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on Risk Management |
| By: | Acharya, Viral; Brunnermeier, Markus; Pierret, Diane |
| Abstract: | We assess the efficacy of market-based systemic risk measures that rely on U.S. financial firms’ stock return co-movements with market- or sector-wide returns under stress from 1895 to 2023. Stress episodes are identified using corporate bond spread widening and narrative dating, spanning from the Panic of 1907 to the Banking Stress of 2023. Measures observed prior to the onset of stress episodes predict market outcomes (realized volatility and returns), balance sheet outcomes (lending, profitability, and run risk), and bank failures. Specifically, the measures are: (i) particularly effective in capturing the cross-sectional ranking of institutions conditional on a stress episode, rather than aggregate outcomes; (ii) more informative when stress episodes are severe; and (iii) relevant for both banks and non-bank financial institutions, although measures incorporating market leverage are especially informative for banks. A comparative analysis shows that market-based indicators offer information that is distinct from, and complementary to, traditional balance sheet metrics used in supervisory and macroprudential risk assessment. |
| Keywords: | Bank failures; Covar; Financial sector fragility; Leverage; MES; Srisk; Stress episodes |
| JEL: | C58 E32 G01 G12 G17 G20 G32 |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:19727 |
| By: | Afees A. Salisu (Centre for Econometrics and Applied Research, Ibadan, Nigeria; Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa); Ahamuefula E. Ogbonna (Centre for Econometrics and Applied Research, Ibadan, Nigeria); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa); Elie Bouri (School of Business, Lebanese American University, Lebanon) |
| Abstract: | This paper employs the generalized autoregressive conditional heteroscedasticity-mixed data sampling (GARCH-MIDAS) framework to forecast monthly and daily stock return volatility in the United States (US), based on a quarterly news-based Price Conflict Index (PCI) that signals “bad macroeconomic news†. An analysis of historical monthly (1860-2023) and daily (1885-2023) data demonstrates that the GARCH-MIDAS model incorporating PCI outperforms both the benchmark GARCH-MIDAS model with realized volatility (GARCH-MIDAS-RV) and models with macroeconomic variables such as output growth, inflation, unemployment, and interest rates. Furthermore, the inclusion of the PCI in modeling stock return volatility provides higher utility gains compared to models that exclude it. These findings have important implications for both investors and policymakers. |
| Keywords: | Price Conflict, Stock Returns Volatility, Forecasting, GARCH-MIDAS |
| JEL: | C32 C53 E31 G12 |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:pre:wpaper:202620 |
| By: | Junyu Chen; Tom Boot; Lingwei Kong; Weining Wang |
| Abstract: | Conditional Value-at-Risk (CoVaR) quantifies systemic financial risk by measuring the loss quantile of one asset, conditional on another asset experiencing distress. We develop a Transformer-based methodology that integrates financial news articles directly with market data to improve CoVaR estimates. Unlike approaches that use predefined sentiment scores, our method incorporates raw text embeddings generated by a large language model (LLM). We prove explicit error bounds for our Transformer CoVaR estimator, showing that accurate CoVaR learning is possible even with small datasets. Using U.S. market returns and Reuters news items from 2006–2013, our out-of-sample results show that textual information impacts the CoVaR forecasts. With better predictive performance, we identify a pronounced negative dip during market stress periods across several equity assets when comparing the Transformer-based CoVaR to both the CoVaR without text and the CoVaR using traditional sentiment measures. Our results show that textual data can be used to effectively model systemic risk without requiring prohibitively large data sets. |
| Date: | 2026–01–30 |
| URL: | https://d.repec.org/n?u=RePEc:bri:uobdis:26/840 |
| By: | Ting-Jung Lee; Abootaleb Shirvani; Farzana Afroz; Svetlozar T. Rachev; Frank J. Fabozzi |
| Abstract: | Taiwan's central role in global semiconductor manufacturing exposes Taiwan-related ETFs to technology concentration, geopolitical uncertainty, and supply-chain disruptions, resulting in return distributions characterized by heavy tails, volatility clustering, and asymmetric responses to negative shocks. This paper analyzes thirty U.S.