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on Risk Management |
| By: | Ahnert, Toni; Bertsch, Christoph; Leonello, Agnese; Marquez, Robert |
| Abstract: | Shocks to a bank’s ability to raise liquidity at short notice can trigger depositor panics. Why don’t banks take a more active role in managing these risks? We study contingent risk management (hedging) in a standard global-games model of a bank run. Banks fail to hedge precisely when the exposure to a shock is most severe, just when risk management would have the biggest impact. Higher bank capital and broader deposit-insurance coverage crowd out hedging, yet encourage more banks to establish risk management desks in the first place. The model also yields testable implications for hedging incentives and policy design. |
| Keywords: | Bank runs; Liquidity risk; Hedging |
| JEL: | G01 G21 G23 |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:19523 |
| By: | Elliott, Matthew |
| Abstract: | This paper demonstrates the application of Modern Portfolio Theory (MPT) to commodity risk management, contrasting it with traditional technical indicator-based hedging strategies. While MPT approaches have been extensively utilized in the investment management industry for decades to optimize institutional portfolios and mutual funds, these sophisticated methods have not been widely applied by practitioners for optimizing hedges to commodity production. Using actual market data from 2018-2025, we analyze the performance of four distinct strategies: physical position holding, maximum Sharpe ratio optimization, minimum variance optimization, and technical Bollinger Band breakout signals for optimal risk management in commodity production. Our empirical analysis of a representative agricultural producer portfolio ($1.1M in corn and soybeans) reveals that the minimum variance strategy delivers superior risk-adjusted returns with a Sharpe ratio of 0.519, compared to technical strategies averaging -0.094. The study provides quantitative evidence that MPT-based approaches can significantly enhance commodity producers' risk management while reducing portfolio volatility by up to 47%. |
| Keywords: | Agribusiness |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ags:aaea26:404326 |
| By: | Abad, Jorge; Martinez-Miera, David; Suarez, Javier |
| Abstract: | We study banks' systemic risk-taking decisions in a dynamic general equilibrium model, highlighting the macroprudential role of bank capital requirements. Bankers decide on the unobservable exposure of their banks to systemic shocks by balancing risk-shifting gains against the value of preserving their capital after such shocks. Capital requirements reduce systemic risk taking but at the cost of reducing credit and output in calm times, generating welfare trade-offs. We find that systemic risk taking is maximal after long periods of calm and may worsen if capital requirements are countercyclically adjusted. Removing deposit insurance introduces market discipline but increases the bank capital necessary to support credit, implies lower (though far from zero) optimal capital requirements, and has nuanced social welfare effects. |
| Keywords: | Capital requirements; Risk shifting; Deposit insurance; Systemic risk; Financial crises; Macro-prudential policies |
| JEL: | G01 G21 G28 E44 |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:19532 |
| By: | Haifan Hu; Bingzhen Geng; Jiajun Liu; Shijie Wang |
| Abstract: | This paper investigates second-order asymptotic expansions for the conditional higher moment (CoHM) coherent risk measure under a Farlie-Gumbel-Morgenstern (FGM) dependence structure, capturing a weak contagion between a primary loss risk and a reference risk. Assuming that the primary risk belongs to the Fr\'echet, Weibull, or Gumbel maximum domain of attraction, we systematically derive second-order asymptotic expansions using extreme value theory and second-order regular variation theory. Compared with existing first-order results, our refined approximations capture higher-order tail behavior and dependence effects more accurately. Numerical simulations confirm that the second-order asymptotics substantially reduce approximation errors, especially at extreme confidence levels. Empirical applications to insurance claim data further illustrate the practical superiority of the second-order approach. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.16601 |
| By: | Nader Karimi; Davood Ahmadian |
| Abstract: | We develop a continuous-time structural dynamic model to determine the exact insolvency regions of banks arising from the non-linear interaction between liquidity and credit risk. While existing literature predominantly treats these risks in isolation or via reduced-form specifications, we explicitly model the feedback loop where funding shocks and regulatory constraints force balance-sheet adjustments that can lead to endogenous insolvency. By incorporating Basel III regulatory requirements (LCR and NSFR) into a stochastic optimal control framework, we solve for the exact insolvency boundary using the Hamilton-Jacobi-Bellman (HJB) equation. To bridge the gap between theoretical complexity and supervisory practice, we derive and validate a surrogate analytical approximation function that allows for real-time monitoring. Calibrated using granular balance-sheet data from the Iranian banking sector, our model reveals significant non-linear threshold effects: the joint occurrence of liquidity stress and credit portfolio defaults disproportionately accelerates the transition toward insolvency compared to their individual effects. The proposed surrogate function offers supervisors a computationally efficient tool for stress testing and early warning systems. Our findings provide novel insights into financial frictions in emerging markets and offer a rigorous framework for integrated risk management. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.17381 |
| By: | Ang Zhang |
