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on Risk Management |
| By: | Claude Lefevre; Pierre Zuyderhoff |
| Abstract: | This paper investigates risk measures derived from the expected maximum deficit in a continuous-time framework and develops optimal reserve allocation strategies across multiple lines of business. We formalize the expected maximum deficit and study its associated distortion risk measures. Furthermore, we introduce implicitly bounded risk measures based on the minimal capital required to meet prescribed fixed and proportional risk tolerances, and propose approaches for optimal capital allocation using line-specific distorted expected deficits. Theoretical results established include static coherence and convexity properties, dynamic conditional extensions detailing supermartingale time consistency over a fixed horizon and the evolution of capital requirements across rolling horizons, and exact analytical optimizations of the aggregate minimum reserve. |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2605.16448 |
| By: | Patrick Woitschig; Mike West |
| Abstract: | We present a new class of Bayesian dynamic models for bivariate price-realized volatility time series in financial forecasting. A novel dynamic gamma process model adopted for realized volatility is integrated with traditional Bayesian dynamic linear models (DLMs) for asset price series. This represents reduced-form volatility leverage and feedback effects through use of realized volatility proxies in conditional DLMs for prices or returns, coupled with the synthesis of higher frequency data to track and anticipate volatility fluctuations. Analysis is computationally straightforward, extending conjugate-form Bayesian analyses for sequential filtering and model monitoring with simple and direct simulation for forecasting. A main applied setting is equity return forecasting with daily prices and realized volatility from high-frequency, intraday data. Detailed empirical studies of multiple S&P sector ETFs highlight the improvements achievable in asset price forecasting relative to standard models and deliver contextual insights on the nature and practical relevance of volatility leverage and feedback effects. The analytic structure and negligible extra computational cost will enable scaling to higher dimensions for multivariate price series forecasting for decouple/recouple portfolio construction and risk management applications. |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2605.12099 |
| By: | Maxim Bichuch; Zachary Feinstein |
| Abstract: | Financial options are fundamental to traditional markets, enabling strategies ranging from hedging to speculating. Yet, while the Automated Market Maker paradigm has revolutionized decentralized spot markets, no equivalent standard has emerged for on-chain options. Typical designs attempt to replicate centralized exchange mechanics, requiring high-frequency oracles and robust liquidation engines which may fail during stress events. This paper presents a design for amortizing perpetual options tailored to the operational and adversarial constraints of blockchain environments. Leveraging this primitive, we introduce a decentralized market framework with minimal consistency requirements. We demonstrate that this contract functions as a foundational risk primitive for DeFi, enabling applications such as endogenous collateralization and explicitly priced de-peg insurance, thereby showing that this design provides a layer for mutualizing tail risk across protocols without reliance on centralized clearing institutions. |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2605.19146 |
| By: | Max Nendel |
| Abstract: | The aggregation of individual risks in large credit and insurance portfolios is guided by diversification and the law of large numbers, which formalizes the convergence of sample averages to their means. At the same time, regulatory capital requirements and insurance premia are designed to provide a capital buffer or risk margin above the mean. The resulting excess, given by the difference between the nonlinear valuation of the aggregate loss and the corresponding mean, reflects the idea of protection against unexpected losses in the sense of banking and insurance regulation. This paper studies the asymptotic behaviour of this excess for large weighted portfolios. The main result shows that, for monotone cash-additive risk measures on Banach-lattice-valued Orlicz spaces, convergence along weighted averages satisfying a weak law of large numbers together with a uniform integrability condition is equivalent to scalar continuity at the origin. If the risk measure is positively homogeneous, this continuity condition is automatically satisfied, and we prove that the unexpected losses of large weighted portfolios are of order $o(n\overline\lambda_n)$, where $\overline\lambda_n$ denotes the average weight assigned to the first $n$ random variables. We establish analogous asymptotic results for Choquet insurance premia. Finally, we derive risk-ratio limits that quantify the potential underestimation arising when diversified portfolios are compared with co-monotonic alternatives. |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2605.18049 |
