Stress-Testing the Global Economy: A New Resilience Index

Author: Denis Avetisyan

A novel composite index, the Gondauri Index, provides a diagnostic framework for assessing and benchmarking macro-financial stability across nations.

This paper introduces a reproducible, three-pillar index focused on inequality dynamics, liquidity transmission, and inflation forecasting for improved macro-financial resilience assessment.

Conventional macro-financial surveillance often lacks a nuanced understanding of resilience, treating its multi-dimensional components as readily substitutable. This paper addresses this limitation by introducing the Gondauri Index (GI), ‘A Diagnostics-First Composite Index for Macro-Financial Resilience to Socioeconomic Challenges: The Gondauri Index with Benchmarking and Scenario Evidence’, a reproducible framework for benchmarking resilience across economies via a diagnostic approach. The GI synthesizes inequality dynamics, liquidity transmission, and inflation forecasting into a unified $0-{100}$ scale, offering transparent attribution of resilience changes through its three-pillar structure. Will this diagnostic-first approach enable more proactive and targeted policy interventions to bolster macro-financial stability in the face of evolving socioeconomic challenges?

Beyond Silos: Mapping Interdependencies in Macro-Financial Resilience

Conventional evaluations of macro-financial resilience frequently operate in silos, assessing individual risks – such as bank solvency or sovereign debt – without fully accounting for their complex interdependencies. This fragmented approach overlooks crucial transmission channels where a shock in one sector can rapidly cascade across the entire financial system. For instance, a downturn in commercial real estate, traditionally viewed in isolation, can trigger liquidity strains at banks, exacerbate corporate defaults through credit linkages, and ultimately impact household wealth via investment portfolios. Consequently, these assessments often underestimate systemic risk, failing to identify vulnerabilities that emerge from the interactions between various financial institutions and markets. A more comprehensive framework is therefore needed to map these connections and accurately gauge the potential for contagion, moving beyond a purely component-by-component analysis towards a network-based understanding of financial stability.

Conventional measures of economic resilience often present a limited perspective, heavily weighting immediate stability while potentially overlooking crucial long-term consequences. These indicators frequently focus on benchmarks like GDP growth or unemployment rates, offering little insight into how economic benefits – or burdens – are distributed across a population. Consequently, a system can appear ‘resilient’ on the surface even as inequalities widen, creating vulnerabilities that threaten future systemic health. This prioritization of short-term gains obscures the potential for accumulating risks within the broader economic structure, as uneven distribution can stifle innovation, reduce social cohesion, and ultimately undermine the foundations of sustained prosperity. A truly robust assessment of resilience, therefore, demands a shift in focus – one that acknowledges the interconnectedness of economic health, equitable outcomes, and long-term systemic stability.

A comprehensive evaluation of macroeconomic resilience demands a shift beyond singular metrics, necessitating a novel framework capable of assessing interconnected risks across multiple dimensions. Current assessments often treat inequality, liquidity constraints, and inflationary pressures as separate concerns; however, these factors are deeply intertwined and can exacerbate systemic vulnerabilities. A robust framework would move beyond analyzing each in isolation, instead modeling their complex interactions and feedback loops to reveal emergent risks. Such an approach would allow for a more nuanced understanding of how shocks propagate through the financial system and real economy, identifying potential tipping points and informing proactive policy interventions designed to bolster long-term stability and equitable outcomes. By simultaneously considering these critical dimensions, policymakers can move from reactive crisis management to preventative resilience building.

A robust approach to financial resilience isn’t merely about weathering immediate crises, but actively shaping conditions to prevent them. Policymakers equipped with comprehensive assessments-ones that move beyond solely focusing on short-term gains-are better positioned to anticipate and mitigate emerging risks. This proactive stance allows for interventions that address underlying vulnerabilities, such as rising inequality or concentrated liquidity imbalances, before they escalate into systemic threats. By prioritizing long-term economic health and distributional outcomes alongside traditional stability metrics, governments can foster a more sustainable and equitable financial system, ultimately safeguarding against future instability and promoting widespread prosperity. The ability to foresee and address these interconnected factors is becoming increasingly vital in a rapidly evolving global landscape.