-listed ETFs with Taiwan exposure from February 2015 to February 2025 using tail-risk diagnostics, asymmetric volatility modeling, and portfolio optimization under mean--variance and conditional value-at-risk (CVaR) criteria. Hill tail-index estimates document heavy-tailed behavior across the ETF universe. Although the ETFs exhibit broadly similar asymptotic tail-decay behavior, semiconductor-focused ETFs produce substantially larger VaR and CVaR estimates than diversified benchmarks, indicating that cross-sectional differences in extreme downside risk are driven primarily by differences in return scale rather than tail-index estimates. GJR-GARCH estimates reveal persistent, asymmetric volatility, and the apparent long memory in squared returns is largely attributable to conditional heteroskedasticity rather than genuine fractional integration. CVaR optimization produces substantially more concentrated allocations than mean--variance optimization, with the CVaR tangent portfolio allocating a large weight to SMH during the post-COVID AI-driven expansion. Portfolio rankings depend on the performance measure: the Sharpe ratio and STARR measure favor the equally weighted portfolio, whereas the Rachev ratio favors CVaR-based portfolios. Overall, the results suggest that variance-based frameworks alone provide an incomplete characterization of risk in technology-concentrated investment environments and that variance-based and tail-sensitive performance measures may favor different portfolios over the same sample period. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.16450 |
| By: | Vizcarrondo, Esteban; Serra, Teresa; McKenzie, Andrew |
| Abstract: | We develop a daily leverage premium measuring downside-asymmetric volatility risk within the filtered historical simulation framework of Barone-Adesi, Engle, and Mancini (2008). The leverage premium isolates the component of option-implied expected variance attributable to downside asymmetry beyond historical volatility dynamics. Applying this framework to daily options on live cattle, feeder cattle, and corn over 2019, we find that cattle options price about 25% more expected variance for downside asymmetry than historical patterns would justify—roughly twice the corn level. We then exploit the August 9, 2019 Tyson Holcomb fire to test whether option markets reprice downside risk following severe supply-chain disruptions. Using a difference-in-differences design with corn as the control market, the leverage premium increases for live cattle options during the disruption—by approximately 14% for regular options and 23% for serial options—and attenuates as processing capacity is restored. Feeder cattle, which are further upstream from the processing bottleneck, show no comparable response. Translated into hedging costs via a local Black (1976) approximation, the repricing implies $1.60–1.90 per head in additional cost for a representative short-dated, out-of-the-money live cattle put. |
| Keywords: | Demand and Price Analysis, Risk and Uncertainty |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ags:asea26:404809 |
| By: | Aloka, Atta Selorm; Ejimakor, Godfrey |
| Abstract: | This study investigates the financial distress within the North Carolina crop sector caused by sudden climatic shocks. Traditional insurance models often fail to anticipate catastrophic losses because they rely on linear historical averages that smooth over biological tipping points. This research addresses this gap by developing an early warning system that identifies the specific environmental and financial thresholds that push county-level insurance claims past the break-even point. Using a 15-year panel of North Carolina agricultural data, the study compares the predictive performance of a non-linear ensemble framework against traditional benchmarks to evaluate the reliability of the current crop insurance safety net. The results demonstrate that machine learning significantly improves the detection of extreme loss events by identifying financial scarring, the path dependency of previous losses, as the primary driver of current farm-level fragility. The analysis reveals that extreme heat waves and cumulative thermal stress act as triggers that push financially weakened counties into regional insurance shortfalls. These findings suggest that transitioning from reactive disaster relief to proactive, threshold-based risk management can stabilize the agricultural credit market and protect the savings of rural communities. This study provides a scalable framework for farmers, lenders, and insurers to mitigate the economic damage caused by climate-driven volatility in regional agribusiness sectors. |
| Keywords: | Agribusiness |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ags:aaea26:404323 |
| By: | Rohith Surya M; Dr. Arpita Choudhary (Assistant Professor, Madras School of Economics, Chennai, India.) |
| Abstract: | The research investigates how portfolio optimization techniques maintain their effectiveness during different market conditions which affect both emerging and developed equity markets by studying In¬dia and Singapore as case studies. The analysis compares mean–variance, minimum variance, equally weighted, and Conditional Value-at-Risk (CVaR) portfolios under both stable and stress market condi¬tions. The research identifies market regimes through a drawdown-based framework which uses an XGBoost classifier that processes macro-financial data including equity index returns and exchange rate movements and implied volatility indicators. The findings show that diversification strategies achieve better results in emerging markets which experience constant market changes while CVaR-based op-timization delivers better protection against losses and enhanced results in developed markets with extended stressful periods. The results demonstrate that portfolio strength and optimization success depend on the specific market conditions which affect different regimes. |