| By: | Yosuke Fukunishi (The Graduate School of Economics, The University of Tokyo); Haorong Qiu (Formerly Graduate School of Economics, The University of Tokyo); Akihiko Takahashi (The University of Tokyo) |
| Abstract: | Modeling the probability distribution of stock returns is a fundamental challenge in quantitative finance, with significant implications for risk management, derivative pricing, and portfolio optimization. This paper proposes a diffusion-based generative framework tailored to the statistical characteristics of financial return distributions. By incorporating learned reverse-process variance, velocity parameterization, and a sigmoid noise schedule, the proposed model aims to improve distributional fidelity, particularly in the tails. The framework is further extended to regime-conditional generation, enabling controlled simulation of distinct market states. Empirical evaluations demonstrate that the proposed approach outperforms classical parametric models such as Geometric Brownian Motion and GARCH, deep generative baselines like VAEs, and existing diffusion-based methods across multiple distributional metrics, including higher-order moments and tail behaviors. The results highlight the potential of diffusion models as robust tools for synthetic return generation and scenario analysis in finance. |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:tky:fseres:2026cf1273 |
| By: | Gabriel Rodriguez (Departamento de Economía de la Pontificia Universidad Católica del Perú); Fiorela Liza (Pontificia Universidad Católica del Perú); Miguel Ataurima Arellano (CAF-Development Bank of Latin America and the Caribbean y Pontificia Universidad Católica del Perú) |
| Abstract: | Using daily equity market data for Latin American (Latam) and high-income (HI) countries over 2008-2023, this paper estimates GARCH and GJR models to forecast Value at Risk (VaR) and Expected Shortfall (ES). The performance of a broad set of heavy-tailed and asymmetric distributions is evaluated, including the Normal (N), Skewed Normal (skN), Student’s t (S), skewed S (skS), generalized hyperbolic skS (GHskS), normal inverse Gaussian (NIG), skewed NIG (skNIG), normal reciprocal inverse Gaussian (NRIG), and skewed NRIG (skNRIG). The key findings can be summarized as follows: (i) for VaR forecasting, asymmetric distributionsare preferred at both confidence levels, and at the 99% level heavy tails are also required; (ii) for ES forecasting, at both confidence levels the selected models rely on asymmetric heavy-tailed distributions, with GHskS emerging as the dominant specification; (iii) for VaR forecasting, modeling leverage effects is necessary for most HI countries, whereas this is required for only about half of the Latam countries; and (iv) for ES forecasting, volatility specification plays a more limited role than in VaR forecasting. Palabras claves: Valor al Riesgo, Pérdida Esperada, Modelos GARCH, Distribuciones de Colas Pesadas, Países LATAM, Países de Altos Ingresos, Mercados Bursátiles, Mercados Forex. JEL Classification-JE: C52, C53, G17 |
| Keywords: | Value at Risk, Expected Shortfall, GARCH Models, Heavy-Tailed Distributions, Latin American Countries, High-Income Countries, Equity Markets, Forex Markets. |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:pcp:pucwps:wp00554 |
| By: | Hirbod Assa |
| Abstract: | Risk governance is not only about identifying and measuring adverse states of the world. It also asks when an institution is entitled to rely on a risk claim. This paper introduces modal epistemic tools for that second layer of QRM. For a risk proposition $p$, $Kp$ denotes assurance-grade endorsement for certification, audit reliance, board sign-off, or regulatory reporting. By contrast, $Bp$ denotes working commitment: a disciplined action-guiding stance under incomplete assurance. The framework distinguishes object-level risk claims from stances toward them. It develops crisp and fuzzy modal semantics for assurance, working commitment, live possibility, non-exclusion, hesitation, and epistemic inconsistency. The central diagnostics are \[ p\wedge\neg Kp \qquad\text{and}\qquad p\wedge\neg Bp, \] which identify cases in which a risk is present but lacks the relevant stance. Thus QRM should model not only hazards and losses, but also evidential incompleteness, model risk, validation gaps, and failures of escalation. Two governance principles motivate the analysis. The Risk Management Principle says that if $p$ is a risk, then the absence of the relevant stance, $p\wedge\neg Mp$, is itself risk-relevant. The Risk Reach Principle says that real and decision-relevant risks should be reachable by the appropriate stance. Their unrestricted combination creates Moorean and Fitch-style collapse pressure: treating $p\wedge\neg Kp$ or $p\wedge\neg Bp$ as ordinary targets of the same stance whose absence they record undermines the diagnostic. The response is architectural. Object-level risk claims should be separated from meta-level epistemic diagnostics. The latter should be governed through an audit layer that records and controls epistemic gaps. This preserves action and precaution without collapsing risk governance into institutional omniscience. |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2605.11200 |
