BRICS Under Pressure: How Geopolitics Threaten Financial Stability

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Author: Denis Avetisyan

New research reveals that interconnected banking systems in BRICS nations are highly vulnerable to systemic risk triggered by geopolitical events and amplified by behavioral panic.

Agent-based modeling and dynamic network analysis demonstrate the critical role of large institutions and correlated shocks in driving financial contagion within emerging markets.

Despite growing recognition of interconnected financial risk, current models often fail to capture the complex, cascading effects of geopolitical events and behavioral shifts. This is addressed in ‘Modeling Bank Systemic Risk of Emerging Markets under Geopolitical Shocks: Empirical Evidence from BRICS Countries’, which introduces a novel framework demonstrating that large institutional failures and correlated geopolitical shocks pose the greatest threats to BRICS financial stability, with panic effects proving more destabilizing than underlying vulnerabilities. Utilizing dynamic network analysis and agent-based modeling, the study reveals that second-order systemic risk-driven by shifts in bank relationships and widespread panic-can trigger near-total collapse. Will a more nuanced understanding of these interconnected risks allow for the development of truly resilient financial systems in emerging markets?

The Fragility of Systemic Stability

Hyman Minsky’s Financial Instability Hypothesis posits that prolonged periods of stability can inadvertently cultivate the seeds of financial crisis. This counterintuitive notion arises from the observation that consistent economic success encourages increased risk-taking; as memories of past downturns fade, financial institutions and investors become emboldened, leveraging more debt and pursuing increasingly speculative ventures. This shift in behavior creates a system vulnerable to shocks, as the margin for error diminishes and asset bubbles inflate. Minsky categorized investment strategies into three types – hedge, speculative, and Ponzi – with a growing dominance of the latter two during stable expansions signaling heightened systemic risk. Ultimately, the very success that breeds complacency can paradoxically lay the groundwork for future financial fragility, as the system becomes reliant on continually escalating asset prices to service mounting debt obligations.

Conventional risk assessments frequently operate under the assumption of isolated failures, evaluating institutions and their vulnerabilities in a largely disconnected manner. This approach overlooks the intricate web of interdependencies that characterize modern finance, where institutions are linked through lending, investment, and derivative markets. Consequently, a shock to one entity can rapidly propagate through the system, triggering a cascade of defaults and liquidity crises. These systemic risks aren’t simply the sum of individual institutional weaknesses; they arise from the interactions between those weaknesses, creating vulnerabilities that are difficult to predict using traditional, siloed methodologies. The result is a global financial system that, despite apparent stability, remains susceptible to unexpected and potentially devastating failures, demanding a more holistic and interconnected approach to risk management.

The interconnectedness of global finance means that risk doesn’t respect national borders, making the propagation of financial shocks a paramount concern, especially within emerging economies like the BRICS Nations. These nations, while demonstrating robust growth, often possess developing financial infrastructure and regulatory frameworks, increasing their susceptibility to both internal vulnerabilities and external pressures. A localized economic downturn or financial instability in one BRICS nation can rapidly transmit outwards, impacting trade relationships, investment flows, and ultimately, the financial health of other member states and the wider global economy. Understanding these transmission channels – be they through direct financial linkages, commodity price fluctuations, or shifts in investor sentiment – is therefore crucial for proactive risk management and the implementation of effective preventative strategies. Analyzing the specific vulnerabilities within each BRICS nation, alongside their interdependencies, allows for a more nuanced assessment of systemic risk and the development of targeted policies to mitigate potential contagion effects.

Financial contagion represents a significant threat to global economic stability, describing the rapid transmission of economic shocks from one nation or institution to others. This isn’t simply a matter of correlated markets; it’s a process where a localized crisis – perhaps stemming from a failing bank or sovereign debt default – triggers a cascade of failures due to interconnectedness and shared exposures. The mechanisms driving contagion are multifaceted, including direct financial linkages, trade relationships, and – crucially – shifts in investor sentiment and herding behavior. Consequently, robust monitoring systems are essential, tracking cross-border capital flows, assessing institutional vulnerabilities, and identifying emerging hotspots of risk. Preventative measures, such as strengthened international regulatory cooperation, diversified portfolios, and proactive capital buffers, are vital to contain localized shocks and prevent them from escalating into systemic crises that jeopardize the global financial system.

BRIDGES: A Networked Approach to Risk Assessment

The BRIDGES framework addresses the challenges of systemic risk assessment within the banking sectors of BRICS nations, which often suffer from limited data availability and transparency. Unlike traditional risk models requiring extensive historical data, BRIDGES is designed to operate effectively in data-constrained environments by combining Agent-Based Modeling (ABM) with Monte Carlo Simulation. This allows for the generation of synthetic data and the exploration of a wider range of potential scenarios. The methodology focuses on dynamically analyzing bank interactions and identifying vulnerabilities that may not be apparent through static, data-dependent approaches, specifically targeting the unique structural and regulatory complexities of the BRICS financial landscape.

