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on Computational Economics |
| By: | Manuel Naviglio; Fabrizio Lillo |
| Abstract: | Understanding the joint dynamics of prices and trades is central to market microstructure, where returns and order flow interact through nonlinear and state-dependent mechanisms. Linear models are interpretable but may miss these effects, while deep neural networks improve forecasting at the cost of transparency. We use neural networks as tools for structural discovery rather than only for prediction. A deep feed-forward network is trained on high-frequency returns and signed volumes for large- and small-tick stocks and compared with a linear VAR benchmark. The neural network improves predictive performance, especially for returns, revealing nonlinear dependencies beyond the linear specification. Using Shapley-based explainability, we show that the dominant contributions are concentrated at the most recent lags. Model-implied responses are consistent with conditional averages reconstructed from the data. Unlike empirical averages, however, the neural-network decomposition isolates individual regressor contributions to the aggregate dependence. Lagged signed volume generates sign-preserving and saturating effects, consistent with nonlinear price impact and order-flow persistence. Lagged returns act as state variables: when the previous trade does not move the price, the model predicts continuation in the direction of past order flow, whereas non-zero returns generate attenuation or reversal. Building on these findings, we introduce a parsimonious SHAP-inspired nonlinear parametric model. It reproduces the main return-volume dependencies, outperforms the linear VAR benchmark, and achieves performance comparable to the neural network. A multi-lag extension captures residual longer-memory effects while preserving interpretability. Overall, explainability offers a route from black-box prediction to economically meaningful parametric models of price and trade dynamics. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.06085 |
| By: | Haochen Li; Xinshuai Guo; Jingdong Ouyang; Wei Zhang; Leilei Shi |
| Abstract: | We introduce a probability-wave framework for modeling the collective behavior of interacting adaptive agents, deriving testable eigenmodes through a generalized behavioral intelligence (GBI) nonlocal probability-wave equation. This framework captures a broad range of human intelligence behaviors with analytical mechanisms and offers an indirect method to examine the Liu-Chen-Ao (LCA) hypothesis of nonlocal entangled nerve fibers in the brain through collective trader behaviors. Our empirical analysis of Chinese intraday stock market data demonstrates that adaptive entangled game modes explain 82-94% (89% overall) of observed decision patterns, a sharp contrast to the predictions of neoclassical finance based on independent rational agents. Moreover, 2-12% of behaviors show adaption to intraday news, events, and environments, characterized by dual equilibrium states and abrupt reference point shifts, while purely independent modes occur in less than 5% of cases. These findings empirically support the LCA hypothesis, as observable trading behaviors reflect underlying brain mechanisms and internal intelligence decision-making in behavioral psychology. Our results highlight the necessity of incorporating adaptive entangled game modules into artificial general intelligence (AGI) architectures, addressing the limitations of conventional artificial neural network (ANN)-based AI, which relies on trillions of opaque parameters. By integrating ANN-based AI with probability-wave-based entangled-brain simulations, machine learning can enrich AGI foundation models (FMs) and facilitate the development of human-like processing units (HPUs) that leverage brain-inspired mechanisms. Such HPUs may ultimately create more compact, efficient, and robust AGI systems, particularly for embodied intelligence and robotics. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.09226 |
| By: | Nneka Umeorah; Tolulope Fadina |
| Abstract: | This paper develops a unified framework for assessing systemic risk and identifying contagion channels in the global banking system using a Temporal Heterogeneous Multiplex Graph Neural Network. We construct a harmonised quarterly panel combining bank fundamentals, CDS spreads, and macroeconomic indicators, and represent these data as dynamic multiplex networks linking banks through financial similarity and liquidity co-movement, augmented with country-level macroeconomic relationships. The model integrates graph convolutional layers with recurrent GRU dynamics and incorporates a learnable fusion gate to capture time-varying reliance on alternative contagion channels. Empirical results show that the framework outperforms conventional econometric, machine learning, and graph-based benchmarks for short-term changes in CDS spreads. Beyond forecasting, we provide an interpretable framework to quantify bank-level systemic importance via stress testing, assess country-level spillovers under macroeconomic shocks, and uncover transmission pathways through edge perturbation analysis. Robustness tests confirm the stability of both predictive accuracy and systemic risk rankings. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.27295 |
| By: | Kunjesh Parekh; Anil Kumar Tiwari; Divya Saxena |