| By: | Ujjwala Vadrevu |
| Abstract: | Accurate forecasting of the Volatility-Covariance Matrix (VCV) is central to regulatory capital adequacy processes such as the Internal Capital Adequacy Assessment Process (ICAAP) and the Comprehensive Capital Analysis and Review (CCAR). Traditional econometric models, including GARCH-family and Exponentially Weighted Moving Average (EWMA) approaches, suffer from parametric rigidity, distributional assumptions, and numerical instability under stress, leading to systematic underestimation of tail risk. This paper proposes and validates a novel Hybrid Gaussian Process Regression-Historical Simulation (GPR-HS) framework for estimating Value-at-Risk (VaR) and Expected Shortfall (ES) across a diversified portfolio of seven major global equity indices. The framework decouples the VCV estimation problem: individual asset volatilities are modelled dynamically using Univariate GPR with a Matern 5/2 kernel, while inter-asset correlations are estimated via stable historical covariance. A key methodological contribution is the Aggressive Noise Initialization (ANI) strategy, which sets the initial White Noise kernel variance equal to the empirical variance of the training returns, ensuring Gram matrix positive-definiteness, regularization, and conservative, regulatory-compliant forecasts. Evaluated using an expanding window forward-chaining cross-validation scheme over June 2020 -June 2025, the GPR-HS framework achieves regulatory compliance in the majority of test splits; including a 100% ES pass rate at the portfolio level, while outperforming the static Historical VaR benchmark in 71.4% of univariate cases by Quadratic Loss and 100% of cases by violation count. |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2605.17275 |
| By: | Kirill Zernikov (New Economic School) |
| Abstract: | This paper studies empirical deep hedging for S&P 500 index options under a local downside-shortfall reward. It moves beyond performance comparison by asking what the learned hedge does, when it fails, and whether it can be made auditable. TD3 agents are compared with a daily-updated Black-Scholes delta hedge on the same option episodes. In walk-forward tests from 2015 to 2023, the agents usually learn a systematic delta haircut relative to Black-Scholes. The correction is explained by spot-implied-volatility co-movement and often improves accumulated reward and terminal downside variance, but it is regime-fragile: 2022 exposes losses in adverse daily states, while 2023 shows that underhedging can raise ordinary variance when option P&L is spot-dominated and the volatility channel is unusually weak. Symbolic regression distills the neural policies into compact formulas that can be traded out of sample; these formulas preserve much of the reward, downside-variance, and CVaR advantage over Black-Scholes, and sometimes sharpen it, but inherit the same fragility in difficult regimes. |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2605.21696 |
| By: | Bondarenko, Yevheniia; Kang, Nayeon; Lewis, Vivien; Rottner, Matthias; Schüler, Yves |
| Abstract: | Geopolitical risk is a major concern for the euro area, yet widely used measures largely reflect a US perspective. We introduce a geopolitical risk indicator tailored to the euro area using local European news sources. Shocks to this index have significant recessionary and inflationary consequences in the euro area, effects that would be missed when relying on the corresponding US-based measure. We estimate that the Russo-Ukrainian War imposed substantial output losses and inflationary pressures on the euro area in 2022. Combining structural scenario analysis with end-of-sample now-casting, we show that euro area prospects are highly sensitive to future developments in geopolitical risk. We complement these analyses with two news-based measures of sanctions intensity and shortages for the euro area. |
| Keywords: | euro area, geopolitical risk, inflation, sanctions, shortages |
| JEL: | E31 E32 F42 F51 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:bubdps:341100 |
| By: | Sven Fuhrmann; Michael Kupper; Max Nendel |
