|
on Risk Management |
| By: | Buczak, Maciej |
| Abstract: | The aim of this article is to analyse the 2023 crisis episode - the failures of regional US banks - from the perspective of the effectiveness of the post-crisis financial safety architecture developed in the aftermath of the 2008 global financial crisis. Drawing on a review of the literature and regulatory documents, the article reconstructs the sequence of events, identifies their causes, and confronts them with the paradigms underlying modern banking supervision. The analysis shows that the banks involved in the crisis episode met their capital requirements and, in many cases, their liquidity requirements as well. The source of the problems lay in qualitative factors: poor risk management, unsustainable business models, and inadequate supervision. The episode also revealed a new mechanics of banking crises - social media and digital banking compressed the timeline of deposit runs from days to hours, fundamentally altering the dynamics of tail risk events. The article's main conclusion is that the existing regulatory framework, built on the paradigm of normally distributed phenomena and quantitative risk measures, is ill-suited to this new reality. Distribution tails are fatter, and future crises may be more violent and harder to contain than previously assumed. The 2023 episode, though limited in scale, should be treated as a warning signal for the financial safety system as a whole. |
| Keywords: | banking crisis; systemic risk; prudential regulation; Basel III; tail risk; deposit run; digi-tal banking; Silicon Valley Bank |
| JEL: | C52 F37 G01 G21 G32 |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:129944 |
| By: | Corrado De Vecchi; Max Nendel; Steven Vanduffel |
| Abstract: | We study risk aggregation problems for arbitrary non-decreasing aggregation functions and tail risk measures under dependence uncertainty in a distributionally robust setting. To this end, we introduce the notion of hidden dependence for random vectors, which is built on the concepts of risk concentration and common tail events developed in Wang and Zitikis (2020). We show that, starting from a tail event $A$ of the aggregate loss for an arbitrary random vector $Y$, one can construct a random vector with hidden dependence that dominates $Y$ on the tail event $A$. We then focus on the case in which model uncertainty is described by small perturbations of the distribution of a random vector with respect to a suitable probability distance without changing the marginals. We show that these perturbations of the reference distribution are compatible with hidden dependence and thus lead to the same worst-case risk bounds as in the unconstrained case for arbitrary $\gamma$-tail risk measures with a suitable level $\gamma$. Finally, we apply our results in a credit risk context and quantify the potential underestimation of portfolio risk arising from uncertainty in the dependence structure. In particular, we show that even small deviations from a reference Gaussian dependence model can, in principle, justify dramatic increases in capital requirements. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.30193 |
| By: | Mathias Beiglb\"ock; Silvana M. Pesenti; Maxime Sylvestre |
| Abstract: | In static risk measurement, law invariance expresses the principle that the risk of a position should depend only on its distribution, and not on the particular probability space on which it is represented. In a dynamic setting, the same principle leads naturally to adapted law invariance: the risk assessment should depend only on the probabilistic structure of the financial position together with the way information about it is revealed over time. We show that, for time-consistent risk measures, adapted law invariance is equivalent to a recursive one-step conditional-law representation. More precisely, assuming Fatou regularity, the one-step risk evaluations are exactly conditional lifts of static law-invariant risk measures, and the full dynamic risk measure is obtained by backward composition of these one-step maps. Convexity and coherence of the dynamic risk measure are characterized by the corresponding properties of the static one-step risk measures. This identifies adapted law invariance as the dynamic counterpart of ordinary law invariance. It also clarifies the strength of terminal-law invariance, as it appears in the rigidity theorem of Kupper and Schachermayer: it does not distinguish risks with the same distribution but different times of resolution. We further obtain an adapted Kusuoka representation in the coherent case and establish an extension of the Kupper--Schachermayer theorem. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.04392 |
| By: | Chen, Damiaan; Beetsma, Roel; van Wijnbergen, Sweder |
| Abstract: | We explore to what extent real returns on investment portfolios can be hedged against inflation risk by using existing financial market instruments. We find that inflation-linked bonds offer only limited protection against inflation risk and that nominal debt and stocks play at least comparable roles in this respect. These findings apply to both a static and a dynamic setting. The demonstrated limits of hedging inflation risk are of particular relevance for long-term investors, such as pension funds with participants concerned about the real value of their pension benefits. |
| JEL: | C61 E21 G11 G23 |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:20720 |
| By: | Haji Mohamad Zubir, Ahmad Shauqi bin; Mohd Nasir, Muhammad Luqman bin |
| Abstract: | Trading volume has forecast volatility in half a century of research, yet risk models hold capital at extreme quantiles the volume literature never scores. We evaluate the expected and unexpected components of trading volume inside a layered tail risk architecture, a volatility filter beneath an extreme value model for standardized exceedances, for 300 liquid Bursa Malaysia firms over 467, 148 evaluation days from 2018 to 2025, with every model re-estimated annually in real time and forecasts ranked by strictly consistent scoring functions. Four results emerge. Unexpected volume improves 99th and 99.5th percentile forecasts by about a tenth of a percent of the quantile loss, on 58 percent of days, at unchanged coverage and capital, robust to a pre-committed battery including a full pipeline permutation placebo. The signal is layer specific: surprise volume works entirely through the volatility filter and adds nothing to the exceedance scale at any threshold, the exclusion that return subordination implies, while expected volume compresses the extreme tail from within, the signature of market depth. The gains concentrate in 2021 through 2025, dating the signal's value to a documented transformation of the trading environment. And direct censored likelihood quantification shows this market's price limits shift implied extreme quantiles by half a basis point, licensing standard architectures by measurement rather than assumption. Volume belongs in risk systems, in a specific place, at a measurable price. |
| Keywords: | Trading volume; Value at risk; Extreme value theory; Forecast evaluation; Market depth; Price limits |
| JEL: | C1 G12 |
| Date: | 2026–07–22 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:130162 |
| By: | Mavus Kutuk, Merve; van Wijnbergen, Sweder |
| Abstract: | This paper examines the role of currency crash risk in explaining the persistent profitability of carry trades. Focusing on the US Dollar–Turkish Lira market, we construct three forward-looking measures of crash risk: risk reversals, crash probabilities from option-implied distributions, and jump risk from a jump-diffusion model. Using survey-based exchange rate expectations, we separate ex ante carry premia from ex post surprises. Our results show that higher crash risk significantly increases expected returns, indicating that investors demand compensation for bearing such risk rather than arbitraging away mispricing. Shapley decomposition attributes over 20\% of the explained variance in expected carry returns to crash risk, while balance sheet constraints and global risk aversion further reinforce premia. A comparison of hedged and unhedged strategies reveals that 46–77\% of carry returns reflect compensation for crash exposure. |
| Keywords: | Exchange rates |
| JEL: | F13 G01 G10 G12 G15 |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:20745 |
| By: | Whelan, Karl |