| Abstract: | Calculation-intensive financial question answering requires exact reasoning over structured rates, temporal conditions, numerical formulas, and rule-based constraints. Although Large Language Models (LLMs) perform strongly on natural language tasks, they often produce numerically incorrect yet plausible answers when solving multi-step financial calculations. To address this limitation, we introduce CIFQA (Calculation-Intensive Financial Query Answering), a deterministic tool-grounded multi-agent LLM framework for financial question answering. CIFQA separates language understanding from numerical execution by assigning specialized agents to query interpretation, routing, parameter extraction, computation planning, and response generation, while deterministic Python-based tools perform financial calculations and rule application. We instantiate CIFQA for fixed deposit query answering and evaluate it on a curated benchmark of fixed deposit queries. CIFQA achieves 95.54% accuracy on calculation-intensive queries and 90.87% overall accuracy, substantially outperforming direct LLM baselines even when provided with complete formulas, rate cards, and benchmark instructions. Ablation studies show that deterministic components such as exact rate lookup, tenure computation, rolling-year adjustment, and premature-withdrawal logic are critical contributors to performance. Notably, a 17B open-source backbone operating within CIFQA outperforms substantially larger frontier models evaluated with the same financial information, demonstrating that architectural design is a more important determinant of numerical reliability than model scale. While evaluated on fixed deposit queries, CIFQA provides a generalizable framework for calculation-intensive financial reasoning tasks. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.26114 |
| By: | Pierre Brugière (CEREMADE - CEntre de REcherches en MAthématiques de la DEcision - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres - CNRS - Centre National de la Recherche Scientifique) |
| Abstract: | We present the theory of Statistical learning (Supervised and Unsupervised Learning) and some applications in Finance. |
| Keywords: | Vapnik Chervonenkis, SVM, SVR, Supervised Learning, Unsupervised Learning, Ridge Regression, Lasso Regression, XG Boost, Kernel methods, Yield curve modeling |
| Date: | 2026–08–30 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05734318 |
| By: | Marouane Battach (L2S - Laboratoire des signaux et systèmes - CentraleSupélec - Université Paris-Saclay - CNRS - Centre National de la Recherche Scientifique); Salah Eddine Elayoubi (L2S - Laboratoire des signaux et systèmes - CentraleSupélec - Université Paris-Saclay - CNRS - Centre National de la Recherche Scientifique); Tania Jimenez (LIA - Laboratoire Informatique d'Avignon - AU - Avignon Université - Centre d'Enseignement et de Recherche en Informatique - CERI); Linda Salahaldin (ESCE PARIS, ECE Engineering School - OMNES Education) |
| Abstract: | As the crowdfunding industry grows increasingly competitive, platform managers lack systematic, datadriven tools to move beyond ad hoc decision-making toward an informed management of their platforms. We propose a CFP management framework that leverages Machine Learning (ML) and data analytics to enhance the aggregate success rate. In particular, we provide managers with an explainable Artificial Intelligence (xAI) tool that links success to key parameters of the platform's state. We compare several ML algorithms and combine correlation analysis (SHAP) with causality testing (Granger) to identify the most influential and actionable parameters. The management framework builds on this xAI analysis for controlling the success rate: a predictor forecasts the CFP state while a controller proposes an optimized campaign launch schedule to increase the aggregate success rate. We show the performance of a controller based on a Genetic Algorithm (GA) on a large-scale simulator built on real platform data. Our results illustrate how explainable and actionable AI enable the platform manager to understand the dynamics of its platform and control it for a better performance. |
| Keywords: | Data-driven Decision-making, Success Prediction, Platform Management, Crowdfunding |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05744861 |
| By: | Sheryan Kumar |
| Abstract: | Classical option-hedging methods like Black-Scholes delta assume constant, free rebalancing, which real markets don't allow. Deep hedging trains a neural network to handle these frictions directly, and prior work reports strong results. But those comparisons usually pit deep hedging against a frictionless classical baseline on simulated price data. That's not a fair fight, and it leaves open whether the advantage is real. We test this using five years of actual BTC options data from Deribit (2020-2024), comparing Black-Scholes delta, Leland's cost-adjusted hedge, and the Whalley-Wilmott no-trade band against three deep hedging setups: an LSTM and a feedforward network, each trained with a CVaR loss and, in some runs, a penalty for trading too often. All six strategies face the same 5 basis point transaction cost. On 11, 546 test episodes from September 2023 to December 2024, Whalley-Wilmott cuts transaction costs significantly versus hourly rebalancing, saving $1.79 per episode against plain BS delta (95% CI [-2.21, -1.39], p |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.29025 |
| By: | Gijs A. F. Niewzwaag; Marijn G. S. Veth; Manuele Massei; Marcos R. Machado |