| Abstract: | We study a class of dynamically consistent risk measures that robustify a time-homogeneous Markovian reference model by allowing for distributional uncertainty in its transition laws. We start from one-step convex risk evaluations in which ambiguity is captured by penalized worst-case expectations over alternative transition laws. Imposing time consistency then yields a convex monotone semigroup on bounded continuous payoff functions, and this semigroup represents the associated dynamic risk measure. The semigroup is uniquely characterized by its risk generator. Under a lower bound on the family of penalties in terms of suitable optimal transport costs relative to the reference laws, we identify the generator on smooth test functions. For optimal transport bounds with linear small-time scaling, this produces a first-order, drift-type correction given by a convex Hamiltonian acting on the gradient. Under martingale transport constraints and a different scaling, however, the leading correction is genuinely of second order and is described by a convex monotone functional acting on the Hessian. We illustrate both regimes for Wasserstein and martingale Wasserstein penalizations and derive explicit formulas via convex conjugates of the underlying transport costs. The associated dynamic risk measures admit stochastic control representations in which the control acts on the drift in the first-order case and on the volatility in the second-order case. |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2605.21759 |
| By: | Gregory A. Fanous |
| Abstract: | Volatility is the language in which finance often describes risk, but it is not the language in which institutions experience risk. Allocators live through drawdowns, liquidity needs, spending rules, rebalance decisions, board oversight, and the interval between a prior high-water mark and full recovery. This paper develops a path-dependent framework for asymmetric volatility management. The arithmetic of recovery is nonlinear: after a drawdown of depth $D$, the required gain is $R=\frac{1}{1-D}-1$. Lower volatility can improve geometric compounding through the familiar small-return approximation $g \approx \mu-\frac{1}{2}\sigma^2$, but symmetric de-risking can also impair recovery if it sacrifices too much upside participation. The relevant design problem is therefore not volatility reduction in isolation; it is conditional exposure shaping. Skew engineering is defined here as the portfolio construction discipline of reducing harmful downside participation more than productive upside participation, controlling submergence, and preserving enough recovery participation to sustain compounding under adverse regimes. The resulting Recovery-Efficiency Protocol links drawdown depth, time underwater, recovery burden reduction, and rebound participation into an allocator-facing reporting discipline. Machine learning and AI methods are framed as tools for conditional estimation, regime mapping, robustness testing, and model-risk governance, not as market prediction. |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2605.09123 |
| By: | Caio Gomes (Magalu) |
| Abstract: | Capped-usage SaaS products -- LLM subscriptions such as Claude Code and ChatGPT, cloud platforms such as Vercel and Cloudflare Workers, corporate benefit platforms, identity-verification services with liability transfer -- share a structural signature with insurance products: a fixed premium decoupled from realized consumption, stochastic per-user demand with heavy-tailed severity, a non-fungible cap that resets on a fixed schedule, and a portfolio-level exposure that requires reserve adequacy under tail risk. We argue that this is not an analogy. It is the same operational problem actuarial science has been tooled for decades to address, restated with new dependent variables (tokens, bandwidth bytes, function-invocations, gym check-ins) in place of medical claims. This paper proposes a modeling framework for capped-usage SaaS pricing built from frequency-severity decomposition, premium calculation principles, and Monte Carlo reserve adequacy. We map the framework to publicly observable subscription tiers in two domains (LLM services and cloud platforms), ground it in canonical health-insurance economics (Arrow 1963; Pauly 1968; Manning et al. 1987; Brot-Goldberg et al. 2017), and demonstrate divergence from traditional unit economics through a worked example. The contribution is operational rather than theoretical: not a new theorem, but vocabulary and tools currently absent from cs.LG/stat.ML practice. |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2605.16699 |
| By: | Julian Atanassov (University of Nebraska); Gabriele Lattanzio (University of New Hampshire); Bektemir Ysmailov (Nazarbayev University, Graduate School of Business) |