| Keywords: | Portfolio optimization, CVaR, regime detection, emerging markets, developed markets. |
| JEL: | G11 G17 G32 C58 C63 |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:mad:wpaper:2026-302 |
| By: | De Polis, Andrea; Melosi, Leonardo; Petrella, Ivan |
| Abstract: | We document that inflation risk in the U.S. varies significantly over time and is often asymmetric. To analyze the first-order macroeconomic effects of these asymmetric risks within a tractable framework, we construct the beliefs representation of a general equilibrium model with skewed distribution of markup shocks. Optimal policy requires shifting agents' expectations counter to the direction of inflation risks. We perform counterfactual analyses using a quantitative general equilibrium model to evaluate the implications of incorporating real-time estimates of the balance of inflation risks into monetary policy communications and decisions. |
| Keywords: | Optimal monetary policy; risk-adjusted inflation targeting |
| JEL: | E52 E31 C53 |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:19760 |
| By: | Muñoz, Manuel A.; Smets, Frank |
| Abstract: | We build a quantitative macro-banking model to study the optimal setting of the counter-cyclical capital buffer (CCyB) over the cycle. The model provides a rationale for micro and macro-prudential capital regulations by allowing for empirically-relevant bank default risk and binding borrowing constraints faced by banks and firms. We find that over-the-cycle adjustments in the CCyB can induce significant stabilization and welfare gains. Such gains: (i) are the largest if the CCyB is built in response to expected upward shifts in the bank lending spread, and (ii) increase with aggregate economic volatility and with the share of firms whose borrowing capacity is tied to their property collateral (rather than to their earnings). The calibrated optimal positive neutral CCyB for the case of the euro area lies between 1.8% and 2.5%. |
| Keywords: | Macroprudential policy; Collateral and borrowing constraints; Financial frictions; Bank risk; Bank capital requirements; CCyB |
| JEL: | E3 E44 E6 G21 |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:19790 |
| By: | Chahad, Mohammed; Mogliani, Matteo; Bańbura, Marta; Kulikov, Dmitry; Montes-Galdón, Carlos; Landau, Bettina; Meunier, Baptiste; Odendahl, Florens; Paredes, Joan; Sigwalt, Antoine; Theofilakou, Anastasia; Aristidou, Chryso; Pacella, Claudia; Rodrigues, Paulo; Roth, Markus |
| Abstract: | This paper introduces reduced-form macroeconometric tools, emphasising quantile regression models, to identify key risk drivers for the euro area economy and assess risks around the baseline ECB/Eurosystem staff macroeconomic projections for the euro area inflation and growth. The analysis uses a large number of risk factors, going beyond the usual financial factors, employing a sequential selection approach with robustness checks. To support the analysis a MATLAB toolbox (M@RX) was developed, incorporating several quantile regression-based model classes with a novel parametric tilting methodology and a copula approach for transforming predictive densities across frequencies. This paper contributes to the literature on the treatment of the COVID-era data in quantile regression models. Results indicate that the predictive content of risk factors is horizon, time and objective-dependent. For example, labour market indicators are particularly relevant for assessing upside inflation risks, but to a time-varying extent and with limited predictive power for downside risks. Conversely, uncertainty, money and credit indicators perform better for downside inflation risks. As regards risks to growth, the results confirm the established role of financial conditions, while also highlighting the relevance of monetary indicators, particularly for downside risks. Combined risk factor frameworks – with several different risk indicators – tend to systematically outperform single-factor specifications for density forecasting, due to complementarities across risk indicator groups. An empirical application highlights the policy relevance of these tools, as they provide timely signals and accurately track the direction of realised outcomes. Given the time-varying and state-dependent nature of their predictive performance, a regular performance assessment of the specifications is recommended to maintain reliability. JEL Classification: C22, C53, E27, E37 |
| Keywords: | density forecasts, forecasting, Macro-at-Risk, quantile regression, tail risks |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:ecb:ecbops:2026396 |
| By: | Stephen Millard |
| Abstract: | In this paper, I examine how scenario analysis can improve the communication of risks in macroeconomic forecasting. Building on the Bernanke review, I evaluate four uses of scenarios: assessing policy interventions, illustrating specific forecast risks, addressing model uncertainty, and decomposing historical forecast errors. I argue that scenarios are most effective when used to convey distinct, narrative‐driven risks around a central forecast, offering clearer insights than fan charts alone in this case. However, I also argue that fan charts are useful for communicating general uncertainty. Overall, I find that narrative‐based scenario analysis is a powerful tool for communicating macroeconomic risks. |