| Abstract: | Machine learning-based credit scoring is increasingly central to Peer-to-Peer (P2P) lending, yet its resilience to adversarial manipulation, where applicants strategically alter self-reported inputs to secure favourable decisions, remains poorly understood. Most adversarial-robustness evidence comes from image and text domains and evaluates a single attack against a matching defence, offering little guidance on how defences generalise across attack types in tabular credit data. We address this with a systematic train-test robustness benchmark on a large Lending Club subset, spanning three model families (logistic regression, a feed-forward neural network, and a transformer for tabular data) and four attacks confined to applicant-mutable features: Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD), Salt-and-Pepper (S&P) noise, and DeepFool, plus a mixed-attack regime. Across a full grid evaluated with stratified cross-validation, adversarial training sharply improves robustness against the attack it is trained on and transfers well within the gradient-based family, but transfers weakly to non-gradient corruption, so single-attack defences overstate real-world resilience. Mixed training delivers the most balanced robustness across heterogeneous attacks while preserving clean-test performance, supporting multi-attack stress testing in credit-model governance. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.09945 |
| By: | Usman Ahmed; Jason Hawkins |
| Abstract: | Land use and transportation infrastructure are tightly linked systems, and residential property prices play a central role in transportation planning. Transportation investments also influence property values, making accurate forecasting of both systems an interconnected research challenge. This study examines these interactions and evaluates machine learning methods for modeling residential real estate prices across contrasting urban contexts. We use XGBoost and Random Forest models to assess how land use and transportation infrastructure shape dwelling prices, comparing results between the Rawalpindi and Islamabad Metropolitan Area in Pakistan and the City of Toronto in Canada. We also examine the performance of machine learning models on spatial data by comparing nonspatial and spatial cross validation, and use SHAP values to interpret feature impacts. Despite differences in demographics and economic development, both cities show similar effects of transportation infrastructure and local amenities on prices. Proximity to major urban cores increases sale price, while high quality transit such as subway and bus rapid transit raises prices and conventional bus stop proximity lowers them. Nonspatial cross validation overestimates predictive accuracy for spatial datasets. XGBoost performs slightly better than Random Forest. We recommend careful application of machine learning methods when modeling spatially dependent land price data. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.00375 |
| By: | Parsa Yousefnezhad; Gholamreza Mansourfar; Mohammadreza Feizi Derakhshi |
| Abstract: | Bitcoin's future fluctuations are a substantial concern for investments and risk management. Investors and financial institutions require accurate forecasts of these price movements to hedge and optimize portfolios. This study aims to answer the question of whether Bitcoin's price will rise beyond 5% within the next 7 days by utilizing on-chain, market, and sentiment data from February 2018 to December 2025. The proposed model consists of a multi-scale temporal convolutional network with InceptionTCN blocks, CNN channel attention, adaptive average pooling, and a pairwise ranking loss. Dilated convolutions with bottleneck and fusion layers are employed to efficiently capture features over horizons from 1 to 4 days. Given the class imbalance in the dataset, AUC is used instead of accuracy and other classification metrics to reflect the model's performance better. Subsequently, a profit-optimized decision threshold is also applied to align model selection with financial objectives. The proposed model is compared with 5 other baselines: ImprovedTCN_GRU, LSTM, TCN, XGBoost, and Random Forest. Results indicate that the proposed model achieved an AUC of 0.6316 and a profit of 1.703, outperforming all baseline models. Using a novel deep learning model would assist investors in making better financial decisions. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.26174 |
| By: | Andrea Molent; Michel Vellekoop |
| Abstract: | We propose a neural calibration method to construct a recombining binomial tree directly from a set of given option prices. Rather than estimating a continuous option pricing function or a local volatility surface as an intermediate object, a neural network is used to deform a benchmark lattice. This leads to a discrete pricing model which is guaranteed to be arbitrage-free, complete, easy to interpret, and can be used directly for pricing and to find replicating trading strategies. Calibration is formulated as a penalized optimization problem that combines a repricing error with an admissibility penalty, and an optional spatial regularization term based on implied local volatilities. Numerical experiments on synthetic and SPX market data show that the proposed approach yields accurate repricing and is very competitive when compared to recently proposed other neural calibration methods. It preserves the computational advantages of lattice-based valuation and hedging. In particular, the calibrated tree can be reused to price contracts that allow early exercise, and could even be calibrated directly with American option prices. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.30867 |