| Abstract: | Human capital is a central organizational input, but standard financial data reveal little about firm-specific disruptions to workforce availability, cost, skills, and continuity. I construct a measure of disclosed human-capital disruption from earnings calls using author-defined coding criteria and a contextual language model. Within firms, a one-standard-deviation increase in the annual measure is associated with 0.55 percentage points higher idiosyncratic volatility, 0.58 percentage points higher downside deviation, and a 0.46 percentage point lower worst monthly return, with no corresponding relation to market beta. The results are stable across seven broader and narrower classification rules and remain after removing explicit labor-shortage passages and controlling for a recently published labor-shortage measure and transcript-wide negative and uncertain language. At the call level, human-capital disruption predicts approximately 0.50% higher idiosyncratic volatility over the following 42 trading days after conditioning on pre-call risk. Risk is elevated before the call as well, and the score predicts the continuation of that firm-specific risk state over the following 42 trading days. After telegraphing and succession passages are removed, the measure also predicts subsequent named-executive roster exits and the incumbent CEO's effective departure from office. Earnings calls therefore reveal disturbances to a key organizational input that are broader than labor shortages and informative about the distribution of firm outcomes. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.14859 |
| By: | Xu, Chenguang; He, Xinyue |
| Abstract: | Algorithmic trading is commonly used in agricultural futures markets. Using high-frequency order-book data for Dalian Commodity Exchange of corn and soybean meal futures, this paper studies how algorithmic trading affects price volatility and tail risk in China's agricultural futures markets. The evidence points to a dual effect: Algorithmic trading significantly lowers conventional realized volatility; At the same time, it increases downside tail co-movement and Asymmetry between each contract and agricultural futures market. This conclusion is supported by robustness checks that exclude the DCE 7.0 transition period, reconstruct tail-risk measures using 5-minute data, and use the combined day-and-night trading session. In addition, while the Symmetrized Joe-Clayton copula baseline check confirms the robustness of the empirical tail-risk results to an alternative tail-dependence measure, we also use instrumental variables to test how algorithmic trading affects volatility. Further analysis investigates the impact of quantitative trading on volatility under different volatility regimes and during the rollover periods of dominant contracts. This study provides a theoretical basis for the “dual-effect” mechanism in agricultural financial derivatives markets and proposes regulatory suggestions for algorithmic trading in agricultural futures markets. |
| Keywords: | Agricultural Finance, Farm Management |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ags:aaea26:404354 |
| By: | Tarik Jellouli (USMBA - Université Sidi Mohamed Ben Abdellah); Hamza Ousi Moh (USMBA - Université Sidi Mohamed Ben Abdellah) |
| Abstract: | Credit risk represents the primary source of vulnerability for banking institutions and constitutes a central concern within contemporary prudential frameworks. In this context, the introduction of the Internal Ratings-Based (IRB) approach under the Basel II framework, and its subsequent reinforcement under Basel III, has profoundly transformed the mechanisms used to measure and manage credit risk. This article provides a critical and integrative review of the academic literature examining the impact of internal rating systems on credit risk management. Based on a structured analysis of theoretical and empirical studies published over the past two decades, the paper investigates the contributions of the IRB system in improving risk measurement—particularly through the estimation of key parameters such as the probability of default (PD), loss given default (LGD), and exposure at default (EAD). It also explores the role of internal rating systems in optimizing the allocation of both regulatory and economic capital, as well as in refining risk-based pricing strategies. The literature review highlights that internal models enhance the differentiation of risk profiles and promote a more efficient allocation of financial resources, thereby improving risk-adjusted performance. However, the existing body of research also identifies several significant limitations, including model procyclicality, excessive reliance on historical data, methodological complexity, risks of regulatory arbitrage, and governance-related issues associated with the discretionary use of internal parameters. Lessons drawn from the 2008 global financial crisis have led to the strengthening of the prudential framework and greater harmonization of practices, notably through the introduction of mechanisms such as the regulatory output floor. The article concludes that the effectiveness of internal rating systems does not depend solely on their technical sophistication but also on the quality of banking governance, the robustness of internal control mechanisms, and the strength of regulatory oversight. Finally, the study proposes an integrative conceptual framework linking internal ratings, governance, and banking performance, thereby opening new avenues for future research, particularly in emerging economies. |
| Abstract: | Le risque de crédit constitue la principale source de vulnérabilité des établissements bancaires et représente un enjeu central dans les dispositifs prudentiels contemporains. Dans ce contexte, l'introduction du système de notation interne (Internal Rating Based – IRB) dans le cadre des accords de Bâle II puis son renforcement sous Bâle III ont profondément transformé les mécanismes de mesure et de gestion du risque de crédit. Cet article propose une revue critique et intégrative de la littérature académique consacrée à l'impact du système de notation interne sur la gestion du risque de crédit. À partir d'une analyse structurée des travaux théoriques et empiriques publiés au cours des deux dernières décennies, l'étude examine les apports du système IRB en matière d'amélioration de la mesure du risque (probabilité de défaut, perte en cas de défaut, exposition au défaut), d'optimisation de l'allocation du capital réglementaire et économique, ainsi que d'affinement de la tarification fondée sur le risque. La revue met en évidence que les modèles internes permettent une meilleure différenciation des profils de risque et une allocation plus efficiente des ressources, contribuant ainsi à la performance ajustée au risque. Cependant, la littérature souligne également plusieurs limites majeures : procyclicité des modèles, dépendance excessive aux données historiques, complexité méthodologique, risques d'arbitrage réglementaire et problèmes de gouvernance liés à l'utilisation discrétionnaire des paramètres internes. Les enseignements tirés de la crise financière de 2008 ont notamment conduit à un renforcement du cadre prudentiel et à une harmonisation accrue des pratiques via l'introduction de mécanismes tels que l'output floor. L'article conclut que l'efficacité du système de notation interne ne dépend pas uniquement de sa sophistication technique, mais également de la qualité de la gouvernance bancaire, du dispositif de contrôle interne et de l'encadrement réglementaire. Il propose enfin un cadre conceptuel intégratif reliant notation interne, gouvernance et performance bancaire, ouvrant ainsi des perspectives de recherche futures, notamment dans les économies émergentes. |