| Abstract: | We analyze sequential investment strategies that expose capital to asymmetric payoff structures — scenarios in which losses are small and frequent, while gains are large but rare. This setup generalizes the classical gambler's ruin problem, traditionally framed as a symmetric game, into a framework for modeling repeated financial decisions under uncertainty. Payoff asymmetry is common in domains such as venture capital, tail-risk hedging, and derivative strategies. We consider cases where each investment has positive, zero, or negative expected return, and derive analytic results for ruin probabilities, expected final wealth, and game duration. Our findings show that increasing asymmetry — higher potential rewards but lower success probability — can paradoxically increase the likelihood of ruin in positive-return settings and mitigate it when returns are negative. For zero-return strategies, we establish bounds on ruin probabilities and show that convergence to terminal outcomes is faster when payoffs are skewed. The results have implications for portfolio risk management and capital allocation in repeated-risk environments. |
| JEL: | G11 G17 |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:20544 |
| By: | Zhen-Yuan Ralph Liu (CUMT - China University of Mining and Technology); Yu-Ting Wang (NFU - Nanjing Forestry University); Jia-Jia Yan (School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350116, China and Engineering Research Center for Metal Rubber, Fuzhou University, Fuzhou 350116, China., Fuzhou University [Fuzhou]); Shivam Gupta (NEOMA - Neoma Business School); Mihalis Giannakis (Audencia Business School) |
| Abstract: | Despite the extensive discussions of human-centric AI (HCAI) in Industry 5.0, its effects on firms' idiosyncratic risks (IR) remains underexplored. This is an imperative issue for firms navigate financial risks during the current technological revolution, as IR reflects investor reactions to corporate heterogeneous AI strategies and implementations by isolating firm-level stock volatility from systematic factors. Integrating situated AI theory with social-technical systems theory, we conceptualise HCAI as a situated AI strategy that reduces AI-related ethical risks and fosters AI-Human synergies in firms' business operations, ultimately reducing IR by aligning with stakeholders' diverse expectations. Moreover, socio-technical factors, namely digitalisation, operational efficiency, executive shareholding, and CEOs with IT background, may moderate the HCAI-IR relationship. Using a multi-source panel dataset of Chinese listed firms from 2015 to 2023, we find that HCAI is associated with lower firm IR. Furthermore, digitalisation and executive shareholding strengthen this risk-reducing effect, whereas operational efficiency and CEOs with IT background surprisingly attenuate it. Our findings offer theoretical contributions and practical insights for both ethical AI governance and firm financial risk management in the AI era. |
| Keywords: | Large language models, Socio-technical systems theory, Situating AI theory, Idiosyncratic risk, Human-centric AI, Human-centric AI Idiosyncratic risk Situating AI theory Socio-technical systems theory Large language models |
| Date: | 2026–06–20 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05665153 |
| By: | Niushan Gao; Denny H. Leung; Foivos Xanthos |
| Abstract: | Prudence is a stability property of risk functionals recently introduced by Wang and Zitikis and subsequently studied by Amarante and Liebrich. In this paper, we first establish general relationships between prudence and other stability properties, showing, in particular, that weak prudence and prudence coincide for a broad class of convex, law-invariant functionals. We then prove that prudence is preserved by cash-additive hulls of star-shaped functionals under a simple asymptotic condition, and by inf-convolutions of convex, cash-additive, law-invariant prudent functionals. Our results provide general methods for constructing prudent risk measures from existing prudent functionals. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.21871 |
| By: | Choi, Bokmin; Degryse, Hans; Smedts, Kristien |
| Abstract: | Firms are increasingly exposed to cybersecurity risks. Using syndicated loan data covering US firms, we examine how lenders price firms’ ex-ante cybersecurity risks. Our findings indicate that lenders on average charge a 4 to 13 basis points higher loan rate when a firm exhibits greater cybersecurity risks over time. Furthermore, we document that the pricing of cybersecurity risks differs between lender types, as well as within the same lender type depending on their level of awareness of the risks. Commercial banks tend to adopt a more stringent approach to pricing cybersecurity risks compared to non-bank lenders. They also impose more financial covenants and monitor more intensively as firms become riskier. Even within commercial banks, the pricing of cybersecurity risks is primarily driven by lenders who recognize their own cybersecurity risks and have discussed internal risk management policies. These findings highlight the importance of lender awareness on the pricing of unconventional risks. Lastly, the purchase of cybersecurity insurance does not mitigate higher loan spreads. |
| Keywords: | Insurance |
| JEL: | G21 G23 |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:20335 |
| By: | Cecchetti, Stephen; Kress, Jeremy; Schoenholtz, Kermit L. |
| Abstract: | In 2023, US regulators proposed the “Basel Endgame, †a long-awaited overhaul of bank capital requirements. The proposal aimed to bring the United States into compliance with international standards established by the Basel Committee on Banking Supervision in response to the 2008 Global Financial Crisis. However, fierce industry opposition to what banks viewed as a costly increase in capital requirements effectively killed the proposal. In this essay, we describe the purpose of bank capital and the history of international standard-setting in bank regulation. We then highlight the most important aspects of the Basel Endgame, as well as the arguments for and against adopting the rule. We show that the debate unnecessarily conflated two distinct questions: (1) whether the United States should comply with international regulatory standards, and (2) whether the United States should raise large banks’ capital requirements. While there are strong grounds to answer both questions in the affirmative, they need not be addressed together. That is, the United States can implement international standards in a capital-neutral manner to preserve global cooperation in bank regulation, leaving the separate question of raising capital requirements for another day. |
| Keywords: | Basel committee on banking supervision; Bank regulation; Capital requirements; International Standard Setting |
| JEL: | G23 |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:20386 |
| By: | Matteo Ferrari; Roger J. A. Laeven; Emanuela Rosazza Gianin; Marco Zullino |
| Abstract: | Financial resilience concerns the rate at which a position recovers, or further deteriorates, in response to adverse conditions. As a first step, Laeven, Ferrari, Rosazza Gianin, and Zullino (arXiv:2505.07502) introduced the resilience rate, defined as the expected instantaneous rate of (favorable) change of a price or risk-assessment process. Since this quantity captures only the conditional mean of future increments, it cannot distinguish between positions having the same expected recovery but different conditional risk profiles. We obtain a richer characterization by evaluating such increments through a genuine, possibly nonlinear, dynamic risk measure. More precisely, for an It\^o process $\pi$ and a normalized, cash-additive dynamic risk measure $\rho$, we define the resilience evaluation by \[\mathcal D_s^\rho\pi_t := L^1\text{-}\lim_{\varepsilon\to0^+} \frac{1}{\varepsilon}\rho_s(\pi_{t+\varepsilon}-\pi_t), \qquad 0\leq s\leq t |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.30070 |
| By: | Hyung Joo Kim; Dong Hwan Oh |
| Abstract: | Despite documented heterogeneity in volatility dynamics across the option surface, standard implied volatility forecasting models apply homogeneous parameters throughout. We introduce a machine-learning framework that uses regression trees to partition the surface along both moneyness and maturity dimensions, identifying data-driven regions where distinct forecasting models perform best. Extending the Surface Heterogeneous Autoregressive (SHAR) framework of Dufays, Jacobs, and Rombouts (2025), we develop tree-based SHAR specifications that preserve interpretable structure while allowing model parameters to vary across the surface. Empirical analysis using S&P 500 options demonstrates that the boosted tree-based specification achieves the lowest out-of-sample forecast errors across all horizons, reducing one-month-ahead RMSE by 13 percent versus the benchmark SHAR model. The improvements are statistically significant and particularly pronounced during stress periods. The estimated tree presents economically interpretable segmentation: short-dated options exhibit higher daily persistence but lower monthly persistence than long-dated options, while deep out-of-the-money calls or puts display distinct dynamics from near-the-money contracts. |
