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Les publications des régulateurs européens et internationaux et les nouveaux travaux de recherche, collectés automatiquement. ⭐ signale ce qui touche directement la banque, l'assurance et la finance.
arXiv — gestion des risques
Risk measures for infinite-server queues driven by a Cox-BESQ process
arXiv:2610.36473v1 Announce Type: cross Abstract: This paper introduces a model for infinite-server queues driven by a Cox process with stochastic intensity given by a squared Bessel (BESQ) process. Exploiting the affine structure of the underlying dynamics, we derive a closed-form expression for the joint moment-generating function (MGF) of the arrival intensity, the number of customers in the system, and the integral of the number of customers in the system over a time interval. This MGF can also be obtained at two (or more) dates, thereby allowing an analysis of the time evolution of infini
arXiv — finance statistique
Evaluating the Predictive Validity of ICT's Accumulation-Manipulation-Distribution (AMD) Model on CME E-Mini NASDAQ-100 Futures
arXiv:2610.35887v1 Announce Type: cross Abstract: To our knowledge, this paper provides the first systematic quantitative evaluation of the ICT Accumulation-Manipulation-Distribution (AMD) model on CME E-Mini NASDAQ-100 futures (NQ). The AMD model proposes that institutional participants structure intraday price delivery in three phases: overnight accumulation in the Asian session, a deliberate false breakout during London, and directional delivery to the opposing liquidity pool during the New York morning session. Using four years of real 15-minute OHLCV data from Databento (January 2022 to J
arXiv — finance statistique
FinLLM-KG: Knowledge Graph-Enhanced LLMs for Financial Sentiment Analysis in Algorithmic Trading
arXiv:2610.36083v1 Announce Type: cross Abstract: Recent advances in Generative AI have substantially improved financial sentiment analysis through post-trained financial large language models (LLMs). However, existing approaches remain confined to predicting the sentiment of each article in isolation and operate as black boxes, which limits contextual understanding and hinders deployment in high-stakes financial decision-making. To this end, we introduce FinLLM-KG, a model-agnostic framework that combines causal LLMs with a financial knowledge graph, in order to inject temporally grounded, co
arXiv — finance statistique
Contemporaneous Transmission Networks and Systemic Risk in European Banking
arXiv:2610.37487v1 Announce Type: new Abstract: We study how contemporaneous equity-return transmission shapes systemic exposure among 51 listed European banks. Using a recursive non-Gaussian network model on volatility-standardized returns, we construct two market-capitalization-weighted measures based on direct and indirect transmission effects: the Systemic Impact Score (SIS), capturing a bank's potential influence on other institutions, and the Systemic Vulnerability Score (SVS), capturing its exposure to disturbances originating elsewhere. The fitted network reveals distinct transmitting
arXiv — finance statistique
Event definition and the branching ratio: Hawkes estimates of price-change clustering in the consolidated quote feed
arXiv:2610.36317v1 Announce Type: new Abstract: Hawkes models of price changes in the consolidated US quote feed find a sub-millisecond kernel component and a branching ratio near criticality. We show that in large-tick stocks the first is the feed's construction: half of NBBO changes are locks or depleted quotes, resolved within a millisecond, which the model reads as excitation. Defining the event as a mid-price level persisting for one millisecond removes the component. Raw fits put three times the clean kernel mass within one second, which a simulation reproduces. Since the total branching
arXiv — gestion des risques
An Application of Geometric Riemannian Pricing Algorithm for Valuing Catastrophe Bonds in Incomplete Markets: Market-Consistent Financial Entropy and the Split of Frequency and Severity Risk Premia
arXiv:2610.37171v1 Announce Type: cross Abstract: This paper presents a framework for pricing catastrophe (CAT) bonds in incomplete markets through Riemannian geometric optimization of financial entropy. We formulate pricing-measure selection as a maximum financial entropy problem on a finite-dimensional manifold of equivalent measures, parameterized by the non-hedgeable market prices of catastrophe frequency and severity risk, with discounting fixed by the default-free term structure. Since unconstrained entropy maximization recovers the physical measure, we incorporate observed secondary-mar
arXiv — finance statistique
Weekend Crypto Volatility and Monday Market Risk
arXiv:2610.35702v1 Announce Type: new Abstract: Cryptocurrencies trade continuously while major traditional markets close over the weekend. We examine whether weekend cryptocurrency volatility predicts Monday variance in equity, commodity and fixed-income markets. Using daily range-based variance measures for Bitcoin, Ether, XRP, the S&P 500, gold and a US Treasury ETF over 2017-2026, we augment heterogeneous autoregressive models with a common weekend cryptocurrency factor. Predictive content is weak before 2024 but significant thereafter. In the post-2024 sample, a one-standard-deviation inc
arXiv — finance statistique
Multiscaling in the Rough Bergomi Model: A Tale of Tails