| Keywords: | Internal rating system, Credit risk, Internal ratings-Based approach, Basel accords, Bank risk management, Risque de crédit, Approche IRB, Accords de Bale, Gestion du risque bancaire, Système de notation interne |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05649272 |
| By: | Oscar Brooks; Dusica Bajalica; Yating Liu; Imen Ben Tahar |
| Abstract: | We propose an arbitrage-aware latent flow-matching framework for unconditional implied volatility surface generation. The method first compresses high-dimensional surfaces into a low-dimensional latent space using a variational autoencoder regularized by differentiable calendar-spread, call-spread and butterfly-arbitrage penalties. A flow-matching model then learns to transport a Gaussian prior toward the empirical latent distribution, and generated latent samples are decoded back into volatility surfaces. We evaluate the approach using marginal and surface-level Wasserstein distances, smile and skew diagnostics, pointwise quantile surfaces, financially interpretable shape metrics, and static no-arbitrage tests. The proposed model closely reproduces the empirical distribution and the main maturity-moneyness structures, achieves the best performance in the extreme Q99 regime, and generates 90.8% of surfaces satisfying all tested static no-arbitrage conditions. Overall, the results show that latent flow matching provides a favorable balance between distributional similarity, tail preservation, and financial consistency without requiring post-sampling reweighting. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.00616 |
| By: | Alexandre Alouadi; Charles-Albert Lehalle |
| Abstract: | Estimating the covariance structure of financial assets typically relies on historical returns, making risk models dependent on noisy and asset-specific time series. We propose the Characteristic-Driven Dynamic Factor Model (CD-DFM), a non-linear latent factor model that instead constructs a representation of the asset cross-section directly from observable firm characteristics, primarily company fundamentals. The learned latent space jointly determines interpretable factor exposures and a forward covariance estimator, and is trained end to end on an objective that combines a Stein covariance loss with a factor reconstruction term, targeting the out-of-sample second moments used in risk management. Because the latent representation, i.e. the encoder depends only on characteristics, previously unseen assets can be embedded at inference time without retraining. Experiments on S&P 500 equities show that CD-DFM produces economically structured latent representations, interpretable factor portfolios, and competitive covariance forecasts despite relying on substantially lower-frequency information than return-based approaches. Among the benchmarked methods, it is the only model that simultaneously combines characteristic-driven representations, factor interpretability, competitive covariance calibration, and zero-shot onboarding of unseen assets. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.24410 |
| By: | Jan Fialkowski; Shlomo Havlin; Stefan Thurner |
| Abstract: | Whether the COVID-19 pandemic or the Iran war, recent events have highlighted the systemic fragility of supply chains. Due to highly specific and mutual buyer-supplier dependencies, even the failure of a single firm can cause system-wide economic disruptions in the form of cascading failures up and down the supply chain network. Only recently has it become possible to quantify the systemic impact of the failure of individual firms on the total supply chain. Here, we demonstrate that the systemic risk contributions of combinations of firm failures can be drastically larger than the sum of the damage caused by the firms individually. Using a unique data set that allows us to reconstruct the national supply chain network of Ecuador at the firm-level, we find that combined failures can produce systemic risk amplifications of up to a factor of 257. However, only a tiny fraction of 0.14\% of pairs exhibit a more than 4-fold amplification of systemic risk. We develop a simple method to identify firm combinations that lead to large systemic risk amplifications. The origin of these amplifications is a breakdown of the substitutability of defaulted suppliers. We discuss the implications of the existence of rare but strong systemic risk amplification for situations that simultaneously affect multiple firms, such as natural disasters and wars. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.20068 |
| By: | Yingli Wang; Yinhao Wu; Lingjiong Zhu |
| Abstract: | Hawkes-based microstructural foundations for rough volatility, leverage, and rough Heston-type limits were developed by El Euch et al. (2018, Finance Stoch., 22(2), 241--280) and connected to the affine rough Heston framework of El Euch and Rosenbaum (2019, Math. Finance, 29(1), 3--38). The rough Hawkes--Heston model with common price--volatility jumps of Bondi et al. (2024, Math. Finance, 34(4), 1197--1241) extends this framework by adding state-dependent common jumps to rough affine volatility. We provide a microstructural foundation for its variance and common-jump mechanism by constructing a Poisson-embedded marked Hawkes order-flow model. Ordinary arrivals generate rough continuous volatility and leverage through a nearly unstable heavy-tailed Hawkes mechanism, while rare marked arrivals represent common shock events that produce simultaneous price jumps and volatility excitation. Under the nearly unstable scaling and the reduced-form admissibility conditions, the complete rescaled price/variance/jump system converges along the full sequence to the unique complete canonical rough Hawkes--Heston weak solution. The Hawkes renewal structure yields a Mittag--Leffler Volterra representation, which is then rewritten in Riemann--Liouville fractional form. The limiting coefficients are expressed explicitly in terms of the microscopic parameters. The construction provides a microstructural foundation for the variance and common-jump mechanism of the rough Hawkes--Heston model. Numerical experiments illustrate the convergence of our microstructural foundation to the rough Hawkes-Heston model. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.07709 |