The Gondauri Index: A Holistic Benchmark for Resilience

The Gondauri Index functions as a benchmark for macro-financial resilience by combining indicators across three core areas: inequality, liquidity, and inflation. This composite approach moves beyond single-factor assessments to provide a more holistic view of systemic stability. Specifically, the Index integrates data related to income and wealth distribution, financial system liquidity – including measures of credit availability and market depth – and price stability as reflected by inflation rates. The resulting score allows for comparative analysis of resilience across different economies, identifying potential vulnerabilities and strengths in their macro-financial systems.

The Gondauri Index utilizes the ‘Inequality Resilience Score’ (IRS) to assess the stability of income and wealth distribution, moving beyond the limitations of the Gini coefficient. While the Gini coefficient provides a static snapshot of inequality at a single point in time, the IRS incorporates multiple distributional indicators and assesses their volatility over a defined period. This allows for the identification of economies where distributional metrics, even if currently at similar levels, exhibit significantly different degrees of stability. The IRS calculation considers both the level and the rate of change of key indicators, providing a more comprehensive understanding of an economy’s susceptibility to shocks impacting income and wealth distribution. This dynamic approach enables the Index to differentiate between economies with superficially similar inequality levels but divergent risks of social and economic instability stemming from distributional factors.

The Gondauri Index utilizes geometric aggregation to combine normalized indicator values – inequality, liquidity, and inflation – into a single composite score. This method, as opposed to arithmetic means, minimizes the impact of outliers and provides a more balanced representation of overall resilience. The resulting Index values are scaled to range from 0 to 100, allowing for direct comparative assessment of macro-financial resilience across benchmarked economies; a higher score indicates greater resilience. This standardized scoring facilitates cross-country comparisons and tracks resilience improvements or deteriorations over time.

Rolling window analysis, as implemented within the Gondauri Index, evaluates the temporal stability of constituent indicators – inequality, liquidity, and inflation – by calculating values over defined, shifting time periods. This technique moves beyond static assessments, identifying changes in resilience as conditions evolve. Specifically, a window of, for example, 36 months is applied, and advanced incrementally through the dataset; indicators are re-calculated for each window, allowing for the detection of trends and potential vulnerabilities that may not be apparent in single-point-in-time analyses. The resulting time series data reveals the dynamic nature of macro-financial resilience, highlighting periods of increasing or decreasing stability and enabling proactive identification of systemic risks.

Scenario Analysis: Identifying Binding Constraints on Resilience

Scenario analysis within the Gondauri Index utilizes a modeling framework to project potential economic resilience pathways under three distinct conditions: a Baseline scenario representing continued current trends, an Adverse scenario simulating negative economic shocks, and an Optimistic scenario reflecting positive policy interventions and favorable conditions. These projections are not predictions, but rather explorations of how key economic indicators – such as GDP, employment, and poverty rates – might evolve under each scenario. The modeling incorporates established economic relationships and allows for the systematic assessment of vulnerabilities and opportunities, providing a quantitative basis for evaluating the effectiveness of different policy responses across a range of possible future states. The outputs of this analysis are then used to identify the most significant factors limiting resilience under each scenario.

The Gondauri Index utilizes scenario analysis to identify ‘Binding Pillars’, which represent the most significant constraints hindering economic resilience. These pillars are determined by consistently appearing as critical limitations across multiple projected scenarios – Baseline, Adverse, and Optimistic – indicating they are not specific to a single economic condition. Identifying these dominant constraints allows policymakers to move beyond generalized strategies and focus interventions on the specific factors most limiting growth and stability. Prioritizing these Binding Pillars ensures that limited resources are allocated to address the bottlenecks with the greatest impact on overall economic performance and the ability to withstand future shocks.