arXiv:2601.11305v2 Announce Type: replace Abstract: The rough Bergomi (rBergomi) model, characterised by its roughness parameter $H$, has been shown to exhibit multiscaling behaviour as $H$ approaches zero. Multiscaling has profound implications for financial modelling: it affects extreme risk estimation, influences optimal portfolio allocation across different time horizons, and challenges traditional option pricing approaches that assume uniscaling behaviours. Understanding whether multiscaling arises primarily from the roughness of volatility paths or from the resulting fat-tailed returns h
arXiv — gestion des risques
Optimal collective investment under regulatory uncertainty
arXiv:2610.32959v1 Announce Type: cross Abstract: We study optimal collective investment subject to a Value-at-Risk (VaR)-type constraint that accounts for uncertainty about future regulatory requirements. The constraint incorporates several possible wealth thresholds and imposes a minimum weighted average probability of meeting them. We characterize the optimal terminal wealth, which combines regions of constant wealth at regulatory thresholds with Merton-type investment regions. Unlike a classical VaR constraint with only one threshold, our formulation generates protection against several th
arXiv — gestion des risques
Risk Adequacy and Anti-Discrimination in Insurance Pricing: Classification, Aggregation, and Transfers
arXiv:2610.32614v1 Announce Type: new Abstract: Insurance tariffs must reconcile risk-based pricing, portfolio coverage, and anti-discrimination requirements. We introduce a framework in which tariffs are constructed through classification, aggregation of benchmark risk prices within classes, and balanced transfers, and are evaluated by several distinct criteria. This yields a multi-objective tariff-design problem whose attainable objective set and Pareto frontier describe the resulting trade-offs. Risk adequacy is measured by the discrepancy between charged premiums and individual benchmark r
arXiv — gestion des risques
A General Framework for Importance Sampling with Markov Random Walks
arXiv:2311.12330v3 Announce Type: replace-cross Abstract: Although stochastic models driven by latent Markov processes are widely used, the classical importance sampling methods based on the exponential tilting for these models suffer from difficulties in computing associated eigenvalues and eigenfunctions and the plausibility of the indirect asymptotic large deviation regime for the variance of the estimator. We propose an importance sampling framework that twists the observable and latent processes separately using a link function that directly minimizes the estimator's variance. An optimal
arXiv — gestion des risques
Multichoice Modeling: Exploring Decision Configuration Space under Uncertainty
arXiv:2610.31038v1 Announce Type: cross Abstract: Decision support has become sophisticated at exploring uncertainty, but much less so at representing the architecture of the decision itself. As a result, the decision space is often narrowed before uncertainty is analyzed, leaving interactions among choices and the effects of alternatives they imply largely invisible. This paper introduces multichoice modeling, a framework for making large decision configuration spaces explicit and computationally explorable under uncertainty. Drawing on the logic of rugged landscapes, we conceptualize such pr
arXiv — finance statistique
What Prediction-Market Contracts Reveal at Launch: Trading Volume and Dispute Risk
arXiv:2610.29578v1 Announce Type: new Abstract: Using a near-census of 802,863 resolved markets listed on Polymarket between January 2023 and May 2026, and only information available at listing (the market's title and description, named resolution source, and contract metadata), we predict lifetime trading volume and whether settlement is later disputed. Contract information predicts trading volume well (out-of-sample $R^2 = 0.747$ for log volume), and most of the signal is interpretable: market topic provides the largest gain ($+0.289$ $R^2$ over a $0.220$ contract-feature baseline), wording
arXiv — gestion des risques
Distributions or Quantiles? A Convex Variational Theory of Worst-Case Risk
arXiv:2610.29652v1 Announce Type: new Abstract: Worst-case risk evaluation is central to finance and operations research, yet existing analytical results remain fragmented and largely confined to ambiguity sets with specific structures. Using spectral risk measures as the underlying class, we develop a convex variational theory that unifies these analyses and extends them to richer ambiguity models. The theory rests on a representation principle: use the quantile or distribution formulation that preserves convexity of the full ambiguity set. In both representations, the resulting optimality co
arXiv — gestion des risques
Dynamic portfolio selection under range value-at-risk with logarithmic returns
arXiv:2610.26919v1 Announce Type: new Abstract: This paper studies continuous-time portfolio selection under Range Value-at-Risk (RVaR), with both portfolio performance and risk evaluated in terms of logarithmic returns. We formulate a mean--RVaR problem in a continuous-time financial market and reduce the dynamic optimization to a static quantile problem. Using quantile representation, Lagrangian optimization, and convexification, we characterize the optimal terminal wealth through the convex envelope of an RVaR-specific transformed weighting function. The RVaR-specific convex envelope exhibi
arXiv — finance statistique
Arithmetic Models for Commodity Futures, Vanilla, and Calendar Spread Options
arXiv:2610.28071v1 Announce Type: cross Abstract: We develop an arithmetic multi-factor stochastic volatility model for commodity futures and options. The arithmetic specification yields a low-dimensional Markovian representation of the futures curve and semi-closed-form pricing formulas for vanilla and basket options, including calendar spread options. These properties make the model suitable for large-scale empirical estimation. We estimate the model using an unscented Kalman filter on a panel of WTI crude oil futures, vanilla options, and calendar spread options spanning 2013-2024. We find