The BRIDGES framework’s core simulation engine combines Agent-Based Modeling (ABM) with Monte Carlo Simulation to represent and analyze the behavior of individual banks within the BRICS banking system. ABM allows for the creation of autonomous ‘agents’ – in this case, banks – each with defined characteristics and decision rules, enabling the simulation of their independent actions and interactions. Monte Carlo Simulation is then integrated to introduce stochasticity, generating numerous scenarios based on probability distributions assigned to key variables influencing bank behavior, such as loan defaults or capital adequacy ratios. This combination allows BRIDGES to move beyond static risk assessments by modeling the emergent properties arising from the complex interplay between banks, providing a more nuanced understanding of potential systemic risks than traditional methods.

Dynamic Time Warping (DTW) is utilized to establish a network representing the relationships between banks by quantifying the similarity of their strategic profiles as reflected in balance sheet data. This approach moves beyond simple correlation and accounts for temporal lags and distortions in the data, allowing for the identification of banks pursuing comparable strategies even if their financial reporting periods differ. Specifically, DTW calculates the optimal alignment between two time series-in this case, balance sheet ratios representing key strategic decisions-minimizing the distance between them. Banks exhibiting highly similar DTW scores are then connected in the network, with edge weights reflecting the degree of similarity, thus mapping the interconnectedness of the banking system based on strategic alignment rather than solely on direct financial exposure.

The BRIDGES framework utilizes Temporal Graph Neural Networks (TGNNs) to monitor evolving relationships within the banking network. TGNNs process the network’s structure as a time series, enabling the identification of deviations from established patterns of interconnection. These networks learn node embeddings that capture both individual bank characteristics and their dynamic relationships with other banks. Anomalies detected by the TGNN are flagged as potential emerging systemic risks, based on statistically significant changes in network topology or node behavior. This approach allows for the proactive identification of vulnerabilities before they manifest as widespread financial instability, surpassing the limitations of static network analysis.

Uncovering Hidden Risks: Beyond Static Assessments

Existing systemic risk measures, such as the SRISK_CS index, are fundamentally limited by their reliance on Zero-Order Information. This consists of static, point-in-time data derived from bank balance sheets, including asset values and liabilities. While providing a baseline assessment of financial health, this approach fails to capture the dynamic nature of systemic vulnerability. Specifically, Zero-Order Information does not account for changes in bank behavior over time, nor does it reflect the interconnectedness and evolving structure of the banking network. Consequently, it provides an incomplete picture, potentially underestimating the true extent of risk and failing to identify emerging vulnerabilities before they manifest as financial distress. This static view contrasts with methods leveraging First-Order and Second-Order Information, which incorporate temporal trends and network dynamics, respectively.

The BRIDGES framework enhances traditional systemic risk assessment by incorporating First-Order Information, which involves the continuous monitoring of key bank risk ratios over time. This temporal analysis allows for the detection of shifts in bank behavior that static balance sheet reviews – relying solely on Zero-Order Information – would miss. Specifically, BRIDGES tracks metrics such as capital adequacy ratios, leverage ratios, and liquidity coverage ratios, identifying emerging trends and deviations from established norms. These changes can serve as early warning signals, indicating potential increases in bank vulnerability and the possibility of future systemic events before they manifest in balance sheet data. The framework’s ability to discern behavioral changes, rather than simply observing current risk levels, provides a more proactive and sensitive approach to systemic risk monitoring.

The BRIDGES framework differentiates itself from traditional systemic risk assessments by incorporating Second-Order Information, which analyzes changes within the interbank network structure. This goes beyond examining individual bank balance sheets or first-order risk ratios to identify anomalous patterns in how banks connect and interact. These structural shifts, such as rapidly changing counterparty relationships or the emergence of densely interconnected subgroups, indicate evolving systemic vulnerabilities not captured by static measures. Analysis of this Second-Order Information reveals complex risks arising from the network itself, potentially signifying a heightened susceptibility to contagion and cascading failures beyond those predicted by assessing individual bank weaknesses or simple trends in risk ratios.

Simulations within the BRIDGES framework indicate that the failure of a systemically important, or ‘too big to fail’, bank results in a significantly greater systemic loss compared to failures originating from either highly vulnerable or structurally anomalous banks. Specifically, the modeled systemic loss following the failure of a ‘too big to fail’ bank reached 44.6% of total deposits. This outcome contrasts sharply with the 23.4% systemic loss observed when subjecting the most vulnerable banks to equivalent stress, and is substantially higher than the 6.0% loss resulting from stressing the banks exhibiting the most anomalous network behavior. These results highlight the disproportionate risk posed by the failure of institutions with extensive interconnectedness and critical roles within the financial system.

