we confirm the presence of asymmetric relationships among systemic risk, economic uncertainty and real economic activity. Most of these asymmetric linkages are found for the USA, followed by the UK and China.

We investigate the effect of sanctions on the occurrence of financial crises. We use the Classification and Regression Tree (CART) algorithm to check whether binary classification mechanism selects sanctions as a predictive factor for the different types of financial crises. We find that trade sanctions matter for the increased probability of banking crises, while military sanctions are associated with currency crises. We find no evidence of the effect of sanctions on sovereign debt crises. We furthermore indicate which variables and their respective thresholds serve as potential harbingers of financial crises.

The paper studies the relationships among the composite indicators of environmental performance, financial development, systemic risk and economic uncertainty for a balanced panel of 57 countries during 2010-2020. The analysis builds on panel local projections by Jordá (2005). In addition to the whole panel, this technique also applies to two sub-panels obtained via the K-means clusterization conditional on a set of composite indicators of environmental performance. We underscore a two-way relationship between systemic risk and environmental performance. An increase in systemic risk improves the environmental quality, albeit to the detriment of economic growth and energy consumption, whereas ex ante higher values of the key composite indicators of environmental performance mitigate systemic risk. Financial development adversely affects environmental performance. Contrary to the prevailing view, this effect is mostly related to the development of financial markets compared to the development of financial institutions. Economic uncertainty is found totally unrelated to the composite indicators of environmental performance. The aforementioned key findings generally hold after splitting the whole panel into the two sub-panels. Overall, our results induce policymakers to treat with caution certain policy recommendations aimed at improving environmental quality, since reducing systemic risk, increasing financial development as a whole or shifting towards a market-based financial system do not necessarily help accomplish this goal.

We identify robust predictors of global systemic risk proxied by conditional capital shortfall (SRISK) among a comprehensive set of commodity prices for the period between January 2004 and December 2021. The search is based on a battery of ML variable selection algorithms which apply both to price levels and price shocks in the presence of control variables, including the first lag of SRISK, world industrial production, global economic policy uncertainty, geopolitical risk as well as the global stance of monetary and macroprudential policies. We find that these controls outweigh commodity prices as the predictors of global systemic risk. Of the commodities themselves, the prices for agricultural commodities, including food, e.g. chicken, bananas, beef, tea, cocoa, are more important predictors of global systemic risk than the prices for energy commodities, e.g. natural gas and oil prices. The financialization of agricultural commodities, bio-energy expansion as well as commodity-specific dependence of the major economies contributing to global systemic risk, e.g. China, account for our main finding. We also document the positive linkage between commodity prices and systemic risk for the majority of commodities. Thus, monitoring commodity prices to avoid their unbalanced growth is of vast importance to curb global systemic financial risk.

We test the predictive performance of different ensemble methods for forecasting systemic risk in Russia for the period 2008-2024. In contrast to the existing research on machine learning ensemble techniques, we find that conventional random forest works better for the Russian data. Based on this model, we additionally conduct variable importance analysis. We identify that the first three lags of the exchange rate as well as the level of non-performing loans are of utmost importance for systemic risk in the national financial sector.

This study examines the evolution of research on systemic risk during the 2007–2021 period, encompassing the Global Financial Crisis, European financial crisis, the outbreak of the COVID-19 and a number of other notable episodes undermining global financial stability. Our research goal is two-fold. First, based on Scopus-indexed publications, we identify the most impactful countries, institutions and scholars in the field, revealing a gradual but notable decline in the annual shares of publications on systemic risk associated with major advanced economies. This is offset by the increasing role of emerging markets, primarily, China, which makes this research field more competitive. Second, we are also concerned with the drivers of research on systemic risk in a vast sample of countries during the observation period. By applying a combination of variable selection techniques to 33 indicators that can potentially incentivize research on systemic risk, we find that low bank profitability as well as the general productivity of economic research are the most robust factors stimulating such publications. A higher country’s score on the global innovation index also spurs the research in this field. Conversely, countries with a higher power distance index, i.e favoring hierarchy and low risk-taking, tend to produce less research on systemic risk.

The paper aims to indentify and compare the determinants of the overall FinTech market expansion and its major segments – cryptocurrency and peer-to-peer lending markets – in a dataset, which covers 64 countries and 51 potentially relevant factors. To this end, we apply a battery of state-of-the-art variable selection techniques from machine learning, comprising Bayesian model averaging (BMA), least absolute shrinkage and selection operator (LASSO), variable selection using random forests (VSURF) as well as spike-and-slab regression. We document substantial heterogeneity of the pivotal determinants across the FinTech market as a whole and its major segments. Thus, specific rather than general policy measures are needed to foster the development of standalone FinTech market segments. Moreover, our findings suggest that most countries don't need to seek a universal specialization in FinTech activities, concentrating on the segment where they have a competitive edge in terms of the pivotal determinants which drive its expansion.