| Keywords: | Scenario analysis, Macroeconomic forecasting, Forecast uncertainty, Risk communication, Narrative-based analysis |
| JEL: | C53 C54 E17 |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:nsr:niesrd:584 |
| By: | Weiye Xi; Ciamac C. Moallemi; Mallesh Pai; Shouqiao Want |
| Abstract: | Forward-looking volatility forecasts are central inputs to derivatives pricing, market making, risk management, and volatility-linked trading strategies, with ARCH and GARCH models serving as the canonical workhorses. Such models are natural in standard asset markets, where prices are positive-valued stochastic processes and volatility is typically inferred from return dynamics. Prediction markets have a different structure: prices are bounded probabilities, payoffs are binary, and contracts resolve at known deadlines. We develop and estimate a volatility model tailored to binary prediction markets. The model combines two economic mechanisms: a Wright-Fisher deadline-resolution component, capturing how remaining binary uncertainty is forced to resolve over time, and a Glosten-Milgrom order-flow component, capturing volatility from informed trading as reflected in spreads and volume. Using a large panel of Kalshi contracts, we show that these structural variables carry substantial forecasting power. Plain ARCH/GARCH benchmarks are dominated by structural specifications; combining the structural model with residual GARCH dynamics gives the best overall forecasts. The model also provides an interpretable measurement framework: volatility is highest near fifty-fifty prices, rises near resolution, and varies across categories with the timing and discreteness of information arrival. Economics contracts are closer to smooth deadline-resolution dynamics, while sports contracts exhibit more event-concentrated, jump-like behavior. Across major categories, category-specific fitting does not systematically improve out-of-sample performance, suggesting that the structural specification transfers beyond the pooled headline result. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.08199 |
| By: | Fan, Fan; Lee, Seowoo; Liu, Yong |
| Abstract: | Understanding how hurricanes translate into agricultural insurance losses is important for crop insurance design and agricultural risk management. We estimate the local causal effect of hurricane wind exposure using a spatial boundary regression discontinuity design. Using NOAA best-track and wind-radius data, we construct realized hurricane wind-field boundaries and compare counties just inside and just outside these boundaries within narrow geographic bandwidths. Combining these exposure measures with county-level crop insurance data from the USDA Risk Management Agency over 2004–2024, we find that crossing into the realized 34-knot wind field increases the hurricane loss-cost ratio by approximately 0.85 percentage points at the boundary. A broader storm-related loss measure produces a larger estimate, suggesting that narrow hurricane cause-of-loss categories may understate the insured loss response. A fuzzy RD specification further indicates that insured losses increase with within-county exposure share, and crop-specific estimates show meaningful heterogeneity across major crops. Estimates at the 64-knot boundary are larger but less precise. Descriptive comparisons with HIP-WI suggest that its effective indemnity footprint may not fully align with the broader realized exposure footprint over which agricultural losses occur. Our findings provide local causal evidence on hurricane losses and inform hurricane-specific crop insurance design. |
| Keywords: | Risk and Uncertainty |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ags:aaea26:404410 |
| By: | Conlon, Thomas; Corbet, Shaen; Larkin, Charles; Muñiz, Jose Antonio |
| Abstract: | This study examines the efficiency of systemic risk transmission to international oil futures markets by analyzing the dynamic connectedness between three distinct Common Volatility (COVOL) measures: Energy, Asset, and Country, and compares such with five major oil benchmarks. Utilizing a framework that combines TVP-VAR, EGARCH, and wavelet coherence analyses, we investigate whether nontrading weekend breaks create a structural barrier to the pricing of systemic risk. Our findings identify a significant Monday effect, characterized by a pronounced decoupling between systemic risk signals and oil futures prices. The effect is highly state-dependent: during the COVID-19 pandemic, the disconnect dissipated for Energy and Asset COVOL but intensified for Country COVOL, while geopolitical conflicts extended the breakdown of the signal into Tuesday. These results indicate a hierarchy of influence in which country-level systemic risks exert the strongest effect on oil markets. |
| Keywords: | COVOL;energy markets;information flow;oil;systemic risk;wavelets |