| Keywords: | implied volatility forecasting; option surface; machine learning; regression trees; ensemble methods; heterogeneous autoregressive models |
| JEL: | C14 C22 C32 C51 C53 C58 G12 |
| Date: | 2026–07–06 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedgfe:103519 |
| By: | Ujjwala Vadrevu |
| Abstract: | The adoption of non-parametric machine learning models for regulatory capital estimation introduces a fundamental governance challenge: the inability to explain model outputs in a manner auditable by supervisory bodies. This 'black box' problem remains a major barrier to the adoption of Gaussian Process Regression (GPR) and related ML architectures in ICAAP and CCAR workflows despite their predictive advantages over traditional parametric approaches. This paper addresses this barrier through SHARC (SHAP for Regulatory Capital), an explainability framework for the Hybrid GPR-HS architecture and its stress-testing extension. SHapley Additive exPlanations (SHAP), derived from cooperative game theory and satisfying the properties of Local Accuracy, Missingness, Consistency, and Efficiency, are applied to Stressed Value-at-Risk (SVaR) outputs under three macro scenarios: West Asia War, Climate Risk, and AI Bubble/Regulatory Burden. SHARC decomposes SVaR into baseline, mean-driven, and volatility-driven components, enabling transparent linkage between scenario design and capital outcomes. Two findings emerge. First, SHARC consistently links non-linear SVaR outputs to underlying scenario inputs, confirming framework fidelity and providing auditable traceability of capital drivers. Second, under stress conditions, the mean return component (directional loss magnitude) dominates the variance component (volatility baseline) in determining capital levels, with implications for capital limit-setting, position management, and hedging strategy. The results establish SHARC as a regulator-aligned explainability layer that makes the Hybrid GPR-HS framework fully auditable and consistent with FRTB, ICAAP Pillar 2, and CCAR transparency requirements. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.05484 |
| By: | Ryan Parker; Mark Stedman; Luca Capriotti |
| Abstract: | Using the path-integral formalism, we develop an accurate and easy-to-compute semi-analytical approximation for a general class of {default intensity} models. We illustrate the accuracy of the method by presenting results for the Black-Karasinski model for which the proposed approximation provides remarkably accurate results, even in regimes of high volatility and multi-year time horizons. The accuracy and the computational efficiency of the proposed approximation makes it a viable alternative to fully numerical schemes for a variety of applications in econometrics and derivatives pricing, including the computation of XVA for credit products. As a practical example, we consider the pricing of a quanto Credit Default Swap (CDS) under stochastic intensity of default and an FX devaluation model. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.21800 |
| By: | William W. Lamptey; Nicholas Appiah; Abootaleb Shirvani; Priscilla Ati-Tay; Svetlozar T. Rachev; Frank J. Fabozzi |
| Abstract: | This paper examines portfolio optimization and tail-risk analytics for a heterogeneous universe of actively managed investment funds. Using daily Bloomberg data for 30 funds from 4 December 2020 to 24 December 2025, the study evaluates buy-and-hold, mean--variance, CVaR-based, and tangency-type strategies under long-only and long--short constraints. The sample consists predominantly of actively managed ETFs, with PTTRX retained as an actively managed fixed-income mutual-fund comparator. The results show substantial heterogeneity across thematic equity, fixed-income, income-oriented, multi-asset, and alternative strategies, creating both diversification opportunities and meaningful differences in volatility, drawdown behavior, downside exposure, and tail risk. Historical results indicate that tangency-type portfolios are generally the strongest competitors to the buy-and-hold benchmark in cumulative and risk-adjusted terms, while minimum-variance and CVaR-minimizing portfolios sacrifice upside participation for stronger downside control. Dynamic allocation does not improve all strategies uniformly: the long-only dynamic CVaR-95 portfolio is consistently attractive across several risk-adjusted criteria, whereas long--short dynamic tangency-CVaR portfolios perform strongly but are more sensitive to turnover and implementation costs. Tail-risk diagnostics based on empirical VaR, Expected Shortfall, maximum drawdown, left-tail Hill estimators, and POT--GPD methods show that downside tail exposure remains meaningful after portfolio aggregation. Overall, actively managed ETFs are best evaluated as components of a joint investment opportunity set in which dependence structure, portfolio design, dynamic allocation, implementation frictions, and tail-risk exposure jointly shape performance. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.03082 |
| By: | Bertaut, Carol; Faia, Ester; Kalemli-Ozcan, Sebnem; Marchesini, Camilo; Paetzold, Simon; Schmitz, Martin |
| Abstract: | We use administrative security-level data from the U.S. and Euro Area (EA) portfolios to estimate asset demand and supply elasticities by exploiting exogenous variation in bond-specific currency wedges. Employing a Bartik-style shift-share identification approach, we document extensive heterogeneity in investor demand responsiveness to exogenous changes in the price of currency risk, conditional on the issuer characteristics. Demand for AE-bonds is always inelastic, whereas for EM-bonds, elasticity depends on investor type and currency: insurance/pension, nonbanks and banks have finite-elastic demand for EM-bonds that are issued in their own (investor) currency. For EM-issuer-currency bonds, only EA non-bank investors increase the share of these bonds in their portfolio when currency wedges widen, suggesting they accept higher currency risk for higher returns. In response, issuers adjust their supply endogenously: an exogenous increase of 8 basis point in currency wedges leads to a 0.26% decline in local currency bond issuance relative to GDP. We develop a theoretical framework where debt issuance decisions take into account heterogenous demand of investors in terms of their response to changes in the price of currency risk. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:20665 |
| By: | Marcellino, Massimiliano; Pfarrhofer, Michael |
| Abstract: | We compare homoskedastic and heteroskedastic mixed frequency (MF) vector autoregression and Bayesian additive regression tree (BART) models to assess their relative performance in predicting tail risk. MF-BART is a nonlinear state space model, and we discuss linear approximation approaches to devise computationally efficient estimation algorithms. The models are applied in an out-of-sample backcasting, nowcasting and forecasting exercise for a set of quarterly and monthly macroeconomic variables in Italy. The proposed econometric refinements yield improvements in predictive accuracy. |
| Keywords: | Mixed frequency |
| JEL: | C11 C22 C53 E31 E37 |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:20442 |
| By: | Riedel, Frank (Center for Mathematical Economics, Bielefeld University); Spengemann, Marco (Center for Mathematical Economics, Bielefeld University) |
| Abstract: | We study optimal insurance design under linear transaction costs for a policyholder with smooth ambiguity preferences when the underlying loss distribution is identifiable ex post. Under expected utility, Arrow’s classical theorem implies that the optimal contract is a straight deductible. Under ambiguity, this result generally fails. We show that ex post identifiability restores the straight-deductible structure: the optimal contract consists of model-contingent straight deductibles. We also derive comparative statics with respect to transaction costs and characterize the ordering of deductibles under stochastic ordering of loss distributions. |
| Keywords: | Insurance design, risk sharing, smooth ambiguity aversion, identifiability, straight deductible, comparative statics |
| Date: | 2026–07–22 |
| URL: | https://d.repec.org/n?u=RePEc:bie:wpaper:767 |
| By: | \`Oscar Bur\'es; Rafael De Santiago |