| By: | Gabriel Bruneau; Sascha Clazie-Thomson; Thibaut Duprey; Ruben Hipp; Javier Ojea Ferreiro; Kerem Tuzcuoglu |
| Abstract: | This paper reviews recent efforts to monitor and assess systemic risk in the Canadian financial system and outlines a framework for future system-wide stress testing. We examine how perceived and actual interconnections—across banks and non-bank financial institutions, domestic and foreign entities, and institutions of different sizes—shape the propagation of financial stress. We then review advances in system-wide stress-testing approaches, including agent-based and equilibrium-based models that capture downside amplification mechanisms and macro-financial feedback. Finally, the paper presents a blueprint for a Canadian system-wide stress-testing and reverse stress-testing toolkit designed to support the assessment of financial system resilience. |
| Keywords: | Financial system; Financial stability and systemic risk |
| JEL: | G01 G17 G18 G21 G23 G28 |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:bca:bocsap:26-28 |
| By: | Dmitry Dagaev (New Economic School, HSE University); Elina Ibragimova (HSE University, Kyung Hee University); Ekaterina Lodneva (HSE University, New Economic School) |
| Abstract: | How do elite athletes adjust risk-taking across score contexts and tournament stages, and how do these patterns differ between men’s and women’s tournaments? We address these questions using a novel, manually coded rally-level dataset from the 2024 Olympic volleyball tournaments, covering 8, 670 rallies from 52 men’s and women’s matches. We operationalize risk through observable tactical choices — serve type and attack placement — and estimate how these choices respond to score context, set progression, and playoff stakes. We find that risk-taking is systematically gendered and context-dependent: men attempt riskier serves as sets near their end, while women grow more conservative when protecting a set lead. In attacking, both genders turn more cautious late in sets. This asymmetry aligns with prospect theory since serving is a loss-domain decision that invites risk-seeking, while attacking is a gain-domain decision that invites risk aversion. Contrary to expectations of caution under elimination pressure, both genders take more risks in the knockout stage. These findings extend behavioral models of risk to a high-stakes team sport setting, with implications for coaching and performance analysis. |
| Keywords: | risk-taking, prospect theory, gender differences, high-stakes games, sports economics, Olympic volleyball |
| JEL: | D81 Z20 J16 |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:abo:neswpt:w0298 |
| By: | Christian Oliver Ewald |
| Abstract: | The Arrow-Pratt approximation is one of the cornerstones of expected utility theory, providing the classical local approximation of certainty equivalents and risk premia in terms of absolute risk aversion. Despite its widespread use, its mathematical scope and relationship to higher-order risk preferences remain only partially understood. This paper develops a new framework for the analysis of certainty equivalents and dynamic risk premia based on Malliavin calculus and Wiener chaos analysis. We first show that the classical Arrow-Pratt approximation is not asymptotically valid for arbitrary sequences of vanishing risks, thereby identifying precise limitations of the traditional theory. Motivated by this observation, we formulate certainty equivalents dynamically by considering the progressive revelation of uncertainty through a Brownian filtration. Combining It\^o calculus, the Clark--Ocone representation and the Wiener chaos decomposition, we derive a complete hierarchy of higher-order dynamic risk premia and obtain explicit representations of the corresponding coefficients in terms of Malliavin derivatives. For mixed Wiener chaos expansions, higher-order preference measures, including prudence and temperance, emerge naturally through interactions between chaos components and are characterised using Bell polynomial representations. Explicit results for quadratic Gaussian functionals and the Vasicek interest-rate model illustrate the theory and identify a broad class of regular Wiener functionals for which the classical Arrow-Pratt approximation is recovered as the leading-order term. The results establish a unified framework linking expected utility theory, stochastic analysis and Wiener chaos expansions, opening a new perspective on higher-order certainty equivalents and the dynamic measurement of risk. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.23161 |
| By: | Herv\'e Andr\`es; Alexandre Boumezoued; Arthur Bourdon; Benjamin Jourdain |
| Abstract: | In the insurance industry, Asset and Liability Management (ALM) models are key tools for numerous applications, including Solvency Capital Requirement (SCR) computation and asset allocation optimization. However, their use often entails a significant computational cost, especially when a large number of sensitivities or stressed balance-sheet evaluations must be performed. In this work, we propose an approximation framework for the outputs of an ALM model, such as the Value In Force or the Best Estimate, based on the theory of path signatures. More precisely, the proposed approach consists of approximating ALM outputs by a linear combination of signature terms derived from input economic scenarios. We show that the resulting surrogate is easy to calibrate, essentially through regularized linear regression, and exhibits strong predictive performance while drastically reducing computational costs. We further investigate its robustness under changes in the distribution of economic scenarios by considering variations in the parameters of the underlying model of risk factors while the surrogate model is kept fixed. These results make the proposed approach particularly suitable for large-scale sensitivity analyses and fast balance-sheet evaluations in practical actuarial applications. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.24150 |
| By: | Furno, Francesco; Giannone, Domenico |