| JEL: | G14 G15 Q41 |
| Date: | 2026–07–15 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:140276 |
| By: | Rachid Maghniwi (UM5 - Université mohamed 5, Rabat) |
| Abstract: | The rapid expansion of Open Banking in Morocco, accelerated by Bank Al-Maghrib regulatory frameworks and the digital transformation of financial services, creates systemic challenges for credit risk assessment across distributed banking networks. This paper proposes a federated learning (FL) architecture reinforced by differential privacy (DP) mechanisms for collaborative credit risk modelling in the Moroccan banking sector. The framework enables financial institutions to jointly train predictive models on distributed customer data without compromising individual privacy or violating data sovereignty constraints. Drawing on empirical data from a survey of 500 clients and 25 expert interviews conducted in the Rabat-Salé-Kénitra (RSK) region, we design a privacy-preserving gradient aggregation protocol adapted to the heterogeneous structures of Moroccan retail banking portfolios. Our federated model (FL-DP-AC) achieves an AUC-ROC of 0.847, representing a 14.3% improvement over centralised baselines, while maintaining a privacy budget (ε) of 1.2 under the Gaussian mechanism, meeting privacy thresholds imposed by Moroccan data protection law (Law No. 09-08). Results validate the feasibility of federated credit scoring as a secure and interoperable foundation for Open Banking ecosystems, and contribute a blueprint for AI-driven financial inclusion across the African banking sector. |
| Keywords: | Open Banking, machine learning, financial inclusion, Morocco, Bank Al-Maghrib, privacy-preserving AI, gradient aggregation, UTAUT, Moroccan banking sector, credit risk, differential privacy, federated learning, federated learning differential privacy credit risk Open Banking machine learning financial inclusion Morocco Bank Al-Maghrib privacy-preserving AI gradient aggregation UTAUT Moroccan banking sector |
| Date: | 2026–05–26 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05638586 |
| By: | Gyöngyi Loranth; Anatoli Segura; Jing Zeng |
| Abstract: | We study the effects of aligning the incentives of national authorities through the common provision of deposit insurance in a model of cross-border banks with both endogenous risk-taking and within-group risk-sharing. Under national deposit insurance, local authorities inefficiently ring-fence resources flowing from healthy to impaired subsidiaries. A single authority respon sible for a common deposit insurance fund does not ring-fence. This encourages cross-border integration, but has an ambiguous impact on banks’ risk-taking. Overall, common deposit in surance increases welfare when the fundamental risk in the economy is high but otherwise can lead to excessive cross-border integration and lower welfare. |
| Keywords: | cross-border bank, common deposit insurance, intragroup support, ring-fencing, banking union |
| JEL: | D8 G11 G2 |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:bon:boncrc:crctr224_2025_769 |
| By: | Guo, Hongfei; Marín Díazaraque, Juan Miguel; Veiga, Helena |
| Abstract: | Adding flexibility to a multivariate volatility model can worsen covariance forecasts; we show when, and how to detect it. We decompose the multivariate QLIKE loss into trace, marginal-scale, and correlation log-determinant components; counterfactual block substitutions attribute gains or losses to either part. In Bayesian dynamic-correlation stochastic volatility, the diagnostic yields a sequence: diffuse dynamic correlations can underperform a constant-correlation baseline; a stabilizing prior repairs the correlation component; once stabilized, lagged realized-volatility inputs are the strongest remaining lever, with neural corrections competitive but not dominant. Under a rolling protocol, the stabilized realized-augmented family rivals realized-covariance benchmarks while retaining full predictive densities. |
| Keywords: | Covariance forecasting; Dynamic correlations; Forecast-object diagnostics; Neural networks; Prior regularization; Realized volatility; Stochastic volatility |
| JEL: | C11 C32 C53 C58 G17 |
| Date: | 2026–07–28 |
| URL: | https://d.repec.org/n?u=RePEc:cte:wsrepe:50561 |
| By: | Fredy Gamboa; Jose Vicente Romero |
| Abstract: | This study examines how geopolitical risk (GPR) transmits to sovereign credit risk in emerging market economies (EMEs), using monthly data on the 5-year sovereign credit default swap (SCDS) and the J.P. Morgan Emerging Markets Bond Index (EMBI) spread for 13 EMEs over the period of January 2005–October 2025. Using fixed-effects panel local projections, the framework is extended to allow for state-dependent transmission. Differences in impulse responses across states are attributed to specific macrofinancial fundamentals. Three main findings are identified. First, an increase in the GPR index raises both SCDS and EMBI spreads. Second, disaggregating the index into its subcomponents reveals a larger response to threats than to acts, consistent with the possibility of anticipation effects in sovereign credit markets. Third, evaluating the state-dependent impulse response around the Russian invasion of Ukraine yields substantially different responses, with the post-invasion configuration increasing the sovereign risk premia response. Our findings show the importance of modeling the state-dependent transmission of geopolitical shocks and provide a useful tool for incorporating geopolitical scenarios into sovereign risk analysis. |