The Gondauri Index facilitates the strategic sequencing of policy interventions by identifying critical bottlenecks – or binding pillars – that most significantly constrain economic resilience. This approach moves beyond simply identifying areas for improvement to prioritizing interventions based on their potential to unlock progress across multiple dimensions. By addressing the most impactful constraints first, policymakers can avoid allocating limited resources to interventions with marginal returns, and instead focus on initiatives that create enabling conditions for broader economic growth and stability. This sequenced approach ensures that each intervention builds upon the previous, maximizing the cumulative impact and accelerating progress toward a more resilient and equitable economy.

The Gondauri Index identifies binding constraints – the primary limitations hindering economic resilience – to facilitate targeted policy interventions. This process moves beyond generalized recommendations by pinpointing specific bottlenecks within an economy, such as inadequate infrastructure, limited access to finance, or skills gaps. By quantifying the impact of these constraints across different economic scenarios – Baseline, Adverse, and Optimistic – the Index provides a prioritized sequence for resource allocation. Addressing these binding pillars first demonstrably improves the effectiveness of subsequent interventions, leading to more robust and equitable economic outcomes and maximizing return on investment for policymakers focused on building long-term resilience.

Refining Forecasts and Fostering Data Accessibility

The Gondauri Index seeks to refine the precision of inflation predictions by strategically integrating established forecasting techniques with novel data-driven signals-a process known as hybrid model augmentation. Rather than replacing conventional methods, the Index enhances their performance by layering in supplementary indicators, effectively creating a more robust and responsive predictive framework. This approach acknowledges the inherent limitations of any single forecasting model and leverages the strengths of multiple sources to achieve greater coherence in inflation projections. By combining the stability of time-series analysis with the agility of contemporary signals, the Gondauri Index aims to provide a more nuanced and reliable assessment of future price levels, ultimately contributing to more informed economic decision-making.

Rigorous testing of the Gondauri Index involved its integration into several established forecasting models – an Autoregressive model of order one $AR(1)$ , a foundational Phillips-Perron Adaptive Smoothing (FPAS) baseline, and an enhanced FPAS model incorporating the ζ signal. Analysis revealed that the inclusion of ζ did not yield uniform improvements across all economic conditions; rather, its impact was heterogeneous, demonstrating a clear dependence on the prevailing economic regime. This regime dependence, while complex, ultimately translated into more accurate and reliable inflation projections, as the Index dynamically adjusts its weighting of the ζ signal based on contextual factors. The findings suggest that incorporating innovative signals, even with nuanced performance, can substantially refine forecasting capabilities when coupled with a robust understanding of the underlying economic landscape.

The Gondauri Index prioritizes scientific rigor through complete openness; all underlying data and methodological details are freely accessible via the Zenodo Repository. This commitment to transparency isn’t merely a matter of principle, but a deliberate strategy to facilitate independent verification and foster collaborative advancement in macroeconomic forecasting. By providing unrestricted access to the Index’s foundations, researchers can scrutinize the approach, replicate the results, and build upon the existing framework – ultimately strengthening the reliability and applicability of inflation projections. This open science approach encourages a community-driven refinement of the Index, allowing for broader participation and accelerating progress in understanding complex economic dynamics.

The Gondauri Index isn’t simply a forecasting tool; it embodies a commitment to the principles of open science, intentionally designed to accelerate progress in economic modeling. By making all underlying data and methodological details freely accessible through the Zenodo Repository, the Index invites scrutiny and collaborative improvement. This transparency allows researchers worldwide to independently verify the reported findings, replicate analyses, and build upon the existing framework. Such open access fosters a decentralized approach to model development, enabling a broader community to contribute to refinements and potentially uncover novel insights, ultimately leading to more robust and reliable inflation projections than would be possible through isolated research efforts.

Towards Future Resilience: Expanding the Index and Ensuring Rigor

The development of this manuscript benefitted from a rigorous review and editing process, incorporating the advanced capabilities of ‘Chatgpt5’ to refine both clarity and coherence. This wasn’t simply a proofread; the language model was strategically employed to identify and resolve ambiguities, streamline complex sentences, and ensure logical flow throughout the text. By leveraging this technology, researchers aimed to move beyond technical accuracy to achieve genuine accessibility, allowing a wider audience to engage with and understand the nuances of the study’s findings. The result is a manuscript designed not only to present robust research, but also to communicate it effectively, fostering broader impact and informed discussion.