| Abstract: | We document that during periods of heightened policy uncertainty, firms rebalance their cash portfolios away from riskier marketable securities and toward safer, more liquid assets. Our findings are robust to instrumental variable analyses, alternative model specifications, and varying definitions of policy uncertainty. The effect is stronger among financially constrained firms, firms with greater external financing needs, firms in highly competitive product markets, and highly intangible firms - consistent with precautionary motives. However, we also find evidence consistent with an investment-delay channel, whereby heightened policy uncertainty induces firms to postpone investment, creating temporary excess liquidity that may be allocated to marketable securities. By uncovering this nuanced behavior, our findings provide new insights into corporate financial decision-making under uncertainty and how firms manage liquidity and risk. |
| Keywords: | political risk, corporate cash, precautionary savings, real options, investment |
| JEL: | G30 G31 G32 |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:asx:nugsbw:2024-03 |
| By: | Philippe Goulet Coulombe |
| Abstract: | Average forecast accuracy is not the same as forecast reliability. I treat forecast loss differentials relative to a benchmark as a return series. I then evaluate these returns using risk-adjusted performance measures from finance, including the Sharpe ratio, Sortino ratio, Omega ratio, and drawdown-based metrics. I also introduce the Edge Ratio capturing a model's propensity to deliver uniquely informative predictions relative to the forecasting frontier. I apply this framework to U.S. macroeconomic forecasting, comparing econometric benchmarks, machine learning models, a foundation model (TabPFN), and the Survey of Professional Forecasters. While it is often feasible to beat professional forecasters in terms of average accuracy, it is much harder to beat them on a risk-adjusted basis. They rarely exhibit catastrophic failures and often achieve high Edge Ratios, plausibly reflecting the value of contextual judgment. Nonetheless, selected machine learning methods deliver attractive risk profiles for specific targets. The framework naturally extends to meta-analyses across targets, horizons, and samples, illustrated with a density forecast evaluation and the M4 competition. |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2605.09712 |
| By: | Masaaki Fukasawa; Shunta Murayama |
| Abstract: | In financial markets, accurately measuring the risk of future fluctuations in asset prices is of paramount importance. Studies such as Carr and Madan have shown that the expected value of the quadratic variation of log prices can be expressed as an integral of European option prices over a continuum of strikes. This has led to the widespread estimation of model-free volatility (implied variance). However, this theoretical calculation assumes that options are continuously traded across all strike prices, which creates a fundamental gap with real-world market environments where options are only traded at discrete strikes. How to appropriately address this gap and robustly estimate volatility is a crucial issue for both practitioners and academics, and is the primary objective of this paper. Focusing on the fact that volatility indices are primarily calculated from the prices of out-of-the-money (OTM) options, this paper proposes a novel method for constructing a continuous European option pricing function that is consistent with the bid-ask spreads of observed OTM options and strictly satisfies arbitrage-free conditions (such as monotonicity and convexity). Although previous studies have attempted to construct arbitrage-free option pricing functions from bid-ask spreads, the construction method proposed in this paper requires fewer market parameters than existing methods. This makes it possible to robustly calculate volatility indices while maintaining theoretical consistency, even in markets with extremely low liquidity. |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2605.17446 |
| By: | Bj\"orn L\"ofdahl Grelsson |
| Abstract: | Historical Simulation (HS) and its extensions form a popular class of methods for estimating Value-at-Risk for portfolios of financial assets based on historical data. In this note, we seek to unify several ideas and models from throughout the literature into a single modeling framework. By explicitly defining a parametric model form for the asset returns and extracting the realized increments of the driving innovation process from historical data, we are able to reproduce the Historical Simulation, filtered Historical Simulation, and displaced Historical Simulation methods. This shows beyond a doubt that these methods need more underlying assumptions than what is often alluded to. |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2605.10066 |
| By: | Alex Leung; Rex Zhang; Ervin Ling; Kentaroh Toyoda; SiewMei Loh |