| Keywords: | sovereign risk, credit default swaps, EMBI, emerging markets, geopolitical risk, panel local projections, state-dependent transmission |
| JEL: | C23 C54 F34 G15 |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:bis:biswps:1368 |
| By: | Alessandro Bucciol (Department of Economics (University of Verona)); Silvana Robone (University of Eastern Piedmont); Luca Zarri (Department of Economics (University of Verona)) |
| Abstract: | Major life events are widely recognized as important determinants of individuals’ risk attitudes. However, the empirical evidence does not point to a clear directional effect, with studies documenting both increases and decreases in risk taking following exposure to adverse events. This paper investigates whether individual differences in Locus of Control (LoC) – a psychological trait capturing the extent to which individuals perceive life outcomes as contingent on their own actions rather than on external forces – may help account for this heterogeneity when financial risk attitudes are considered. Using original survey data on a sample of 1, 827 individuals representative of the Italian population, we investigate how exposure to different traumatic life events is associated with a series of measures of financial risk attitudes, including both incentive-compatible and self-reported measures, and whether these associations vary by individuals’ LoC and gender. Results reveal substantial heterogeneity that would remain hidden in models considering only the average association between events and risk attitudes. Our findings show that behavioral responses to traumatic experiences are to a large extent event-, measure- and gender-specific, and suggest that LoC can help explain the mixed evidence documented in the existing literature. |
| Keywords: | Financial Risk Attitudes, Traumatic Events, Locus of Control, Personality Traits |
| JEL: | D81 D91 G11 G41 |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:ver:wpaper:04/2026 |
| By: | Yan, Hongqiang; Manfredo, Mark; Mishra, Ashok |
| Abstract: | We examine whether broad economic news Granger-causes implied volatility derived from options on agricultural futures markets. Using approximately 1.5 million Wall Street Journal articles (1984–2026), we construct 180 daily news topic attention series via Latent Dirichlet Allocation and apply a high-dimensional Granger causality framework that combines sparse-group LASSO variable selection with debiased inference and HAC standard errors. Using implied volatility measures provided by Bloomberg, we find that financial and macroeconomic news topics systematically Granger-cause implied volatility across corn, soybeans, soybean meal, and soybean oil markets and across Bloomberg constant-maturity horizons of 30 days, 60 days, and 3 months. Topics related to program trading and index arbitrage, investment banking, and the 2008 financial crisis are the most robust across commodities, consistent with the financialization of agricultural futures markets. The number of significant news topics declines from 23 at the 30-day maturity to 10 at the 3-month maturity, suggesting that financial news primarily influences short-term uncertainty. A crop-specific news topic is not selected for any commodity-maturity combination. This may be due to crop fundamentals being transmitted to implied volatility through scheduled USDA report releases rather than through ongoing national media coverage. |
| Keywords: | Research Methods/Statistical Methods, Risk and Uncertainty |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ags:asea26:404810 |
| By: | Kellner, Domenic; Lang, Jan Hannes; Rusnák, Marek; Nagy, Lukas Joseph |
| Abstract: | Financial stability risks consist of two distinct components: vulnerabilities and possible trigger events. While there has been considerable progress regarding the measurement of vulnerabilities, the assessment of possible trigger events remains largely qualitative. To fill this gap, we employ Large Language Models to extract information about the Severity and Probability Of potential Trigger events (SPOT) from a large dataset of financial news articles over the period2005 – 2026. The SPOT indicator increases ahead of major historical trigger events, correctly identifies trigger sources, and helps to improve forward looking model estimates of downside risks to the economy. The results indicate that the use of AI-based signal extraction from text can be a promising avenue to improve the monitoring of financial stability risks. JEL Classification: C55, C88, E32, E44, G01 |
| Keywords: | artificial intelligence, crisis indicators, financial stability, growth-at-risk |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:ecb:ecbwps:20263262 |
| By: | Seungduck Lee; Angelo Ranaldo; Tomohiro Tsuruga |