A central tenet of this research involved prioritizing not only the accuracy of the findings, but also their broad dissemination and understanding. The manuscript benefited from a rigorous review and editing process, leveraging advanced language tools to refine clarity and coherence. This dedication to accessible communication intends to move beyond specialist circles, ensuring that policymakers, practitioners, and the general public can readily engage with the research. By presenting complex concepts in a straightforward manner, the study aims to maximize its impact and foster informed decision-making regarding macro-financial resilience, ultimately broadening the scope of its influence and potential applications.

Investigations are set to broaden the scope of the Gondauri Index beyond its current parameters, with a particular emphasis on integrating dimensions of resilience related to climate change adaptation. This expansion recognizes the increasingly critical interplay between macro-financial stability and environmental challenges, acknowledging that a nation’s ability to withstand economic shocks is inextricably linked to its preparedness for climate-related events. Future iterations of the index will therefore incorporate metrics assessing infrastructure robustness, disaster risk management capabilities, and the capacity for transitioning to a green economy, offering a more holistic and nuanced evaluation of a country’s overall resilience profile and its long-term sustainability.

This research significantly advances the field of macro-financial resilience by offering a nuanced perspective on systemic risk and vulnerability. The development of the Gondauri Index, and its underlying methodology, provides a practical tool for assessing a nation’s capacity to withstand economic shocks – going beyond traditional indicators to incorporate a broader range of factors. Consequently, policymakers now have access to a more robust and comprehensive framework for identifying potential weaknesses within the financial system, enabling the implementation of targeted interventions and preventative measures. The findings directly support evidence-based policymaking, facilitating the creation of more effective regulations and strategies aimed at bolstering economic stability and safeguarding against future crises, ultimately promoting sustainable and inclusive growth.

The Gondauri Index, as detailed in the paper, operates on the premise that understanding systemic vulnerability requires dissecting interconnected components – a principle echoing John Stuart Mill’s assertion that “It is better to be a dissatisfied Socrates than a satisfied fool.” The Index’s three-pillar structure-inequality, liquidity transmission, and inflation forecasting-doesn’t merely aggregate data; it aims to diagnose the patterns of resilience, or the lack thereof. This diagnostic approach, central to the Index’s design, allows for a continuous cycle of observation, hypothesis formulation regarding macro-financial stability, and experimentation through scenario analysis. The resulting benchmark, therefore, isn’t a static measure but a dynamic tool for interpreting systemic risks and informing policy interventions.

Looking Ahead

The Gondauri Index, as presented, is less a final answer than a deliberately overdetermined question. The construction itself-a composite index prioritizing diagnostic transparency-highlights the inherent difficulty in condensing complex macro-financial realities into a single metric. Errors in the index are not necessarily failures of prediction, but rather reveal the limits of current data and modeling approaches, particularly concerning the interplay between inequality, liquidity traps, and inflationary pressures. The value lies not in the number itself, but in the patterns revealed when the index fails to capture observed phenomena.

Future iterations should explore non-linear dynamics and feedback loops more explicitly. The current framework treats each pillar – inequality, liquidity, and inflation – as largely independent, despite demonstrably intricate connections. A crucial extension would involve stress-testing the index against historical anomalies-events that existing models consistently misprice-to assess its robustness and identify blind spots. Such analysis could reveal previously unconsidered systemic vulnerabilities.

Ultimately, the index’s true test will not be its predictive power, but its utility as a diagnostic tool for policymakers. The framework invites a shift from forecasting ‘the’ future to understanding the range of plausible futures, and identifying policy levers that can nudge systems toward more desirable states. The challenge now is to move beyond benchmarking and toward active experimentation with the index’s components as policy variables.

Original article: https://arxiv.org/pdf/2604.12368.pdf

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