| Abstract: | The rapid diffusion of agentic AI has created a new coverage problem for commercial insurance: some AI-mediated losses are now affirmatively insured, some create silent-AI exposure under legacy cyber, technology errors-and-omissions (E&O), directors-and-officers (D&O), employment practices liability (EPLI), crime, and media policies, and others are being actively excluded. This paper maps that emerging boundary by coding 55 AI threat classes against 26 insurance products, endorsements, and exclusion regimes using public carrier materials and OWASP/MITRE threat catalogs. We identify a four-tier insurability frontier: affirmatively insured perils, silent-AI exposures, actively excluded perils, and perils outside conventional private insurance structures. Our coding measures publicly claimed positioning rather than executed contract wording; the headline statistics describe what carriers publicly state about coverage, not what would be paid in any specific claim. Three patterns emerge. First, affirmative AI coverage is beginning to differentiate by primary risk emphasis: public materials often position Munich Re around model performance and drift, Armilla and parts of the Lloyd's market around hallucination and broader AI liability, Tokio Marine Kiln and CFC around IP and technology E&O concerns, Apollo ibott around emerging autonomous system liability, and Coalition around deepfake and AI-enabled cyber response. Second, legacy lines retain silent-AI exposure where AI is an instrumentality rather than the legal cause of loss. Third, foundation model concentration is the clearest genuinely novel insurability frontier because upstream model failure can correlate losses across many cedents at once; the relevant market design question is which insurability constraint each candidate structure relaxes, not merely which systemic risk template exists. |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2605.18784 |
| By: | Jonas F. Frederiksen; Muneya Matsui; Rasmus S. Pedersen |
| Abstract: | We study the asymptotic behaviour of widely used tests for evaluating and comparing predictive accuracy when forecast errors exhibit heavy tails. In particular, when loss differentials have infinite variance, the Diebold-Mariano test statistic converges to a nonstandard limit involving non-Gaussian stable random variables. As a consequence, conventional critical values can yield severely distorted inference: a nominal 5$\%$ test may reject a true null as often as 70$\%$ of the time. To establish these results, we develop a new stable limit theorem for strongly mixing, infinite-variance time series processes. Building on this theory, we consider sub-sampling-based inference that remains valid irrespective of tail-heaviness and requires no estimation of long-run variances or tail indices. An application to risk forecasts for emerging-market exchange rates shows that accounting for heavy tails can substantially alter conclusions about predictive performance relative to standard procedures. |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2605.16866 |
| By: | Suvam Pal; Viktor Stojkoski; Arnab Pal; Trifce Sandev |
| Abstract: | We study a generalized geometric Brownian motion framework that incorporates both entries of new units and exit mechanisms for the current population, extending earlier stochastic resetting models where these rates are treated as identical. The model captures realistic features observed in many economic observables, which can be explained as market-driven firm entries/exits, worker inflow/outflow, and income growth/loss. This model is not conservative and, despite the asymmetry in the entry and exit rates, we find that the system eventually relaxes to a stationary distribution. Moreover, our analysis reveals three distinct dynamical regimes in the moments of the distribution, arising from the interplay between volatility, drift, entry, and exit rates. We further derive the survival probability and the mean first-passage time associated with the observed variable reaching certain threshold under the competing entry-exit processes. Interestingly, we identify an optimal exit rate that minimizes the mean first-passage time, providing insights into how entry and exit policies can influence the outcome of the system. These results should be useful for understanding the long-run behavior of economic systems in which growth, volatility, entry, and exit jointly shape the evolution of heterogeneous units. |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2605.17299 |
| By: | Tristan Jourde; Sofía Ruiz Romanos; Dilyara Salakhova |
| Abstract: | This blog post builds on the NGFS’s short-term climate scenarios to assess the risk of a financial market correction caused by a series of natural disasters. In the event of extreme climate shocks, French banks, insurers and asset managers would be exposed to losses of EUR 196 billion, equivalent to a 4% drop in the value of their equity and bond portfolios. <p> Ce billet s’appuie sur les scénarios climatiques de court terme du NGFS pour évaluer le risque de correction des marchés financiers à une série de désastres naturels. En cas de chocs climatiques extrêmes, banques, assurances et gestionnaires d’actifs français s’exposeraient à des pertes de 196 milliards d’euros, équivalant à une dépréciation de 4% de leurs portefeuilles d’actions et d’obligations. |