| By: | Samira Amiriyan; Youness Boutaib |
| Abstract: | The computation of Black-Scholes implied volatility is a fundamental task in quantitative finance, underpinning option valuation, model calibration and risk management. Although implied volatility is routinely used in practice, the inversion of the Black-Scholes pricing formula remains a challenging numerical problem, particularly in asymptotic regimes corresponding to extreme option prices, strikes or maturities, where the inverse map becomes highly sensitive to perturbations of the price. In this paper, we introduce a new family of asymptotically-informed neural-network architectures for implied-volatility computation. Exploiting the distinct behaviours of the Black-Scholes pricing function in different volatility regimes, we propose a family of architectures that learn a trainable partition of the price-log-moneyness domain through a system of gating functions and combines specialised local approximations of the implied-volatility function within each region. Extensive numerical experiments demonstrate that the proposed models consistently outperform standard feed-forward neural networks across a wide range of parameter domains, often by several orders of magnitude in relative accuracy while maintaining excellent generalisation properties. Furthermore, the neural-network outputs provide highly accurate initial guesses for a third-order Householder scheme, allowing near machine-precision implied-volatility computations after only two refinement iterations. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.05491 |
| By: | Hesandi Mallawarachchi; Senilka Madurapperumage; Nadil Kulathunge; Thilokya Angeesa; Nethsith Gunaweera; Sandeepa Weerasekara; Patalee Narasinghe; Nisansa de Silva; Sandareka Wickramanayake |
| Abstract: | The Colombo Tea Auction (CTA) plays a vital role in determining global tea prices, yet the relationship between local weather conditions and price behavior across different tea catalogues has not been thoroughly explored. In this study, we develop a novel, structured dataset by extracting information from 105 weekly broker reports spanning late 2023 to 2026, and combined with region-specific weather data. Our analysis focuses on four main tea catalogues of Sri Lankan tea: High Grown, Low Grown, Off-Grade, and Dust. To better understand the factors influencing tea prices, we apply Granger causality analysis alongside tree-based machine learning models: Random Forest, XGBoost, LightGBM, and Gradient Boosting. Our results show that while market dynamics are primary drivers, weather conditions also have significant effects. Notably, Low Grown tea shows strong sensitivity to precipitation and sunshine duration (p |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.24894 |
| By: | Ricardo Alonzo Fern\'andez Salguero |
| Abstract: | This study evaluates alternative designs for reforming Bolivia's liquefied petroleum gas subsidy using a machine-learning agent-based microsimulation framework. The analysis harmonizes household survey data, expenditure data, demographic and health information, and monthly hydrocarbon production and commercialization series to simulate fiscal savings, poverty effects, energy substitution, administrative costs, and social risks under multiple reform scenarios. The model compares uncompensated subsidy removal, fixed energy transfers, full compensation for vulnerable LPG users, voucher-based compensation, maternal-child transfers, clean-energy transition kits, and hybrid policy packages. Machine-learning models are used to learn household vulnerability, fuel-use patterns, food insecurity risk, and behavioral propensities that feed into a monthly agent-based simulation. The results show that eliminating the subsidy without compensation generates the largest fiscal savings but increases poverty, extreme poverty, and pressure toward solid-fuel substitution. Full monetary compensation for Q1-Q2 LPG users substantially reduces social harm while preserving significant fiscal savings and dominates an equivalent voucher design under normal market conditions because of lower administrative friction. The most socially robust design combines targeted monetary energy compensation, maternal-child reinforcement, and clean-energy kits for households using solid fuels. The findings support a gradual replacement of the universal LPG subsidy with targeted, administratively lean, and behaviorally informed compensation mechanisms. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.00414 |
| By: | Alex Chen |
| Abstract: | We study statistical predictability in daily U.S. equity prices using only information available at the market open. Using SPY from February 1, 1993 through March 15, 2024, we benchmark XGBoost against Random Forest, LightGBM, Logistic Regression, and naive baselines under expanding-window walk-forward validation. After observing the current day's opening price and two lagged target-specific prices, the task is to predict whether the same day's close will be above or below the previous day's close. On the last 800 trading days, Logistic Regression attains the highest close-direction accuracy (71.09%), Random Forest reaches 61.20%, and XGBoost reaches 58.45% with a 95% bootstrap confidence interval of [54.94%, 62.08]. For XGBoost, close-direction accuracy rises to 72.7% when the predicted move exceeds 1%, but the usable sample falls to 154 observations. We also report Diebold-Mariano and McNemar tests, regime-specific results, SHAP feature importance, and an auxiliary 541-equity screen. The evidence supports a narrow statistical-finance conclusion: simple daily equity features contain detectable same-day directional information, but the result should be interpreted with careful sample-size accounting and limited economic claims. |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.26106 |