| Abstract: | We propose a simple yet robust framework to nowcast recession risk at a monthly frequency in both the United States and the Euro Area. Our nowcast leverages both macroeconomic and financial conditions, and is available the first business day after the reference month closes. In particular, we argue that financial conditions are not only useful to predict future downturns–as emphasized by the existing literature–but they are also useful to distinguish between expansions and downturns as they unfold. We then connect our recession risk nowcast with growth-at-risk by drawing on the literature on distributional regressions and quantile regressions. Finally, we benchmark our nowcast with the Survey of Professional Forecasters (SPF) and show that, while both have a similar ability to identify downturns, the former is more accurate in correctly identifying periods of expansion. |
| Keywords: | Business cycles; Financial conditions; Macroeconomic forecasting; Risk modeling and forecasting |
| JEL: | E32 C32 C53 |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:19483 |
| By: | Qi Wang |
| Abstract: | Corporate annual reports contain weakly structured evidence about foreign-exchange risk management, derivative use, natural hedging, and explicit non-use. This study develops AWARE-FX, an auditable AI/NLP decision-support system that converts report text into traceable firm-year hedging-disclosure measures. The system combines a professional-source lexicon, negation and accounting-status logic, channel-specific financial encoders, exact evidence gates, conservative aggregation, and an audit ledger. Across 24, 909 Hong Kong firm-years from 2008-2025, it retrieves and scores 543, 527 snippets. Reliability is evaluated through ablations, a stratified 300-snippet human audit, three-seed FinBERT-ModernBERT comparisons, strict 2023-2025 temporal tests, probability calibration, selective prediction, and fixed-prompt generative-model benchmarks. FinBERT has the higher mean F1 in seven of eight encoder task-split comparisons; its temporal F1 ranges from 0.702 to 0.872. Abstaining on the 20% least-confident temporal observations raises retained-sample F1 by 0.050-0.077. Deterministic Qwen3-8B performs strongly on commodity and negation evidence but poorly on foreign-debt and accounting-context labels, showing that a general-purpose LLM does not uniformly replace domain constraints. The strict FX score is negatively associated with linked baseline and stress-period FX exposure, whereas the generic broad score is not. These associations provide external construct validation, not causal estimates of hedging effectiveness. AWARE-FX contributes a tested decision-support architecture in which retrieval, status logic, classification, uncertainty handling, aggregation, and external validation remain separately auditable. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.27611 |
| By: | Chernov, Mikhail; Dahlquist, Magnus; Lochstoer, Lars |
| Abstract: | We show that a small set of emerging markets with floating exchange rates expand the investment frontier substantially relative to G10 currencies. The frontier is characterized by an out-of-sample mean-variance efficient portfolio that prices G10- and emerging markets-based trading strategies unconditionally as well as conditionally. Our approach reveals that returns to prominent trading strategies are largely driven by factors that do not command a risk premium. After real-time hedging of such unpriced risks, the Sharpe ratios of these strategies increase substantially, providing new benchmarks for currency pricing models. For instance, the Sharpe ratio of the carry strategy increases from 0.71 to 1.29. The unpriced risks are related to geographically-based currency factors, while the priced risk that drives currency risk premiums is related to aggregate consumption exposure. |
| Keywords: | Currency risk premiums |
| JEL: | F31 G12 G15 |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:19470 |
| By: | Bekaert, Geert; De Santis, Roberto; Mondino, Tomas |
| Abstract: | We investigate a large set of pricing factors for corporate bonds in the 6 largest international markets. Econometrically, we show that pricing tests of cross-sections of bond portfolios are severely compromised in small samples. Employing Barillas and Shanken (2017) tests and global portfolios, standard corporate bond factor models are rejected in favor of a model featuring the global corporate bond market, a global maturity spread factor and a global liquidity spread factor. This model also prices a wide set of cross sections, including momentum, value, idiosyncratic risk, downside risk and ratings portfolios. The model also fits currency-specific cross-sections well, except for Japanese Yen bonds. Including a local market factor improves the fit substantially for Japanese Yen bonds. All returns are hedged in US dollars as hedged corporate bond portfolios strongly outperform unhedged portfolios. |
| Keywords: | Asset pricing; Corporate bonds; Factors; Financial integration; Currency hedging |
| JEL: | G12 G15 |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:19506 |
| By: | Barba, Francesca R.; Drichoutis, Andreas; Palma, Marco A.; Cerroni, Simone |
| Abstract: | Risk preferences play a central role in farmers’ decisions, such as technology adoption, diversification, and uptake of insurance and other risk-management tools; yet agricultural economics has largely focused on risk aversion, while higher-order risk preferences remain less explored. This paper studies the external validity of experimentally elicited risk aversion, prudence, and temperance in a sample of 162 Italian winegrowers. Preferences are measured through incentivized binary lottery choices and linked to stated intentions, self-reported risk-management behavior, and an observed preventive-technology adoption. Preliminary results show that all three attitudes are present in the sample and capture distinct behavioral dimensions. Risk aversion has no systematic association with external outcomes. Prudence is positively related to stated intentions, but not to observed behavior. Temperance is associated with current insurance status and with the absence of active defense practice adoption. Overall, higher-order risk preferences show limited, outcome-specific external validity for agricultural risk-management behavior. |
| Keywords: | Risk and Uncertainty |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ags:aaea26:404409 |
| By: | Takayuki Sakuma |