| Abstract: | We propose a signature-based framework for the identification of stochastic volatility model classes from observed path data. By mapping volatility trajectories into a feature space via truncated path signatures and applying a gradient boosting classifier, we show that it is possible to distinguish between different classes of volatility dynamics without relying on parametric calibration. Through a series of numerical experiments, we demonstrate that the method achieves high classification accuracy across a range of settings, from structurally distinct models to cases involving rough volatility models with closely spaced Hurst parameters. We show that the method remains effective under parameter uncertainty, where each simulated path is drawn with randomly sampled model parameters, and provide a detailed analysis of the misclassification pattern between the Heston and Ornstein--Uhlenbeck models in terms of the volatility of volatility parameter. The results highlight that most of the relevant discriminative information is captured by the first four levels of the signature, while higher-order terms provide only marginal improvements. Overall, the findings support the view that stochastic volatility models can be effectively identified through the geometry of their sample paths. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.06340 |
| By: | Andrea De Polis (Banco de España); Álvaro Fernández-Gallardo (Banco de España); José Nicolás Rosas (Banco de España) |
| Abstract: | We analyze the asymmetric transmission of oil supply news shocks to the inflation distribution in the United States, the euro area, and the United Kingdom. Using quantile local projections and high-frequency identification, we document a stark asymmetry across these three large advanced economies: while median responses are transitory, the 90th quantile exhibits significant and persistent increases beyond one year. This upside tail sensitivity, consistent with state-dependent pricing, suggests that supply shocks are structural innovations to the skewness of the inflation distribution. Monetary policy should actively monitor such persistent inflation tail risks to keep expectations anchored. |
| Keywords: | oil supply shocks, inflation at risk, quantile local projections, non-linearities, state-dependence |
| JEL: | E31 E52 Q43 |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:bde:wpaper:2622 |
| By: | Ghosh, Pulak; Limodio, Nicola; Vats, Nishant |
| Abstract: | This paper investigates the effect of deposit insurance (DI) on household portfolio allocation between bank deposits and risky assets. Theoretically, limited DI creates a kink in the capital allocation line, causing depositor bunching at the DI threshold and increased equity holdings. Using a natural experiment in India and individual holdings on stocks, deposits, and mutual funds, we confirm depositor bunching at the DI threshold. Leveraging a bunching-in-differences design, we show that DI expansion shifts portfolios from equities and mutual funds to deposits, driven by unmet demand for safe assets. Bunchers increase their deposits between 3.6% and 5.1% by liquidating their stock holdings, which were more exposed to safer state-owned enterprises, transiently affecting the asset prices of these stocks. We show that the share of bunchers is a sufficient statistic to measure the depositor-implied risk of bank default. Our estimates of the welfare effect of changes in DI show that depositors gain at least 4% as DI increases, even after accounting for the resulting moral hazard by banks. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:20753 |
| By: | Cossu, Fenicia; Moro, Alessio; Mottola, Andrea |
| Abstract: | We investigate the role of risk in shaping the pattern of structural transformation of an economy. We first show a novel theoretical result: in a simple two-sector model, for any given level of GDP, higher microeconomic risk (e.g. volatility of TFP shocks) implies a smaller share of services in consumption. This occurs because higher income risk induces the representative household to increase precautionary savings, thus reducing consumption expenditure, whose level determines the structure of consumption due to non-homotheticity. The value added share of services also declines with higher risk, due to the increase in goods intensive investment relative to services intensive consumption. Time-series and cross-sectional U.S. data confirm a negative and statistically significant relationship between different measures of risk and the share of services, in both value added and consumption data. This relationship also holds in South-American and Asian countries experiencing premature de-industrialization. As these countries faced lower risk relative to the U.S. during their development, the proposed mechanism can account for part of their premature de-industrialization. Our estimates suggest that, had the U.S. experienced the same GDP volatility before WWII as it did after, its average services share would be 0.023 percentage points higher - explaining roughly 30% of the gap with premature de-industrializers at comparable income levels. |
| Keywords: | Strucural Change; Income volatility; Precautionary savings |
| JEL: | L16 E21 E30 |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:20594 |
| By: | Anand, Kartik; Kazinnik, Sophia; Leonello, Agnese; Panetti, Ettore |
| Abstract: | Does artificial intelligence (AI) pose a threat to financial stability? We study AI investor behavior, specifically Q-learning and large language model (LLM) investors, in a mutual fund redemption problem with economic and strategic uncertainty. Different AI architectures generate systematically different outcomes. Q-learning investors coordinate well but under default risk exhibit excessive redemption that amplifies fragility. LLM investors internalize equilibrium structure but display belief heterogeneity, weakening coordination and predictability. Our findings show that AI architecture is a first-order determinant of financial stability. |
| Keywords: | Coordination games; Financial stability; Q-learning; Large Language Models; Artificial intelligence; Strategic uncertainty |
| JEL: | G01 G23 C63 |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:20681 |
| By: | Buczak, Maciej |
| Abstract: | In 2024, the Basel Committee on Banking Supervision, implementing the CRR III regulation, withdrew the Advanced Measurement Approach for operational risk (AMA) — a model built on an actuarial foundation — ending its twenty-year history in the banking sector. The official justification was excessive dispersion of model results across institutions, undermining comparability of capital requirements. This article argues that such dispersion was merely a symptom — the deeper problem being a structural inconsistency of the framework, embedded in its assumptions from the outset. Three structural properties of the AMA framework are derived. Property (1) concerns the domination of distant quantiles of the aggregate loss distribution by a single extreme loss when heavy-tailed severity distributions are applied. Property (2) demonstrates that the AMA model outcome is determined by quantiles of the severity distribution higher by one or more orders of magnitude than the regulatory quantile of 0.999. Property (3) indicates the dependence of the extreme loss value — which determines the model outcome — on the count of minor, negligible operational events. The occurrence of these properties was verified by a numerical Monte Carlo experiment. The results indicate that the combined effect of the derived properties led to the necessity of estimation at quantiles of the order 0.9999, 0.99999 and higher — levels beyond the capabilities of reliable statistical modeling given data volumes available in banking practice. Also critical was the ambiguity in the definition of rarity, and the transfer of that concept onto the single loss distribution axis — a transfer arising from the joint occurrence of properties (1), (2), and (3). Rarity readings on this axis depended on the quantile positioning of extreme losses, which could itself depend on the count of low-value, negligible losses. The observed dispersion of results across institutions may therefore have been a consequence of these unintended structural inadequacies, rather than the result of modeling errors. |
| Keywords: | operational risk; AMA; LDA method; heavy-tailed distributions; extreme value theory; Basel II; CRR III |
| JEL: | C51 C52 C53 G21 G32 |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:129937 |
| By: | Swaraj Gambhir; Thanu George; Kairavi Sivasankar |