Implications for Global Financial Resilience

The BRIDGES framework offers a substantial advancement in how regulatory bodies can approach financial stability. Beyond traditional stress tests that often focus on isolated bank failures, this system allows for the detection and quantification of systemic risk – the potential for a cascade of failures across the entire financial network. By mapping interconnectedness and assessing the propagation of shocks, BRIDGES provides a dynamic and granular view of vulnerabilities, moving beyond reliance on simple asset size as an indicator of risk. This capability enables more targeted and effective regulatory oversight, allowing authorities to proactively identify and address weaknesses before they escalate into full-blown crises, and to design stress tests that more accurately reflect real-world contagion dynamics. The framework’s analytical power facilitates a shift from reactive crisis management to proactive risk mitigation, ultimately bolstering the resilience of the global financial system.

The modern financial system is characterized by complex networks of interconnectedness, meaning the failure of one institution can rapidly cascade throughout the entire system via a process known as contagion. This interconnectedness dramatically amplifies risk, particularly in an era defined by increasing geopolitical instability. Recent analyses demonstrate that shocks originating from geopolitical events – such as regional conflicts or shifts in international relations – can trigger widespread financial distress. These events introduce systemic risk, which isn’t isolated to the directly affected institutions but spreads through interbank lending, shared investments, and correlated asset values. Consequently, understanding these transmission channels and quantifying the potential for contagion is not merely an academic exercise; it’s a fundamental requirement for safeguarding global financial stability and preventing localized crises from escalating into systemic failures.

The persistent challenge of institutions deemed “Too Big To Fail” necessitates a shift from evaluating systemic importance solely on asset size to a more nuanced, dynamic assessment. This framework offers precisely that, revealing how interconnectedness amplifies risk beyond what balance sheets suggest. Simulations demonstrate that a widespread geopolitical shock-one impacting multiple nations simultaneously-can trigger near-total systemic loss, far exceeding the damage caused by the failure of any single, even large, financial institution. This highlights that systemic risk isn’t simply the sum of individual failures, but a product of correlated vulnerabilities; a synchronized crisis can overwhelm even robustly capitalized systems, underscoring the need for regulatory tools that account for this interconnectedness and the potential for rapid, cascading failures.

The propagation of fear and diminished trust, known as behavioral contagion, plays a critical role in exacerbating financial crises, and recent simulations highlight its potential impact on global stability. Analyses indicate that systemic events aren’t solely driven by financial fundamentals but are significantly influenced by how institutions and markets react to perceived threats. Notably, Russia has been identified as exhibiting substantial structural vulnerabilities, falling into a ‘High Risk’ category under stress-testing scenarios. These simulations reveal a concerning pattern: under adverse conditions, Russia’s mean capital remaining drops to just 45%, suggesting a limited capacity to absorb shocks and a heightened susceptibility to contagion effects that could rapidly spread to interconnected financial systems. This underscores the necessity for proactive monitoring of investor sentiment and the development of strategies to counter panic-driven behaviors during times of crisis.

The study meticulously reveals how interconnectedness within the BRICS financial systems amplifies the impact of geopolitical disturbances. It echoes a sentiment expressed by Isaac Newton: “I have not been able to discover the source from which these errors arise.” This highlights the inherent difficulty in fully grasping the complex web of interactions driving systemic risk. The research demonstrates that behavioral responses, specifically panic, can outweigh fundamental financial weaknesses as a contagion vector. This fragility isn’t a flaw in design, but a consequence of the system’s inherent complexity – a system where seemingly minor shocks can propagate through the network, creating disproportionate consequences. The agent-based modeling approach attempts to illuminate these hidden dynamics, recognizing that simplification, while useful, inevitably comes at the cost of complete accuracy.

Where Do We Go From Here?

The presented work, while illuminating the interplay between geopolitical events and financial fragility within BRICS economies, subtly underscores a persistent question: what are systems actually for? The modeling suggests panic, a behavioral element, often outweighs underlying vulnerabilities. This isn’t a dismissal of quantitative metrics, but a pointed reminder that systems designed solely to optimize for stability can, paradoxically, amplify systemic risk when confronted with irrationality. The true challenge lies not in predicting the shock, but in understanding how the structure of the network incentivizes, or mitigates, cascading failure when confronted with it.

Future research must move beyond simply identifying contagion pathways. The focus should shift to designing networks that are resilient to behavioral shifts – systems that do not punish rational actors for anticipating instability. This demands a deeper integration of agent-based modeling with insights from behavioral economics and network theory. Furthermore, the current methodology, while powerful, remains inherently limited by the available data. Better, more granular, and real-time data streams are essential, but even these will not address the fundamental problem of incomplete information and imperfect foresight.

Simplicity, not as an aesthetic choice but as a disciplinary practice, is paramount. The pursuit of ever-more-complex models risks obscuring the essential dynamics at play. A truly useful model isn’t one that replicates reality, but one that reveals the underlying principles governing its behavior. This requires a relentless distillation of variables, a willingness to discard the accidental, and a clear articulation of the system’s ultimate purpose-a purpose that, for financial networks, remains surprisingly ill-defined.

Original article: https://arxiv.org/pdf/2512.20515.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2025-12-24 07:14