| Abstract: | Hedging a derivative position under transaction costs and market frictions requires a trading rule that adapts to changing conditions. Deep hedging trains a neural policy for this task but policy training does not determine whether a trading desk can afford to run the policy. We apply robust hedging valuation adjustment (HVA) as a post-training valuation-adjustment layer that evaluates tracking-loss CVaR together with explicit funding and margin add-ons. The funding and margin add-ons share the same KL uncertainty set as HVA. For each policy, a single common-stress tilt computes HVA, funding and margin jointly and a trading desk can get one internally consistent reserve instead of the three separately. We compare classical hedge policies with learned hedge specifications across three market environments with different liquidity. No single specification dominates in every market. Under the strict tracking-risk budget, gamma-wide classical bands are selected in High and Middle Liquidity while sparse learned execution is selected in Low Liquidity. At looser validation budgets wider classical bands are generally selected. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.25258 |
| By: | Perotti, Enrico; Terovitis, Spyros |
| Abstract: | We study how a primary need for minimum safety affects investment choices. In addition to risky projects, agents may choose to invest in personal assets they can control. Investing in personal assets serves as self-insurance, as they ensure a higher minimum return but offer a lower expected return than the risky project offers. In autarky, investors can achieve safety only via self-insurance and costly liquidation of the project. Private intermediaries can reduce inefficient self-insurance by offering safe debt backed by self-insured investors holding equity and can resolve the underlying risk conflict by demandable debt. Public debt crowds out the private supply of safe assets, lowering the safe rate and aggregate investment. In contrast, deposit insurance can either decrease or increase the private supply of safe assets, as well as the safe rate and aggregate investment. Our approach explains the vast and inelastic demand for safe assets, which are hard to explain by standard preferences at times of minimal rates. |
| Keywords: | Safe assets; Demandable debt; Intermediation |
| JEL: | G21 G28 G11 G51 |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:19405 |
| By: | Rahma Mzouri (Faculté des Sciences Juridiques, Economiques et Sociales - UM5 - Université Mohammed V de Rabat [Agdal]); Abdelkrim Kandrouch (Faculté des Sciences Juridiques, Economiques et Sociales - UM5 - Université Mohammed V de Rabat [Agdal]) |
| Abstract: | Corporate failure prediction represents a major challenge for lenders, investors, and managers in a context characterized by increasing bankruptcy rates and growing economic uncertainty. Although discriminant analysis and logistic regression models have been extensively employed in the bankruptcy prediction literature, comparative studies incorporating the Partial Least Squares (PLS) method remain relatively limited, particularly in contexts characterized by high multicollinearity among financial variables.This study aims to compare the predictive performance of Linear Discriminant Analysis (LDA), Logistic Regression (Logit), and the PLS method in forecasting corporate failure.The study is based on a balanced sample of 200 Moroccan firms, including 100 failed companies and 100 non-failed companies. Thirty-three financial ratios covering financial structure, liquidity, solvency, profitability, activity, and growth were analyzed over three forecasting horizons prior to failure (T-1, T-2, and T-3). Discriminating variables were selected using Wilks' Lambda and Fisher's statistic before being incorporated into the different prediction models.The results suggest that the Logit model provides the best short-term predictive performance, achieving a classification accuracy of 93.4% at T-1, compared with 91.2% for discriminant analysis and 90.8% for the PLS method. At longer forecasting horizons, the PLS approach appears to be the most robust, with a classification accuracy of 83.2% at T-3, outperforming both discriminant analysis (78.4%) and Logistic Regression (81.3%). Ratios related to working capital, working capital requirements, solvency, and profitability emerge as the most relevant indicators for the early detection of financial distress.These findings highlight the relevance of Logit and PLS approaches for the development of early warning systems and credit risk scoring models used by financial institutions and decision-makers. |
| Abstract: | La prévision de la défaillance des entreprises constitue un enjeu majeur pour les établissements de crédit, les investisseurs et les dirigeants, dans un contexte marqué par l'augmentation des faillites et l'incertitude économique. Bien que les modèles d'analyse discriminante et de régression logistique aient été largement mobilisés dans la littérature, les comparaisons intégrant la méthode Partial Least Squares (PLS) demeurent relativement limitées, notamment dans les contextes caractérisés par une forte multicolinéarité entre les variables financières. Cette recherche vise à comparer les performances prédictives de l'analyse discriminante linéaire (AD), de la régression Logit et de la méthode PLS dans la prévision de la défaillance des entreprises.L'étude repose sur un échantillon équilibré de 200 entreprises marocaines, composé de 100 entreprises défaillantes et 100 entreprises saines. Trente-trois ratios financiers couvrant la structure financière, la liquidité, la solvabilité, la rentabilité, l'activité et la croissance ont été analysés sur trois horizons temporels précédant la défaillance (T-1, T-2 et T-3). Les variables discriminantes ont été sélectionnées à l'aide du test de Wilks et de la statistique de Fisher, puis intégrées dans les différents modèles de prévision.Les résultats suggèrentque la régression Logit présente la meilleure performance prédictive à court terme avec un taux de bonne classification de 93, 4 % à T-1, contre 91, 2 % pour l'analyse discriminante et 90, 8 % pour la méthode PLS. À horizon plus éloigné, la méthode PLS se révèle la plus robuste avec un taux de classification de 83, 2 % à T-3, supérieur à ceux obtenus par l'analyse discriminante (78, 4 %) et la régression Logit (81, 3 %). Les ratios liés au fonds de roulement, au besoin en fonds de roulement, à la solvabilité et à la rentabilité apparaissent comme les principaux indicateurs de détection précoce des difficultés financières.Ces résultats soulignent l'intérêt des approches Logit et PLS pour la mise en place de systèmes d'alerte précoce et de dispositifs de scoring du risque de crédit destinés aux institutions financières et aux décideurs. |