| By: | Vazquez Torres Estefania; Magliozzi Chiara (European Commission - JRC); Caivano Arnaldo (European Commission - JRC); Cardoso Ana Cristina (European Commission - JRC) |
| Abstract: | The introduction and spread of invasive alien species (IAS) have become a significant concern worldwide, disrupting environmental balance, economic stability, and human health across various activity domains. Effective management of IAS requires a cross-sectoral approach and identification of direct and indirect effects on multiple activity domains. This study explores the potential of artificial intelligence (AI) to support automatic literature reviews and to evaluate IAS impacts. The study analysed 498 IAS impacting 27 activity domains and demonstrated the potential for semi-automation to enhance efficiency, the importance of expert involvement, and the value of AI in extracting contextualized information. The study highlights the feasibility of replicating the assessment on IAS using AI-powered approaches but also emphasizes the need for careful consideration of data quality, algorithmic bias, and the role of subject matter experts in validation and refinement. While we were able to successfully replicate the analysis, the process also revealed technical limitations that should be considered when applying this approach in practice. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:ipt:iptwpa:jrc146630 |
| By: | Yonghong Zhang; Ricardo Correia; Isabel M. Parra; Yong Xie |
| Abstract: | Existing causal-inference benchmarks for LLMs mostly score method descriptions or whether generated code runs, not whether the executed workflow recovers the target causal estimate. CausalVerify studies this verification problem for structured econometric causal-estimation workflows by separating realistic interpretation from verifiable computation. It pairs 259 published economics papers (reconstructed research question, data description, institutional context) with 100 fixed-seed synthetic scenarios that realise CSV datasets for difference-in-differences, event study, instrumental variables, and regression discontinuity designs. Experiment A (real-paper text agreement) scores method-family and direction agreement against four-LLM consensus labels. Experiment B (synthetic execution) runs model-written R code and checks whether the extracted treatment-effect estimate matches a canonical estimator on the same realised dataset; this execution-grounded correctness layer is L2b+, distinct from L2b, which records only whether the code executes. A calibration arm asks whether self-reported confidence separates correct from incorrect workflows. On Experiment B, seven LLMs reach L2b+ pass rates of 10% to 88% at the default 50% tolerance, and 66 of the 426 workflows that execute (15.5%) return a wrong estimate. Execution ranking (L2b) agrees with L2b+ far better than text-direction scoring (L4): Kendall $\tau=0.81$ and Spearman $\rho=0.93$, versus Kendall $\tau$ between $-0.20$ and $0.10$ for L4. Llama-3.3-70B-Instruct shows the same qualitative gap, and reported confidence does not reliably separate correct from incorrect workflows. The claims are confined to standardized single-shot workflows in these four design families under the evaluated R backend and model panel; the benchmark does not measure general causal-inference ability. Code, data, cached outputs, and a datasheet are released. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.07944 |
| By: | Richard Dennis |
| Abstract: | This paper describes SolveDSGE, a comprehensive Julia package designed to solve and analyze nonlinear Dynamic Stochastic General Equilibrium models. Although the package supports standard perturbation methods up to the fourth order, its originality and primary contribution resides in its implementation of various projection methods - including Chebyshev, Smolyak, hyperbolic-cross and piecewise linear approximations - to achieve global accuracy. By providing a unified interface that allows interchangeable solution initialization (homotopy) the framework significantly advances our ability to solve nonlinear models. We demonstrate the utility of the package through a series of applications, highlighting its efficiency and ease of use in modern macroeconomic research. |
| Keywords: | DSGE models, projection methods, perturbation methods, Julia |
| JEL: | E42 E44 E52 E58 E61 |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:een:camaaa:2026-79 |
| By: | YiLi Chien; Hyungsuk Lee; Junsang Lee |
| Abstract: | Can governments use a carry-trade strategy to offset the fiscal costs of population aging? We examine this question through the consolidated balance sheet of Korea’s public sector, combining the assets and liabilities of the government, public pension and investment funds, and the central bank. The consolidated balance sheet reveals increasing reliance on a carry trade in which risky, increasingly foreign assets are financed with safe, low-yielding domestic liabilities. This strategy has generated a favorable return spread and strengthened public net worth. However, present-value accounting shows that projected government and pension deficits substantially exceed the public sector’s accumulated financial wealth. A VAR-based simulation similarly indicates that persistent structural deficits eventually overwhelm the returns from public assets and drive consolidated net worth deeply negative. Financial repression can delay this deterioration but cannot restore fiscal sustainability; moreover, it shifts the cost disproportionately to less financially sophisticated depositors. Neither the carry trade nor financial repression can substitute for fiscal and pension reform. |