| Date: | 2026–04–29 |
| URL: | https://d.repec.org/n?u=RePEc:bfr:econot:449 |
| By: | Mohammad Jalili Torkamani; Pedro Gomes; Amirmohammad Sadeghnejad; Jason Le |
| Abstract: | The film industry is characterized by significant financial uncertainty, where large production investments do not always guarantee commercial success. This study analyzes the relationship between release season, production budget, and movie financial performance using the Full TMDB Movies Dataset 2024. A data mining framework incorporating association rule mining, clustering, machine learning, and SHAP analysis was applied to identify key drivers of revenue and profitability. The results show that release season has limited predictive influence on revenue and return on investment (ROI). In contrast, production budget, popularity, and audience ratings are significantly more influential. Association rule mining revealed that high-budget films with poor ratings are strongly associated with negative ROI outcomes. Random Forest regression achieved substantially stronger predictive performance than Decision Tree regression, with an $R^2$ value of 0.652. SHAP analysis further confirmed that budget and popularity are the dominant predictors of box office revenue, while timing-related variables contribute minimally. These findings suggest that financial success in the film industry is driven more by production investment and market attention than by seasonal release strategies, providing practical insights for budgeting, release planning, and financial risk management. |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2605.12551 |
| By: | Sebastian Bell; Ali Kakhbod; Martin Lettau; Abdolreza Nazemi |
| Abstract: | We propose AlphaGlass, an inherently interpretable machine-learning framework for constructing portfolios that directly optimize investment objectives. AlphaGlass maps stock characteristics into additive signals with sparse interactions and converts these signals into long-short portfolios through a differentiable rank-and-mask layer. This end-to-end design allows the model to optimize objectives such as the Sharpe ratio or mean-variance utility while keeping portfolio weights interpretable and traceable to specific characteristics and interactions. We show theoretically that in-sample objective maximization consistently estimates the population objective and that the differentiable rank-and-mask layer is a faithful smooth proxy for the corresponding conventional long-short quantile portfolio. In U.S. equities, AlphaGlass delivers strong out-of-sample performance and reveals economically interpretable drivers of long and short positions. |
| JEL: | C14 C45 G10 G11 G12 |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35186 |
| By: | Akmal Xodarev |
| Abstract: | We establish four structural results for signature volatility models. First, we prove global existence and uniqueness of strong solutions to the signature SDE $dS_t = S_t \langle \ell, \widehat{W}_t \rangle \, dB_t$ on the weighted tensor algebra $T_w$, identifying the admissibility class through a summability condition H1 and an exponential-integrability condition H3 for the square-integrable stochastic-exponential construction. Second, we establish the asset-pricing part on the natural filtration of the prolonged signature and separate it from transform non-explosion: H3 makes the reference-measure stochastic exponential a true martingale, hence yields NFLVR, while global solvability of the associated infinite-dimensional Riccati equation is the additional condition equivalent to absence of explosion for finite signature transforms. Third, we characterise market completeness on the price filtration via the density of the truncated signature span $\mathrm{span}\{\langle e_I, \widehat{W}_T \rangle : |I| \leq N\}$ inside $L^2(\mathcal{F}^S_T, \mathbb{Q})$, and identify the minimal such $N$, the price-filtration completeness depth. Fourth, we derive the hedging-error decomposition $X = \mathbb{E}_\mathbb{Q}[X] + \int_0^T H_s \, dS_s + \varepsilon_T$ for square-integrable payoffs, with residual expanded through the Gram projection of signature components beyond the completeness depth and bounded by a model-dependent projection error. The four results are tied by an architectural identity: the admissible weighted tensor algebra on which the stochastic exponential is a true martingale and finite signature transforms do not explode is the natural valuation cell of a signature SDE. The proofs are self-contained except for standard results from rough path theory, stochastic integration, and quadratic hedging, recalled in the appendices. |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2605.17142 |
| By: | Yujing Chen |