The paper studies the interaction between a set of bank performance indicators (concentration, profitability, and risk) and the carbon footprint of bank loans. Our research builds on the panel data analysis for 37 countries during 2010–2018, adopting local projections proposed by Jordá (Am Econ Rev 95(1):161–182, 2005 ), a feasible alternative to panel VAR estimation in case of short time series. In order to account for potentially different patterns in the relationship among the indicators, we split the whole panel into two sub-panels, using K-means clusterization based on income level, resource abundance, and overall environmental performance. For the whole panel, the carbon footprint is driven by systemic risk, while leading the non-performing loans (NPL) ratio and Z-score. Thus, curbing systemic risk matters to reduce the carbon footprint of bank loans. Otherwise, it may amplify the effects of the latter on the NPL ratio and Z-score. Interestingly, the effect of systemic risk on the carbon footprint stems from the sub-panel consisting of developed countries, while the effect of the carbon footprint on the NPL ratio and Z-score is mainly shaped by developing and emerging market economies. The relationships between the carbon footprint of lending, concentration, and profitability are much less pronounced both for the whole panel and for the sub-panels.

We propose sentiment-based indicators of real estate market stress for the USA, the UK, Canada, Australia, India, and on the global scale. The global and country-level indicators are based on a novel methodology synthesizing textual analysis of real estate research and Google search data. Using mixed frequency vector autoregressions, we show that in the USA, the UK, Australia and India, the sentiment-based indicators are found to mediate the relationship between real estate prices and systemic financial risk. In particular, for the UK, there is a vicious circle involving the interaction among the three variables: the sentiment-based indicator of real estate market stress unidirectionally leads systemic risk, the latter impacts real estate prices, whereas the prices drive the stress sentiment. Canada appears the only sample country where real estate market stress sentiment is unrelated to real estate prices and systemic risk. On the global scale, there is a bi-directional linkage between the stress sentiment and real estate prices. Overall, our empirical findings suggest that policymakers and real estate market participants should account for sentiment regarding real estate market stress in their decision-making.

This paper examines the impact of the Global Financial Crisis (GFC) on wealth inequality. We investigate this question, using data for 143 countries for the period 2010–2018. We find no significant impact of the occurrence of the crisis on wealth inequality. We show limited evidence that the severity of the banking crisis affects the change in wealth inequality. Furthermore, the impact of the GFC on the change in wealth inequality is influenced by the country characteristics: the GFC has more enhanced wealth inequality in countries with higher levels of economic and financial development as well as lower initial levels of wealth inequality. We therefore contribute to a better understanding of the real effects of banking crises by providing evidence of the distributional effects of the GFC.

This article aims to identify the factors which promote research activity on banking crises in the cross-country framework during the period 2014-2020. Building on the population-adjusted country-level publication data from the Scopus and Web of Science databases and applying Bayesian model averaging (BMA) and least absolute shrinkage and selection operator (LASSO), we conduct an open search for such factors out of 23 candidate predictors. A higher level of bank concentration appears to be the most significant factor motivating research on banking crises. It is robust with respect to both bibliographic databases and variable selection methods used. Based only on the Scopus data, GDP per capita and the peak ratio of non-performing loans to total loans during the latest banking crisis experienced by a country also increase the number of published studies on banking crises.

An increasing attention has been riveted recently on so called ESGfactors impacting financial stability. This paper provides a systematic review of the empirical studies which assess the impact of environmental (climatic), social factors as well as various aspects related to corporate governance on financial stability. Overall, higher ESG-rankings, both aggregate and in terms of the three pillars (E, S, G), tend to enhance the financial system stability from the microand macroprudential perspective by mitigating aggregate individual risk of financial institutions and the contribution to systemic risk, respectively. Nonetheless, the research intensity within the ESG pillars differs substantially. There are significantly more studies investigating the impact of environmental and corporate governance factors then tackling the effects of social ones. This literature review is closed with the discussion of possible directions for future investigation in the given research program.

This paper seeks to identify the most important global drivers of credit-to-GDP gaps for 35 countries. The analysis is performed on a country-by-country basis for the sub-periods 2000Q1:2007Q2, 2007Q3:2013Q4, and 2014Q1:2021Q1 and is based on two state-of-the-art methods for variable selection in the time series framework: the one covariate at a time multiple testing (OCMT) and adaptive least absolute shrinkage and selection operator (LASSO). We find that the number of salient global factors tends to increase over time, reaching its maximum during the post-crisis period. This period is also marked by a pronounced role of the global factors capturing the stance of the US monetary policy, while in the preceding sub-periods, the most significant factors are global credit conditions (the TED spread) and world industrial production, respectively. Regardless of the sub-periods, advanced economies’ credit-to-GDP gaps appear more dependent on the global factors than the gaps in emerging markets. In addition, we identify country-specific variables which shape the susceptibility of the national credit-to-GDP gaps to the global factors.

We conduct an open search of predictors of global real economic activity. To this end, we apply a predictive quantile regression framework, using four alternative proxies of global real economic activity during February 1997–August 2019 and building on a combination of machine learning algorithms to identify their predictors out of 23 candidate explanatory variables. The contemporaneous level of global real economic activity, the Asian and US financial stress are found the most robust predictors. The effect of US financial shocks appears asymmetric, as they undermine global economic growth when the latter is below the median, but do not matter much when the world economy expands fast. Besides, US shadow interest rates are significantly and positively linked to global real economic activity. This effect holds in a high-growth regime of the world economy and suggests that rising US policy rates, contrary to the conventional wisdom, entail US dollar depreciation rather than appreciation. A weaker US dollar stimulates dollar-denominated cross-border bank inflows to the countries other than the USA, leading to a rise in real investment worldwide and industrial output growth. Thus, our empirical findings inform policymakers which indicators should be monitored more closely to predict future shifts in global economic growth and also provide certain insights about optimal policy responses to such shifts.