| Keywords: | consolidated balance sheet; carry trade; financial repression; fiscal sustainability |
| JEL: | E58 E62 E63 G11 G12 H63 |
| Date: | 2026–08–29 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedlwp:103774 |
| By: | Aleksandar Arandjelovic; Pavel V. Shevchenko; George Tzougas |
| Abstract: | Bayesian inference in compound loss models must often be repeated across policies, market scenarios, and prior specifications. Outside conjugate cases, this may require repeated numerical integration or Markov chain Monte Carlo (MCMC). We formulate this problem as amortized posterior approximation and construct a conditional Wasserstein generative adversarial network conditioned on sufficient statistics, prior mean and coefficient of variation, and mixture weights of prior families. Notably, a single shared generator is able to approximate the posterior laws of both the Poisson intensity and the Pareto shape parameter under mixtures of Gamma, inverse-Gaussian, and lognormal priors. We assess the approximation by simulation-based calibration and by comparisons with analytical posteriors, deterministic quadrature, and extensive MCMC simulations. In an application to data on extreme natural catastrophe losses, we produce rolling one-year posterior predictive distributions, and examine the effects of heavy-tailed severity and prior-family uncertainty on aggregate tail risk. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.27229 |
| By: | Van Herck Kristine; Princen Savina; Serruys Hannes (European Commission - JRC); Depoortere Arne (European Commission - JRC) |
| Abstract: | The employment rate in Belgium remains low, partly reflecting features of the tax and benefit system that weaken work incentives. More specifically, the benefit system in Belgium provided – until 1 January 2026 – unemployment benefits of unlimited duration, which made Belgium the only EU Member State in which unemployment benefits were not limited in time. As this feature may have discouraged benefit recipients from taking up work, the federal government adopted in 2025 a long-awaited reform introducing a maximum duration for unemployment benefits. Unemployment benefit recipients affected by the reform are expected to re–enter employment, while the most vulnerable long–term unemployed people can rely on minimum income support. Based on micro-simulations using the EUROMOD model, this economic brief analyses the budgetary and distributional effects of the unemployment benefit reform under different labour supply assumptions, taking into account the interaction with the minimum income support system. The analysis finds that, even in the absence of labour supply effects, the reform is expected to generate net budgetary savings. However, when potential labour supply effects are taken into account, the net budgetary savings are significantly higher (up to EUR 2.1 billion). The overall impact on poverty and inequality is expected to be limited due to the cushioning effect of the minimum income support system and the potential increase in employment. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:ipt:iptwpa:jrc147863 |
| By: | Jihwan Woo |
| Abstract: | Coupled feedback networks are often monitored channel by channel even though cross-channel paths alter both stability margins and transmitted disturbances. We study identification of a structured feedback matrix L_t = Phi diag(gamma_t) in an output-only setting: no commanded, probing, or reference input exists -- only temporally separated outputs and the scheduling gains gamma_t are observed, while the coupling response Phi and the clearing-window inputs are not. Identification rests jointly on the persistent excitation of the observed pre-window output and on two structural features separating coupling from confounds: the known time variation of the gains, which modulates the closed-loop response in a predictable pattern, and a partial-reversal moment by which a known fraction of transient displacement is corrected in a subsequent window. We give a hierarchy of results: exact local identification of the coupling under a Jacobian rank condition on the gain regimes; a first-order interaction estimator whose identification strength is the minimum eigenvalue of the residualized interaction information matrix (provably unidentified under constant gains); and a characterization of the estimand as a resolvent sensitivity -- the right object for screening transmitted disturbances and a first-stage input to spectral-margin recovery -- with sqrt(T) asymptotics for the first-order estimator, a cross-identification theorem mapping each varying gain to exactly identified resolvent rows and columns, and bootstrap validity under consistent selection; the implemented heuristic's empirical coverage (90% at nominal 95%) quantifies the remaining gap. Simulations verify sharpness of the rank condition and quantify benchmark failures under confounding. A case study on leveraged-fund rebalancing feedback, where daily fund disclosures play the role of the known gains, illustrates the method on real data. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.25844 |