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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,695 papers · 148 categories

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3570105140 · Jun 202019922001200920172026
48 results for economic stability

Emerging economies use countercyclical policies to manage crises and dominant currencies.

problem Managing economic crises and fluctuations in dominant currencies like USD and EUR.
method Theoretical analysis, case studies, econometric modeling.
result Emerging economies can stabilize growth with countercyclical monetary policies.

In this paper, simple mathematical models from Control Theory are applied to three very important economic paradigms, namely (a) minimum wages in self-regulating markets, (b) market-versus-true values and currency rates, and (c) government spending and taxation levels. Analytical solutions are provided in all three par…

2013-03-23abs ↗pdf ↗

Study adds memory effect to Solow-Swan model for more accurate economic growth modeling.

problem Inaccuracies in classical Solow-Swan model in capturing long-term dynamics.
method Introduced fractional calculus with Caputo derivative into Solow-Swan framework.
result Fractional-order model shows significant impact on capital accumulation and stability.

Italy and the Eurozone are heading in the year 2012 into a financial depression of unprecedented magnitude, with a forthcoming multitude of often contradictory public economic and financial stability emergency interventions whose ultimate endogenous and exogenous effects on public and private health spending and on the…

2012-07-26abs ↗pdf ↗

This paper argues that the fundamental principle of contemporary financial economics is balanced reciprocity, not the principle of utility maximisation that is important in economics more generally. The argument is developed by analysing the mathematical Fundamental Theory of Asset Pricing with reference to the emergen…

2013-10-10abs ↗pdf ↗

Study examines Fed's pandemic communication strategies.

problem Analyzing Federal Reserve's communication during the COVID-19 pandemic.
method Sentiment analysis, topic modeling, comparative analysis of previous crises.
result Fed's communication during the pandemic focused on financial stability, market volatility, social welfare, and unconventional monetary policy.

The paper analyzes how mutable blockchain protocols affect miner behavior and strategic stability.

problem The mutability of blockchain protocols undermines long-term planning and cooperative equilibria.
method Integrates Austrian capital theory with repeated game theory to examine miner behavior under different institutional conditions.
result Effective time preference increases when protocol rules are mutable, leading to political rent-seeking and undermining strategic coherence.

GNN improves financial risk detection in dynamic networks.

problem Complex, changing financial networks make traditional risk identification methods ineffective.
method Graph Neural Networks (GNN) for embedded representation learning of financial data.
result GNN enhances the detection of hidden risks and abnormal behaviors in financial networks.

As we show using the notion of equilibrium in the theory of infinite sequential games, bubbles and escalations are rational for economic and environmental agents, who believe in an infinite world. This goes against a vision of a self regulating, wise and pacific economy in equilibrium. In other words, in this context, …

2013-05-01abs ↗pdf ↗

Deep-MacroFin uses neural networks to solve complex economic models efficiently.

problem Solving high-dimensional partial differential equations in continuous time economics.
method Leverages deep learning, specifically Multi-Layer Perceptrons and Kolmogorov-Arnold Networks, optimized with HJB equations.
result Offers a more efficient solution (5imes imes less memory, 40imes imes fewer FLOPs) for 50D economic models.

Although classical economic theory is based on the concept of stable equilibrium, real economic systems appear to be always out of equilibrium. Indeed, they share many of the dynamical features of other complex systems, e.g., ecological food-webs. We focus on the relation between increasing complexity of the economic n…

2010-09-06abs ↗pdf ↗

We address the problem of banking system resilience by applying off-equilibrium statistical physics to a system of particles, representing the economic agents, modelled according to the theoretical foundation of the current banking regulation, the so called Merton-Vasicek model. Economic agents are attracted to each ot…

2011-03-03abs ↗pdf ↗

This paper introduces TDA and TSI for better business analytics.

problem Nonlinear, multi-scale business datasets under-represented by traditional tools.
method Topological Data Analysis (TDA) and Topological Stability Index (TSI).
result TSI reveals structural variability in business data.

Study finds relevance of exchange and inflation rates to economic factors.

problem Determining the relevance of exchange and inflation rates to economic factors.
method Introduced concept of adequacy, established positive relation between exchange and inflation rates and other economic factors.
result Close positive relation found between exchange and inflation rates and other economic factors.