| Abstract: | We study Bayesian persuasion when the receiver evaluates actions by reward-side Conditional Value-at-Risk (CVaR) rather than expected utility. CVaR preferences break the standard action-based direct-recommendation reduction: merging signals that recommend the same action can change the receiver's tail-risk ranking and destroy incentive compatibility. We show that this failure does not imply intractability in the explicit finite-state model. Each CVaR action value is max-affine in the posterior, and refining recommendations by the active affine piece yields an active-facet revelation principle and an exact polynomial-size linear program. We further identify a representation boundary: listed polyhedral risks remain tractable by the same LP, whereas succinctly represented facet families make exact persuasion NP-hard. Finally, we give a finite-precision approximation scheme for risk preferences determined by finitely many stable posterior statistics. |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2605.12094 |
| By: | Fabien Le Floc'h |
| Abstract: | We present ThiopheneIV, a Black-Scholes implied-volatility solver with a monotone core and explicit production guards. Prices are first reduced to J\"ackel's out-of-the-money normalisation and inverted through a tail-stable logarithmic price equation. The solver starts from the non-iterative Choi-Huh-Su L3 lower-bound seed and applies three Euler-Chebyshev corrections. In exact arithmetic, the seed is below the admissible root and the Euler-Chebyshev map increases monotonically without overshooting; the proof is included. The implementation then adds the floating-point machinery needed in practice: parity normalisation, microscopic Bachelier-limit handling, saturated-price treatment, finite-update checks, fallback seeds, and an optional J\"ackel-Newton polish. Against the highly accurate expanded J\"ackel reference price, ThiopheneIV is faster than a Java port of J\"ackel's Let's Be Rational while keeping regular-grid errors close. ThiopheneIV+ adds one final J\"ackel-Newton correction for systems that need closer agreement with that expanded reference price. The broader lesson is that a convergence proof gives a clean core, but robust production inversion still depends on boundary handling and on the pricing objective one chooses to match. |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2605.22427 |
| By: | Cara Bordier; Lukas Frei; Simon Stalder |
| Abstract: | The US dollar (USD) is involved in 88% of global foreign exchange transactions, partly due to its role as a vehicle currency. Using high-frequency data from primary interdealer platforms, we develop a novel methodology to identify USD cross-trades. We show both theoretically and empirically that such trades can generate price fluctuations in USD exchange rates. Employing an instrumental variables approach, we find that increased cross-trading activity amplifies aggregate USD volatility. These results highlight a fundamental trade-off: while dollar dominance enhances market liquidity, it also increases the currency’s exposure to shocks originating in other currency pairs. |
| Keywords: | Dollar dominance, Volatility, Foreign exchange markets, High-frequency trading |
| JEL: | F31 G12 G14 G15 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:snb:snbwpa:2026-05 |
| By: | Hasret Ozan Sevim |
| Abstract: | On-chain lending has expanded across multiple distributed ledgers as DeFi becomes increasingly multi-chain. This environment introduces novel technical and financial mechanisms, particularly cross-blockchain communication and asset transfer protocols, yet cross-chain elements remain understudied in lending protocol risk management. To address this gap, we applied panel regression fixed effects and OLS models to empirically analyze cross-blockchain interoperability solutions, using TVL and total revenue as performance proxies from October 2022 to January 2025. Our data set covers 15 decentralized lending protocols and 53 cross-chain bridges across 9 EVM-compatible blockchains, categorized as Ethereum, alternative layer-1s, and Ethereum layer-2 networks. Results reveal that cross-chain activity impacts on protocol performance. Bridge volume emerges as a critical driver, exerts a significant effect on TVL and revenue across different categories, though the direction of this effect varies heterogeneously. Increased bridge integrations are associated with decreased TVL and protocol revenue across categories, indicating liquidity escapes from those lending ecosystems. Liquidations produce heterogeneous effects across categories. New network launches do not have as significant relationships with TVL and revenue while bridge hacks show a significant and positive relationship. High R-squared values confirm meaningful explanatory power. We further show Ethereum attracts large depositors, while layer-2s skew toward retail participation. We conclude that effective DeFi risk models should incorporate cross-chain metrics and adopt a layer-aware approach to accurately reflect the evolving multi-chain landscape. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2605.12508 |