| Abstract: | Markowitz defined portfolio risk as an internal property, built from the covariance among a book's own holdings rather than the distance to any index. Seventy years of simplification reversed that. The market beta of CAPM, the fixed style and industry axes of Barra-type models, and the promotion of benchmark deviation to the definition of risk all traded the inward view for an external one. Risk became distance from an index. For a fund that fits no benchmark, that trade fails. A global book concentrated in a few markets and a few innovation sectors has no natural index to deviate from, and the active-risk number it produces measures the mismatch, not the risk. We return to the covariance. Principal component analysis (PCA) recovers the systematic structure inside the portfolio directly from its own returns. PCA has always carried one cost: its factors resist a plain-English reading. We clear that with a generative-AI labelling layer. It names the leading factors, ranked by their actual contribution to risk rather than by universe variance, and a deterministic rubric keeps it from inventing structure the loadings do not contain. Around this sit four independent signals. Density-based clustering with a mismatch ratio flags groups whose risk outruns their capital. A sign-invariant PCA Risk Score (PRS) marks the names that build the dominant factor bets. A standalone Bleed score catches the slow capital destroyers PCA cannot see. A trailing-return timing gate routes disagreements between the risk signals and recent price action to human judgment. We run the full engine on a proxy global-innovation book of thirty names over one year. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.07465 |
| By: | Anand Deo |
| Abstract: | Financial stress tests based on handpicked scenarios can mislead risk management by overlooking genuinely dangerous configurations or overemphasising shocks that are too implausible to be decision-relevant. We develop a systematic method for generating plausible stress scenarios for financial losses driven by exogenous risk factors. The method exploits a large-deviations principle: conditional on a large loss, the risk factors concentrate near the most likely stress configurations. We use this structure to define representative stress distributions and to extrapolate observed samples into more extreme scenarios while preserving the relative plausibility of stress mechanisms. As a result, the procedure can generate informative stress scenarios even when historical data contain few or no observations in the stressed regime. Numerical experiments on two financial network models show that the method recovers the stressed loss law and key stress diagnostics, including in settings where benchmark generators fail to generate any stressed samples. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.31122 |
| By: | Mantu Gupta; Anand Deo |
| Abstract: | We introduce Self-Similar Generative Estimation (SS-GEN), a method for simulating multivariate tail events and estimating rare-event probabilities in both heavy and light-tailed settings. SS-GEN exploits asymptotic tail structure to decompose the tail distribution into an explicit radial component and a nonparametric angular component, reducing tail learning to a compact-domain problem that can be handled by off-the-shelf deep generative models. The resulting sampler generates representative extreme scenarios and supports probability estimation far beyond the observed data. Under mild nonparametric tail assumptions, we show that the SS-GEN density is asymptotically exact in the tail, with vanishing uniform relative error for regularly varying distributions and vanishing uniform log-relative error for Weibull-type distributions. Unlike existing approaches that rely on specialized architectures or parametric tail specifications, SS-GEN leverages asymptotic tail structure to enable standard generative models to generate representative extreme samples and estimate rare-event probabilities beyond the observed data. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.10700 |
| By: | Albert Kutej; Stefan Rass |
| Abstract: | The consideration of uncertainty is a central but frequently inadequately addressed component of risk management. A systematic treatment of uncertainty is essential for ensuring the quality and traceability of decision-making processes, particularly in complex and safety-critical environments. This review systematically analyzes how established risk management approaches conceptualize and represent uncertainty in both their theoretical foundations and practical applications. Based on a systematic literature review of 370 publications, the identified approaches are classified into five methodological families. These include probabilistic methods, evidence-based and fuzzy-logic approaches, qualitative elicitation techniques, graphical and visual representations and hybrid frameworks. The analysis shows that probabilistic methods remain predominant due to their quantitative rigor, whereas fuzzy and evidence-based approaches are particularly suited to addressing vagueness and epistemic uncertainty. Qualitative and graphical approaches are found to enhance interpretive understanding and support the transparent communication of uncertainty. Despite these developments, the analysis indicates that the practical integration of these approaches into operational risk management remains limited in many domains. The findings highlight the need for more structured guidance in method selection and suggest that future research would benefit from further development of hybrid approaches and visualization techniques. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.27804 |
| By: | Li, Junye; Sarno, Lucio; Zinna, Gabriele |
| Abstract: | Using model-free skewness measures that exploit the asymmetry in semivariances and option data from the over-the-counter currency market, we find that buying currencies with a high skewness risk premium (SRP) and selling currencies with a low SRP generates high returns and Sharpe ratio. Asset pricing tests – which control for omitted variables and measurement errors – show that a SRP factor enters the currency pricing kernel and is central to the pricing of risks inherent in a broad currency cross-section of 60 portfolio excess returns. These results imply that skewness risk is a strong and priced source of currency risk. |
| JEL: | F31 G12 G15 |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:20587 |
| By: | Rosie Hayward; Orla Lennon; Fabio Biancalana |
| Abstract: | Extreme events in financial systems, often captured by indicators such as volatility, remain difficult to identify close to their onset. Volatility shares many statistical properties with other natural, complex systems which experience extreme events, which we explore in this manuscript. We extend the analogy between rogue waves in optical and hydrodynamical systems to financial volatility by identifying rogue-wave-like peaks with similar statistical properties. We use a Schr\"odinger equation where the potential follows the shape of a Kerr nonlinearity to examine the properties of financial volatility indices within a moving time window. We see evidence of Anderson localisation as a rogue peak approaches in the VIX, and show that the numerical gradient of the system's minimum eigenvalue reliably spikes at the onset of an extreme event. We adapt our methodology to simulate the real-time arrival of data, and show that all but one of the VIX's major peaks can be detected given a reasonable amount of history. We then perform two out-of-sample tests, one for the VXO index, and one for the VSTOXX index, and successfully replicate our initial results, identifying all but one major peak (87.5% or 7/8) in both cases. This method of analysis shows considerable promise as a tool for identifying potential financial crises, aiding in their mitigation. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.31475 |
| By: | Viet Nguyen-Tien |
| Abstract: | We study the effect of input uncertainty about critical minerals on firm performance, separating the second-moment risk channel from first-moment mineral sentiment and from general firm-level uncertainty. Using earnings-call transcripts matched to financial data for more than 14, 000 publicly listed firms in 92 countries (2010-2022), we construct text-based measures of perceived critical-mineral risk. Higher perceived risk is robustly associated with lower revenue growth among downstream non-mining firms, consistent with risk-averse firms contracting output under input uncertainty. A one-standard-deviation increase in mineral risk is associated with 0.71 percentage points lower revenue growth for the average non-mining firm, rising to roughly 1.9 percentage points for smaller firms, and is concentrated in thinly traded minerals (lithium, cobalt, rare earths) rather than deeply traded ones (copper, nickel). Firms discuss hedging an stockpiling in response to price volatility rather than price levels, revealing the risk aversion that underlies the output contraction. These findings highlight a new uncertainty channel in the green transition relevant to strategic stockpiling and price transparency. |
| Keywords: | critical minerals, green transition, risk, exposure, sentiment, stockpiling, hedging |