Study uses Perelman and Ricci flow methods to analyze economic inequality.

problem Impact of socio-economic challenges and technological progress on economic inequality.
method Perelman model and Ricci flow methods.
result Technological innovations and social protection programs reduce inequality.

Examines how extending home loan durations affects French households financially.

problem Financial implications for households with extended home loan durations.
method Analysis of French and international home loan systems, including bullet loans and Japanese home loans.
result Extending home loan durations can reduce monthly payments but raises financial risks.

Deep RL solves complex economic models with heterogeneous agents.

problem Solving models with heterogeneous economic actors is difficult.
method Reinforcement Learning techniques for solving general equilibrium models.
result Successfully captures economic behaviors induced by age-based health risks.

In the current era of worldwide stock market interdependencies, the global financial village has become increasingly vulnerable to systemic collapse. The recent global financial crisis has highlighted the necessity of understanding and quantifying interdependencies among the world's economies, developing new effective …

2014-08-03abs ↗pdf ↗

Machine learning predicts US and EuroZone business cycles with high accuracy.

problem Predicting the business cycle phases in US and EuroZone.
method Three machine learning approaches were compared: Multinomial Logistic Regression (MLR) achieved the best results.
result MLR achieved 65.25% accuracy for EuroZone and 75% for US in predicting business cycle phases.

Unified framework deciphers global central bank communications.

problem Misinterpretations of central bank communications can disproportionately impact vulnerable populations.
method Developed the World Central Banks (WCB) dataset, annotated and reviewed sentences, defined tasks, and benchmarked models.
result A model trained on aggregated data across banks outperforms models trained on individual bank data.

We propose a modelling framework for the optimal selection of crypto assets. Crypto assets differ by two essential features: security (technological) and stability (governance). Investors make choices over crypto assets similarly to how they make choices by using a recommender app: the app presents each investor with a…

2019-06-23abs ↗pdf ↗

Model forecasts hourly electricity demand influenced by weather, socio-economic, and political factors.

problem Accurate hourly electricity demand forecasting in the face of multifaceted uncertainties.
method Interpretable probabilistic mid-term forecasting model using Generalized Additive Models (GAMs).
result Highlights vulnerability of countries to extreme weather scenarios under electric heating adoption.

The paper stabilizes PD term structures under forecast uncertainty using a Kalman filter with an anchored observation model.

problem Stable estimation of lifetime PDs under forecast uncertainty.
method Reformulated in state-space framework, introduced an anchored observation model.
result Asymptotic stochastic stability of error dynamics, leading to smoother projections.

Unified framework for DRO and DTA using Bayesian nonparametrics.

problem Combining DRO and DTA under ambiguity.
method Unified framework using DP and HDPs, with outlier robustness.
result Favorable performance in prediction accuracy and stability.

Economic growth is unpredictable unless demand is quantified. We solve this problem by introducing the demand for unpaid spare time and a user quantity named human capacity. It organizes and amplifies spare time required for enjoying affluence like physical capital, the technical infrastructure for production, organize…

2012-06-12abs ↗pdf ↗

News sentiment in U.S. economic newspapers has become more persistent over 45 years.

problem Understanding the temporal dynamics of U.S. economic news sentiment over time.
method Daily economic news sentiment index from 1980-2025, analyzed using sentiment indexes.
result News sentiment states have become more persistent, with longer residence times in optimistic or pessimistic regimes.

Digitwashing gap boosts stock crash risk, study finds.

problem The gap between companies' digital promises and actual performance increases stock crash risk.
method Empirical analysis of Shanghai and Shenzhen A-share companies from 2010 to 2021, robustness tests conducted.
result GDT significantly increases stock price crash risk, confirmed by robust tests.

Unified framework improves option pricing accuracy and stability.

problem Combining structured knowledge with data for better financial modeling.
method Structured-Knowledge-Informed Neural Networks (SKINNs) that embed theoretical insights into neural networks.
result SKINNs improve out-of-sample valuation and hedging performance in financial applications.