| Abstract: | Settlement risk is a central friction in currency markets. We provide causal evidence on its pricing by exploiting Hungary's 2015 adoption of CLS, which introduced payment-versus-payment settlement, sharply reducing settlement risk. Using a difference-in-differences design, we find that currency excess returns decline by about ten basis points after CLS adoption, consistent with lower compensation for bearing settlement risk, while exchange rate volatility also falls. Deviations from triangular arbitrage conditions narrow, indicating a reduction in the effective cost of arbitrage and improved market efficiency. Additional evidence based on U.S.-specific holidays supports a mechanism operating through time-zone exposure. Our findings show that settlement risk is a priced friction and a source of limits to arbitrage in currency markets. |
| Keywords: | Foreign exchange; Settlement risk; Market microstructure; Payment-versus-payment; Limit to arbitrage |
| Date: | 2026–07–24 |
| URL: | https://d.repec.org/n?u=RePEc:imf:imfwpa:2026/156 |
| By: | Wagner, Wolf; Zeng, Jing |
| Abstract: | We analyze the design of bailout regimes when investment is distorted by a too-many-to-fail problem. The first-best allocation equalizes benefits from more banks investing in high-return projects with endogenously higher systemic risk due to more banks failing simultaneously. A standard bailout policy cannot implement the first-best, as bailouts cause herding by banks. However, a targeted bailout policy that assigns banks to separate bailout regimes eliminates herding and achieves the first-best. When such a policy is not feasible, targeted bailouts can be implemented by decentralizing bailout decisions to independent regulators. Our results have various implications for the optimal allocation of regulatory powers, both at the international level and domestically. |
| JEL: | G1 G2 |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:19976 |
| By: | Federico, Salvatore; Modena, Andrea; Regis, Luca |
| Abstract: | This paper examines how state-dependent dividend restrictions (taxes and bans) and capital requirements influence a bank's optimal capital buffers accumulation and risk-taking decisions. In the model, the bank distributes dividends and issues costly equity to maximise shareholder value, while its loans generate stochastic income under time-varying macroeconomic conditions. We solve the bank's stochastic control problem and derive the distribution of its capital buffers in closed form. We find that imposing dividend restrictions in bad macroeconomic states generates an intertemporal trade-off, as it encourages capital buffers accumulation in those states but promotes dividend payouts in the good ones. Furthermore, we show that the policy can undermine financial stability by reducing the bank's value and weakening its incentives to recapitalise in all states. Coordinating dividend taxes with counter-cyclical capital requirements can mitigate value losses and ease the trade-off, but it also exacerbates disincentives for recapitalisation. Finally, we show that when the bank can (optimally) reduce its risky loans in bad states, capital buffers generated through dividend restrictions mitigate the contraction by dampening its precautionary motive against costly recapitalisation. |
| Keywords: | Capital requirements, dividend bans, dividend taxes, policy coordination, stochastic optimal control |
| JEL: | C61 G21 G32 G35 G38 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:safewp:342550 |
| By: | Nam, Hosung; Jo, Jungkeon; Lee, Wonseok |
| Abstract: | Public information not only reduces fundamental uncertainty in financial markets; it also coordinates dispersed private beliefs around a common reference point. We study this coordinating role in the U.S. corn futures market by developing a Bayesian learning framework in which traders form posterior price expectations as a weighted average of private priors and the public signal from USDA crop reports. Using a novel inverse quantile mapping procedure to extract price expectations from agent-level supply forecasts, we estimate that market participants place an average weight of 15 percent on USDA reports relative to their private priors. This anchoring weight is highly dynamic. Traders rely less on the public signal when USDA forecasts become noisier, and substantially more when private-analyst dispersion rises. Using Local Projections with Instrumental Variables (LP-IV), we decompose the effect of each source of uncertainty on realized volatility, and uncover a striking asymmetry. Public uncertainty raises volatility primarily by weakening the anchor. Private uncertainty, by contrast, does not raise total volatility. Its destabilizing direct effect is fully offset by the stabilizing indirect effect of stronger anchoring. The market’s apparent resilience to private uncertainty is therefore a product of public information itself. These findings reveal a trade-off facing public agencies between maximizing forecast accuracy and preserving market stability, and show that the value of credible public statistics rises with the disorder of private information environments. |
| Keywords: | Risk and Uncertainty |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ags:aaea26:404411 |