| Date: | 2026–07–02 |
| URL: | https://d.repec.org/n?u=RePEc:cep:cepdps:dp2197 |
| By: | van Binsbergen, Jules; Cocco, João F.; Grotteria, Marco; Naaraayanan, Lakshmi |
| Abstract: | We quantify the impact of perceived cancer risk on housing values using widely advertised national reclassifications of chemical carcinogenicity in the United States. Combining these information events with an empirical design that compares changes in house values closer to affected toxic plants against those farther away isolates the effect of cancer risk news from other local factors. Focusing on plants previously emitting reclassified carcinogenic chemicals, we estimate a 1-2% decline in housing values within a 3-mile radius compared to those located farther away. The effects are stronger in areas with higher media presence underscoring the role of salience as a mechanism. |
| Keywords: | Salience; Real estate prices; Household finance |
| JEL: | G11 G50 Q51 |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:20512 |
| By: | Chi, Ta-Chung; Fan, Ting-Han; Ghigliazza, Raffaele; Giannone, Domenico; Wang, Zixuan (Kevin) |
| Abstract: | We forecast the full conditional distribution of macroeconomic outcomes by systematically integrating three key principles: using high-dimensional data with appropriate regularization, adopting rigorous out-of-sample validation procedures, and incorporating nonlinearities. By exploiting the rich information embedded in a large set of macroeconomic and financial predictors, we produce accurate predictions of the entire profile of macroeconomic risk in real time. Our findings show that regularization via shrinkage is essential to control model complexity, while introducing nonlinearities yields limited improvements in predictive accuracy. Out-of-sample validation plays a critical role in selecting model architecture and preventing overfitting. |
| Keywords: | Regularization |
| JEL: | C22 C52 C53 C55 |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:20727 |
| By: | Zijun Meng |
| Abstract: | This paper proves a sum-minimization characterization of Pareto efficient insurance with multiple policyholders, multiple insurers, and multiple indemnity environments. We also provide a result regarding the pairwise implementability of the policyholder- and insurer-aggregate level arrangements in the multiple policyholders and multiple insurers setting. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.30779 |
| By: | Alex Chan |
| Abstract: | I study insurance markets in which scalable prediction, like AI, designs residual risk rather than merely classifies fixed risk. A complete-contracting benchmark shows that if prevention is observable, contractible, competitively supplied, and fully priced, it does not matter whether consumers, insurers, or vendors supply it. Adverse selection breaks such irrelevance. When high-risk consumers are more "AI-treatable, " efficient prevention makes low-risk contracts attractive to them. A contract intended for low-risk consumers faces a risk-design trilemma: separate, prevent efficiently, or avoid cross-subsidy, but not all three. The result extends Rothschild-Stiglitz from distorted coverage to distorted risk-control technology and offers market design insights of AI in insurance markets. |
| JEL: | D4 D47 D81 D82 D86 G22 G52 I13 O33 |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35444 |
| By: | Aldasoro, Inaki; Hördahl, Peter; Schrimpf, Andreas; Zhu, Sonya |
| Abstract: | Using newly constructed market conditions indicators (MCIs) for three pivotal markets centered around the US dollar (Treasury, foreign exchange, and money markets), we demonstrate that tree-based machine learning (ML) models significantly outperform traditional time-series approaches in predicting the full distribution of future market stress. Through quantile regressions, we show that the random forest method achieves up to 27\% lower quantile loss than autoregressive benchmarks, particularly at longer horizons (up to 12 months). Shapley value analysis reveals that variables related to macro expectations and uncertainty — especially about the monetary policy stance — are important predictors of future tail realizations of market conditions. For individual market segments, the state of the global financial cycle, as well as liquidity conditions, also play important roles. These results highlight the value of ML in forecasting tail risks and identifying systemic vulnerabilities in real time, bridging the gap between high-frequency data and macroeconomic stability frameworks. |
| Keywords: | Shapley value |
| JEL: | G01 C53 G17 G12 G28 |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:20439 |
| By: | Aluma Dembo; Shachar Kariv; Matthew Polisson; John K. -H. Quah |
| Abstract: | Ellsberg's famous paradox challenged Savage's subjective expected utility theory (EUT) -- which reduces uncertainty to risk -- by suggesting an aversion toward ambiguity. We provide a revealed preference test of the full set of axioms underpinning subjective EUT under uncertainty and compare it to an analogous test of objective EUT under risk. We find that individual choices are as consistent with utility maximization and expected utility maximization under uncertainty as they are under risk. Nevertheless, there is greater empirical scope for non-EUT models under uncertainty than under risk, and the absolute and relative consistency of EUT and non-EUT models vary considerably across subjects. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.09355 |
| By: | Sichao He; Yansong Zhang |
| Abstract: | In a deep forecasting pipeline for fat-tailed financial returns at short horizons, which matters more - the backbone architecture or the output head? We compare four modern backbones (TimesNet, DLinear, N-BEATS, iTransformer) under three output heads: a point head, a single-Gaussian density head, and a Gaussian mixture density head with K=4 components. On S and P 500 monthly log-returns (1871-2023) under anchored walk-forward validation, the three heads form a strict gradient: switching from point to Gaussian improves CRPS by about 1.3 percent; switching from Gaussian to mixture adds a further about 2.4 percent. Switching between backbones, in contrast, changes CRPS by less than 1.5 percent on the point-head row and on the backbone-mean axis; density-head backbone spread is larger (up to 5.1 percent on the h=1 Gaussian row, driven by N-BEATS) but the head gradient (3.7 percentage points) still dominates. The Model Confidence Set on squared errors does not exclude any of the 12 variants at the 5 percent level: the head separates them only on distributional metrics (CRPS, pinball, coverage), not on squared error. The mixture head incremental value over a single Gaussian is largest in the highest-volatility regimes (13.9 percent in 1970s stagflation at h=12), confirming the mixture captures tail risk beyond what a unimodal Gaussian can express. The picture is horizon-dependent: the head dominates at short horizons, but at long horizons (h >= 6) the backbone re-takes the lead - an h-split we document against classical baselines (section 5.1). We conclude that on fat-tailed returns at short horizons, the head dominates the backbone, and the mixture distribution adds genuine value over a single Gaussian during crisis periods when risk-management decisions actually matter. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.30037 |
| By: | Jun Sekine; Marcus Wunsch |
| Abstract: | We study a continuous-time portfolio optimization problem in which an investor is evaluated relative to a non-replicable benchmark and seeks to control the persistence of benchmark-relative underperformance. We introduce a benchmark-relative drawdown-duration criterion that penalizes the expected discounted time spent in unfavorable benchmark-relative performance states. Despite the path dependence induced by benchmark-relative drawdowns, we show that the problem admits a one-dimensional Markovian representation and derive the associated Hamilton-Jacobi-Bellman equation. We obtain an explicit projection-based characterization of the optimal feedback control, establish a verification theorem, and identify geometric settings under which the associated closed-loop reflected diffusion admits a unique strong solution. Our results provide a tractable downside-risk-oriented alternative to classical benchmark-tracking formulations and reveal a novel projection-based control structure for benchmark-relative risk management. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.11335 |
| By: | Albuquerque, Rui; Zawadowski, Adam |