| Keywords: | Logistic Regression, Défaillance des entreprises Prévision de faillite Analyse discriminante Régression Logit Partial Least Squares Ratios financiers Risque de crédit. JEL classification : G32 C38 C51 M41 Recherche empirique Corporate Failure Bankruptcy Prediction Discriminant Analysis Logistic Regression Partial Least Squares (PLS) Financial Ratios Credit Risk. JEL Classification : G32, C38, C51, Credit Risk. JEL Classification : G32, Financial Ratios, Partial Least Squares (PLS), M41 Paper type : Empirical research, Discriminant Analysis, Bankruptcy Prediction, M41 Recherche empirique Corporate Failure, Risque de crédit. JEL classification : G32, Ratios financiers, Partial Least Squares, Régression Logit, Analyse discriminante, Prévision de faillite, Défaillance des entreprises |
| Date: | 2026–06–09 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05652823 |
| By: | Junyi Ye; Gargi Vijay Borde |
| Abstract: | Financial volatility is regime dependent, yet incorporating regime information into neural networks can also destabilize training. This paper asks where such information should enter a neural cross-sectional volatility forecasting model. We study five-day realized-volatility forecasts for 1, 027 U.S. equities using a rolling walk-forward evaluation framework in which information, model capacity, hyperparameter tuning, and random seeds are matched across architectures. We propose RG-ResMoE, a regime-gated residual mixture-of-experts architecture in which regime information is used only for expert routing rather than for direct forecasting. The base predictor models volatility from stock features, while a gating network uses regime state variables to route residual corrections. RG-ResMoE consistently outperforms a capacity-matched MLP in both forecasting accuracy and training stability in the main U.S. study. Similar gains are observed on an independent Japanese panel. The integration pathway is decisive: appending the same regime variables directly to the forecasting input degrades both predictive performance and training stability, whereas restricting them to the routing gate improves accuracy and Value-at-Risk calibration. Hard routing consistently underperforms soft routing. The results suggest that, in compact neural volatility forecasting models, the primary value of mixture-of-experts models lies less in increasing model capacity than in controlling how nonstationary regime information influences prediction. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.12251 |
| By: | Tsuyoshi Iwata; Johannes Laurmaa; Ryohei Hisano |
| Abstract: | The monitoring of business conduct risk is hindered by sparse, uneven, and visibility-biased data. Prior studies show that business conduct risk information and media coverage propagate through supply chain, peer, and corporate structure networks, yet incident records remain incomplete for many firms. As a result, the absence of reported events could reflect limited coverage rather than the absence of underlying business conduct risk. This paper examines whether inter-firm relationships can improve the prediction of future recorded conduct related incidents, particularly among firms with limited prior visibility. We formulate the task as Positive--Unlabeled node classification on a corporate ownership graph, where firms with recorded incidents are treated as labeled positives and firms without recorded incidents remain unlabeled. We then propose a visibility- and relation-aware GCNII framework that combines relation specific message passing with non-negative Positive--Unlabeled learning to account for positive contamination in the unlabeled set. In a forward-looking evaluation, the proposed approach achieved the strongest observed ranking performance relative to non-graph- and simple graph-based benchmarks. The results further show that graph-based inference retains its predictive value among firms without prior recorded incidents. These findings demonstrate the value of inter-firm relational structure as a complementary source of information for extending risk prioritization |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.26859 |
| By: | Seoane, Hernan D.; Yurdagul, Emircan |
| Abstract: | Expropriation risks are key to understanding Emerging Economies' stylized facts and shape the business cycles of these countries. We model a benevolent government that chooses taxation without commitment and optimal debt issuance subject to sovereign default risk. Hence, our model introduces two types of expropriation risks in a standard open economy environment. These risks can explain the sovereign debt overhang effect that has been widely documented in the literature. Moreover, we show that the sovereign debt overhang, in turn, contributes to higher default risk leading to a vicious cycle of low investment and high sovereign default risk. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:19469 |
| By: | Symeon Vaidanis; Marios Kountouris |
| Abstract: | In multi-objective and multi-criteria decision-making under risk, especially in settings involving individual behavior, risk-aware analysis based on subjective evaluation has become increasingly important. Moving beyond risk-neutral modeling and the constraints of Expected Utility Theory (EUT), Cumulative Prospect Theory (CPT) provides a behaviorally grounded framework for capturing how individuals perceive and evaluate risky prospects. This paper conducts a rigorous theoretical analysis of Neilson's definitions of aversion. We provide a gamble-based interpretation, sharpen key conceptual distinctions, and make explicit the conditions under which the weak and strong notions coincide as well as when they diverge. Furthermore, we examine the Kobberling-Wakker utility function and related standard CPT specifications, highlighting structural limitations and inconsistencies that arise when these forms are required to satisfy Neilson-type aversion conditions. To address these issues, we propose a generalized CPT-utility function that retains the canonical reference-dependent shape while offering additional flexibility. This generalization extends the descriptive scope of CPT and provides an explicit functional form that is useful for sensitivity analysis and utility function-based optimization. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.22085 |
| By: | Pierre Jacquet (ENPC - École nationale des ponts et chaussées - IP Paris - Institut Polytechnique de Paris, FERDI - Fondation pour les Etudes et Recherches sur le Développement International) |