| Abstract: | A maturity wall arises when private credit funds reach the end of their term and can no longer roll over loans. Unlike banks, which face no maturity wall, private credit funds use the maturity wall as a credible commitment to liquidate distressed borrowers, thereby strengthening ex-ante incentives, albeit at the cost of ine!cient liquidation. The model predicts that private credit ex-pands aggregate credit supply, reallocates riskier loans away from banks, and amplifies aggregate downturns. Private credit funds emerge as a mechanism that approximates the second-best optimal contract when full commitment is unattainable. |
| Keywords: | Systemic risk |
| JEL: | G21 G23 G28 |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:20737 |
| By: | Chambers, David; Dimson, Elroy; Ilmanen, Antti; Rintamäki, Paul |
| Abstract: | The literature on long-run asset returns has continued to grow steadily, particularly since the start of the new millennium. We survey this expanding body of evidence on historical return premia across the major asset classes — stocks, bonds, and real assets — over the very long run. In addition, we discuss the benefits and pitfalls of these long-run data sets and make suggestions on best practice in compiling and using such data. We report the magnitude of these risk premia over the current and previous two centuries, and we compare estimates from alternative data compilers. We conclude by proposing some promising directions for future research. |
| Keywords: | Asset pricing; historical returns; stock market index; Investment management; risk premium; stocks; Bonds; Real estate; Commodities |
| JEL: | G11 G12 G32 G38 N20 |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:20800 |
| By: | Castells-Jauregui, Madalen; Heider, Florian; Hoerova, Marie; Calomiris, Charles |
| Abstract: | We develop a general equilibrium theory of financial intermediation and its implications for liquidity regulation. The model is built around an agency problem arising from leveraged intermediation: banks finance loan origination with deposits and face moral hazard in risk management, while holding cash mitigates these incentives at the cost of foregone investment returns. Liquidity demand therefore emerges endogenously from incentive considerations rather than from exposure to exogenous funding shocks. In equilibrium, financial experts choose between allocating equity to the banking sector relative to the non-bank financial sector that can provide ex-post liquidity by buying bank assets. Asset prices are determined endogenously in liquidation states, linking banks’ ex ante liquidity choices to market liquidity and the allocation of intermediation across sectors. Comparing the decentralized equilibrium to a planner’s allocation, we show that liquidity regulation mandating higher cash holdings improves incentives within banks but is not sufficient to implement the efficient allocation. In particular, it leads to an inefficiently large banking sector that free-rides on liquidity provision by non-bank investors. Implementing the planner’s allocation requires a second policy instrument, such as limits on bank size or subsidies to equity-financed liquidity provision outside the banking sector. JEL Classification: G21, G28, D82, D6 |
| Keywords: | agency problems, financial intermediation, general equilibrium, liquidity regulation |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:ecb:ecbwps:20263252 |
| By: | Lagrotta, Luiz Carlos Nacif |
| Abstract: | The insolvency of private concessionaires exposes a structural paradox of privatized infrastructure. Concession contracts formally allocate operational, financial, and commercial risks to private operators. Yet when the operator of an essential public service collapses, the State cannot always behave as a passive third party. The service must continue, users must be protected, and public authorities remain politically and legally tied to the function they delegated. The concessionaire is therefore not simply “too big to fail”; it is too public to fail. This article argues that concessionaire insolvency should not be treated merely as a problem of civil procedure, contract enforcement, or bankruptcy law. It is a problem of public governance and institutional risk allocation. Using Brazil as a case study, especially the repetitive appeal proceeding in ProAfR no REsp 2, 005, 469/RJ, the article examines whether enforcement proceedings may be redirected against a public entity when a public service concessionaire becomes insolvent, even though the public authority did not participate in the original adjudicatory phase and is not named in the enforceable title. The article develops a comparative and theoretical framework. It uses the United Kingdom’s Thames Water crisis and the water Special Administration Regime as a living example of continuity without ordinary corporate rescue; the European Union’s concession framework as a legal model centered on operating risk; and the United States as a limited analogy for infrastructure distress, public-private partnerships, and special insolvency regimes. The argument draws on Michael Mann’s concept of infrastructural power, Ayres and Braithwaite’s responsive regulation, Brandon Garrett’s critique of negotiated corporate governance, Jennifer Arlen’s incentive-based theory of corporate liability, and the law-and-economics literature on PPP renegotiation and risk allocation. The article defends a principle of continuity without public insurance. The State must preserve essential services, but it should not automatically absorb the concessionaire’s debts. Public liability should be exceptional, legally grounded, and connected to specific public conduct, statutory assumption, contractual obligation, or continuity measure. The public function may be too important to fail, but the private operator remains private enough to bear the consequences of its own risks. |
| Date: | 2026–07–09 |
| URL: | https://d.repec.org/n?u=RePEc:osf:lawarc:gfy8x_v1 |
| By: | Xavier Fonseca |
| Abstract: | The global minimum-variance portfolio (GMVP) is the canonical decision built from an estimated covariance matrix, yet covariance estimators are universally evaluated by matrix-norm loss, which is not the object the decision depends on. We characterise exactly how covariance-estimation error maps into GMVP suboptimality. We prove an exact regret identity and a non-asymptotic bound showing decision regret depends on the estimation error only through its action on the portfolio weights, scaled by portfolio concentration and the conditioning of the true covariance. From this we derive the decision geometry: GMVP regret is invariant to a (p-1)-dimensional projection of the p^2-dimensional error matrix, with invariance to the covariance-scale direction as an exact special case. We then apply the framework to heavy-tailed returns (tail index kappa in (2, 4)), establishing the regret convergence rate implied by the centred operator-norm rate, and confirm the theory on a skew-t/t-copula simulation design with pre-registered analysis. The decision-focused advantage is a sharper constant and a concentration discount rather than a faster rate; we report an honest high-conditioning boundary of the rate prediction. The results complement recent decision-focused learning approaches by supplying the exact estimation geometry and consistency theory they lack. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.27462 |
| By: | Anastasis Kratsios; Giulia Livieri; Philipp Schmocker |
| Abstract: | We address fundamental challenges in representing and computing $\mathbb{R}^{d}$-valued predictable square-integrable processes over $[0, T]$, collected in the space $\mathcal{H}^2_T(\mathbb{R}^{d})$. These processes are central to continuous-time stochastic control, reinforcement learning, and mathematical finance. Although Wiener-chaos expansions offer strong theoretical tools, traditional computational methods are hindered by the need for large chaos dictionaries and high-order iterated integrals. To overcome these obstacles, we introduce NeuralChaos -- a neural operator architecture that produces elements of $\mathcal{H}^2_T(\mathbb{R}^{d})$ using only finitely many evaluations of the driving Brownian motion, while preserving predictability and square-integrability. We prove that NeuralChaos is dense in $\mathcal{H}^2_T(\mathbb{R}^{d})$ and achieves the best $N$-term chaoslet approximation rates for compressible and Malliavin--Sobolev regular processes. Moreover, compressibility is shown to be typical for processes from $\mathcal{H}^2_T(\mathbb{R}^{d})$ under non-degenerate sub-Gaussian sampling. In contrast, we show that finite-dimensional Markovian neural SDE models constitute a meagre and Gaussian-null subset in $\mathcal{H}^2_T(\mathbb{R}^{d})$, regardless of discretization, whereas compressible processes are generic. Numerical experiments on a stochastic optimal control problem and dynamic hedging highlight the practical effectiveness of our approach. Our results enable more efficient and expressive modelling in stochastic analysis and mathematical finance. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.14361 |
| By: | Han Feng; Difang Huang; Jue Wang; Zhengjun Zhang |
| Abstract: | We explain the long-standing puzzle of na\"ive diversification with a simple, testable condition: equal weighting is minimum-variance optimal when the forecast-error covariance matrix has a uniform eigenstructure. This "Golden Criterion" drives a two-stage adaptive strategy that dynamically blends naive and optimized weights based on the empirical distance from this condition. Applied to U.S. equity premium forecasting, the method delivers consistent out-of-sample gains in forecast accuracy, utility, and Sharpe ratios. Diversity-driven shrinkage dominates at short horizons, while optimized weights regain their edge at longer horizons, offering clear horizon-dependent guidance for portfolio construction. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.11054 |
| By: | Alejandro Rodriguez Dominguez |
| Abstract: | We formalize a single structural condition on a portfolio problem, causal separation: conditional on the realized path of a declared set of drivers through the investment horizon, asset returns are mutually independent. From this condition we derive the complete static portfolio theory it induces. Separation forces a diagonal-plus-low-rank conditional covariance through an exact tower decomposition, with the low-rank block identified as the response to driver innovations, and the constrained mean-variance problem admits a closed-form projected Markowitz solution in which the classical information matrix is replaced by its projection onto the constraint-compatible subspace. We prove uniqueness of the minimal sufficient separator as an information set; invariance of all derived objects under separator equivalence and reparametrization of the driver state; a conditional efficient-frontier theorem; a Hansen-Jagannathan bound whose gap to the unconstrained bound is an exact quadratic form in the shadow prices of the constraint geometry; a conditioning bound showing that separation regularizes estimation through the idiosyncratic variance floor; and exact first-order sensitivity bounds under approximate separation at a certified tolerance. The causal content is stated and proved rather than assumed: under an explicit structural margin the common causes form a separator, observational data identify their realized information and no more, and causal and correlational separators of equal fit are distinguished by interventions on non-parents. Seven reproducible experiments validate the theory, with structural identities holding at machine precision, and quantify its practical content against sample, shrinkage and principal-component covariances, including robustness, intervention invariance, and scaling to thousands of assets. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.05320 |
| By: | Federico D. Forte |
| Abstract: | This paper provides the first analysis of credit relationships between financial institutions and firms through the lens of hypergraphs. Unlike traditional network approaches, which rely on pairwise connections, this framework explicitly represents the shared exposure of multiple financial institutions to the same firm as a simultaneous multilateral relationship. The approach is applied empirically to Credit Registry data from the Central Bank of Argentina, covering the period from August 2023 to December 2025 and focusing on commercial loans between banks and firms. Traditional centrality metrics are compared with hypergraph-specific measures to identify systemically relevant institutions. The paper also proposes an adjusted version of H-eigenvector centrality that nonlinearly weights both the centrality of neighboring institutions and each creditor's lending amount, in order to assess the relevance of a bank within the network. The systemic impact of shocking the top-ranked institutions according to each centrality metric is then estimated through an adaptation of the DebtRank algorithm. The results show that the proposed framework identifies institutions with greater shock-amplification capacity, providing a complementary tool for financial supervision and regulation. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.10943 |
| By: | Jorge Abad (EUROPEAN CENTRAL BANK); Saki Bigio (UCLA AND NBER); Salomón García-Villegas (CUNEF UNIVERSITY); Joël Marbet (BANCO DE ESPAÑA); Galo Nuño (BANCO DE ESPAÑA, CEMFI AND CEPR) |
| Abstract: | How does heterogeneity in banks’ interest-rate risk exposure shape monetary policy transmission? We develop a quantitative macroeconomic model of heterogeneous banks to answer this question. We establish an irrelevance result: differences in interest-rate risk exposure between fixed- and variable-rate banking systems matter for transmission only when bank solvency concerns become relevant. By calibrating the model to the euro area, we show that idiosyncratic default risk pushes a substantial share of banks toward the solvency threshold, making heterogeneity quantitatively important. When policy rates rise, fixed-rate banks’ net interest margin is compressed – funding costs increase while legacy loan income stays unchanged – eroding capital and triggering sharper deleveraging. The elasticity of lending to monetary policy is one-third higher in fixed-rate economies. The effects extend to financial stability: tightening raises bank failure rates in fixed-rate systems while lowering them in variable-rate systems. The results provide a rationale for macroprudential and monetary policy coordination and for monetary policy gradualism. |
| Keywords: | Monetary policy transmission, bank lending channel, heterogeneous banks, interest-rate risk, fixed-rate loans, variable-rate loans, bank solvency, bank capital, macroprudential policy, euro area |
| JEL: | E44 E52 E58 G21 G28 |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:bde:wpaper:2620 |
| By: | Corsetti, Giancarlo; Lloyd, Simon; Marin, Emile; Ostry, Daniel |
| Abstract: | We document a rise in investors’ assessment of U.S. risk relative to other G.7 economies since the late 1990s, driven by higher permanent risk but not reflected in currency returns. Using a two-country framework with trade in a rich maturity structure of bonds which earn convenience yields, alongside risky assets and currencies, we establish an equilibrium relationship between cross-border convenience yields, relative country risk and carry-trade returns. Empirically, we identify a cointegrating relationship between relative permanent risk and long-maturity convenience yields. Counterfactual experiments show rising relative permanent risk explains around one-third of declining long-maturity convenience yields over 2002-2006 and 2010-2014. |
| Keywords: | Equity risk premium |
| JEL: | F30 F31 G12 |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:20657 |
| By: | Crosignani, Matteo; Hiti, Martin |
| Abstract: | We introduce the first comprehensive publicly available dataset on county-level damages, injuries, and fatalities from natural disasters in the U.S. and present a few facts on the economic and human costs of extreme weather events. Our source is the National Oceanic and Atmospheric Administration’s Storm Events Database, which reports losses for geographic areas largely defined based on meteorological science. We map these areas to counties using geographic tools together with the spatial distribution of population, housing stock, and economic activity. Our estimates are particularly accurate for severe disasters. The Losses from Natural Disasters data is regularly updated at newyorkfed.org/research/policy/natural-d isaster-losses. |
| Keywords: | Natural disasters |
| JEL: | H12 H71 Q54 |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:20516 |
| By: | Bruno E. Holtz; Carlos A. Abanto-Valle; Ricardo S. Ehlers; Gabriel Rodr\'iguez |
| Abstract: | This paper extends the approximate Bayesian estimation framework for Stochastic Volatility in Mean (SVM) models to accommodate heavy-tailed distributions from the Scale Mixture of Normals (SMN) family. To overcome the computational challenges arising from these models, we propose a numerically stable estimation procedure that exploits special functions to eliminate the need for direct numerical integration. Furthermore, the implementation incorporates parallel computing strategies that substantially reduce computational costs. Simulation studies and empirical applications demonstrate that the proposed approach delivers accurate inference while achieving computational times that are approximately an order of magnitude smaller than those required by conventional Markov chain Monte Carlo (MCMC) methods. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.22615 |