| Abstract: | This paper analyses derisking in development finance and shows that the failure of the slogan "from billions to trillions" does not condemn the approach, but rather reveals a poorly conceived quantitative ambition. The use of public funds is legitimate only if the investment yields a social return greater than its private return, if the private sector would not have invested on its own, and if no other use of public funds is more effective. Implementing derisking requires genuine public-private engineering: detailed risk analysis, specialist expertise, robust project pipelines and the removal of regulatory barriers that hinder risk-sharing instruments. An effective derisking policy must prioritise additionality, risk assessment and risk management, address criticisms regarding its political legitimacy and the risk of subordinating the collective interest to private interests, promote guarantees in particular, and remain complementary to other development finance instruments. |
| Keywords: | Derisking, Development financing |
| Date: | 2026–07–30 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05707536 |
| By: | Elliot Quiriconi (DRM - Dauphine Recherches en Management - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres - CNRS - Centre National de la Recherche Scientifique, INERIS - Institut National de l'Environnement Industriel et des Risques); Anouck Adrot (Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres); Chabane Mazri (INERIS - Institut National de l'Environnement Industriel et des Risques) |
| Abstract: | NaTech events or risks, defined as technological accidents triggered by natural hazards, have become increasingly important in contemporary risk management. The growing frequency of disasters and intensification of natural hazards generate an urgent need for comprehensive understanding of this complex type of risk. The current literature on NaTech risks is fragmented across multiple fields and disciplines due to the transdisciplinary nature of these risks, as evidenced by the proliferation of existing literature reviews. This first version paper presents an exploratory and original systematic review of the literature that identifies and categorizes the research gaps documented in existing NaTech reviews. Using a two-dimension coding method, we identify, categorize, map and then analyze the research gaps in these reviews. The first dimension draws on the research gap typology developed by Müller-Bloch and Kranz (2014) (deductive coding). The second dimension (inductive coding) proceeds by extracting themes from the gaps. Research progress documented in these reviews is also coded using the same epistemic categories, enabling a parallel analysis of both gaps and advances. The intersection of these coding dimensions produces a bidimensional matrix (epistemic nature of the gap; thematic content). This enables us to study the evolution of these gaps through parallel documentation of both identified gaps and research progress, determining which gaps have been addressed, which persist, and which are emerging. Based on these results, we identify unidentified gaps: as a lack of ontological and epistemological questioning, a need to shift from site-centric to systemic conceptualizations, and a gap about warning and alert processes in multi-risk contexts. These gaps, complementing those already identified, may form a new research agenda for future cross-disciplinary research. |
| Keywords: | Natural hazards, Industrial hazards, Review, Research gap, NaTech |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05711012 |
| By: | Marco Bianchetti; Camilla Ricci; Marco Scaringi |
| Abstract: | The growth of peer-to-peer exchanges and the blockchain technology has led to a proliferation of cryptocurrencies and to a massive increase in the number of investors who actually negotiate digital money. Cryptocurrencies trade at prices mainly driven by investor sentiment, becoming a potential source of financial bubbles and instabilities. In this work, we apply quantitative models to the study of Bitcoin and Ether, two of the most famous cryptocurrencies. Our bubble detection methodology combines the Log Periodic Power Law (LPPL) model, originally created by Johansen, Ledoit and Sornette (JLS), and the statistical model developed by Phillips, Shi, and Yu (PSY). In particular, we employ three different versions of JLS model, i.e. Ordinary Least Square (OLS), Generalised Least Squares (GLS) and Maximum Likelihood Estimation (MLE), and two PSY statistical tests (BSADF and BSADF*). We find that, during the sample period 1st December 2016 - 16th January 2018, Bitcoin shows typical hallmarks of a bubble phase in mid December 2017 and in the first half of January 2018, anticipating the large crashes observed thereafter. Also the Ether price dynamics reveals bubble evidence in mid June 2017, anticipating the crash observed on 12th June, and a weaker signal around 12th January 2018, anticipating the crash observed in the same days. This paper confirms the high risk of speculative bubbles associated with cryptocurrencies, related to investor exuberance pumping market prices far away from their fundamental values, thus creating critical situations subject to possible crashes. Our methodology is general and can be applied to virtually any financial time series, and may support investing and risk management strategies. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.21826 |
| By: | Christian Bongiorno; Efstratios Manolakis; Rosario Nunzio Mantegna |
| Abstract: | This paper introduces a compact reformulation of a modular end-to-end neural network for global minimum-variance portfolio optimization that decouples model complexity from both look-back window length and universe size. A five-parameter hyperbolic weighted moving average combined with a saturating exponential replaces the original 2, 400-parameter lag-transformation layer, and a bidirectional gated-recurrent-unit eigencleaning module together with a streamlined marginal-volatility network reduce total learnable parameters from 39, 586 to just 2, 175. In out-of-sample tests against state-of-the-art nonlinear-shrinkage and risk-parity benchmarks, the compact network attains the lowest realized portfolio variance without compromising expected return. Under long-only constraints, the variance reduction supports substantially higher leverage while maintaining comparable drawdown control. Validation in a high-fidelity trading simulator that incorporates realistic margin-call dynamics confirms enhanced over-leverage resilience. These findings demonstrate that end-to-end variance-minimization architectures can achieve substantial parameter efficiency and robust capital-efficiency gains without sacrificing risk-adjusted performance. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.23068 |