arXiv — finance statistique
Conflicting History in Foundation Model Return Forecasts
arXiv:2610.25822v1 Announce Type: new Abstract: Older observations can help a forecaster learn a predictive relationship while becoming misleading when that relationship changes. We study this tension using paired return histories that differ in older observations but share recent returns and the conditional distribution of the next return. Across five foundation-model forecasting procedures and three simulated processes, each evaluated on 1,024 independent replications, conflicting history reduces the probability of improving on zero in all 30 higher-signal comparisons. This loss of usefulnes
arXiv — finance statistique
More Mentions, Less Information? Diagnosing Identity and Alignment in Reddit Embeddings for Volatility Forecasting
arXiv:2610.25468v1 Announce Type: new Abstract: Since the 2021 GameStop short squeeze, r/WallStreetBets (WSB) has been associated with episodes of extreme volatility, and its text is increasingly used to forecast volatility. A model using only large language model (LLM) embeddings of WSB posts attains an out-of-sample $R^2$ of 0.13 for log realized variance. That number shows predictive power, but it does not reveal where the information comes from or how much the text adds beyond a firm's own volatility history. We answer these questions with a diagnostic protocol that nests embeddings on a h
arXiv — gestion des risques
Requested, Executed and Recoverable Credit: Shared Action Ordering among LLM Borrowers in a Simulated Lending Market
arXiv:2610.15973v2 Announce Type: replace Abstract: We test whether shared action ordering changes executed credit in a simulated collateralized lending market. Three small open-weight language models face a borrowing cap that values collateral at the pool price; only two of the three model families traded. A validation campaign, hash-locked locally before its first model call, pairs 12 common-order markets with 12 balanced-order markets under a revised interface. An exact randomization test rejects the sharp null that common-market net debt is invariant to complete-order assignment (two-sided
arXiv — finance statistique
Inverse Learning of Latent Risk-Neutral Densities from Irregular Option Quotes
arXiv:2607.27188v3 Announce Type: replace-cross Abstract: Accurate option prices do not imply accurate recovery of the latent risk-neutral density. We study this distinction with two complementary benchmarks. A controlled benchmark exposes simulator-truth densities for latent evaluation, while a chronological NIFTY benchmark tests only held-out market prices. A two-component lognormal mixture has the lowest aggregate price, $L^1$, Wasserstein, and fixed-tail errors on the synthetic benchmark. Learned operators retain narrower strengths: DeepONet reduces 1% quantile and variance error by 39.0%
arXiv — finance statistique
Do Round Numbers Hold Prices? Occupation Densities and Depth-Conditioned Hazards in the Limit Order Book
arXiv:2610.34787v1 Announce Type: cross Abstract: Prices cluster at round numbers, a pattern often interpreted as evidence that these levels act as price barriers. This pattern also admits a mechanical explanation: more liquidity rests there, and prices take longer to leave deeper queues. Limit order book data allow us to distinguish these mechanisms through a tick-grid framework in which local time counts departures and level-wise volatility reflects the departure hazard. Using data for five Nasdaq stocks throughout 2025, we find that round levels attract more liquidity but do not exhibit lon
arXiv — finance statistique
Fast Approximate Modular Inference for Option Price Prediction
arXiv:2412.00658v3 Announce Type: replace Abstract: A new approximate Bayesian inferential framework is proposed that exploits multiple information sources -- daily spot returns, high-frequency spot data and option prices -- and enables fast calculation of probabilistic predictions of future option prices. This approach operates directly from the theoretical option pricing model, and does not require an explicit statistical model, or likelihood, for the observed option prices. We demonstrate that our approach achieves predictive accuracy comparable to likelihood-based modular inference, despit
arXiv — gestion des risques
General bounds on functionals of the lifetime under life table constraints in a joint actuarial-financial framework
arXiv:2603.06238v3 Announce Type: replace Abstract: In life insurance, life tables provide survival probabilities at integer ages but leave the distribution of deaths within each year unspecified. This incompleteness is particularly relevant for modern insurance products such as variable annuities, whose payoffs depend jointly on lifetime uncertainty and financial market performance. The valuation of such contracts must therefore be carried out in a joint actuarial-financial framework, as their values depend not only on the full information about mortality rates but also on the interaction bet
arXiv — gestion des risques
Deep catastrophic-risk-aware reinforcement learning
arXiv:2610.27214v1 Announce Type: cross Abstract: Estimating extreme risk in reinforcement learning becomes increasingly difficult as the confidence level increases, since only a small number of observations contribute to empirical estimates of Conditional Value-at-Risk (CVaR). In this paper, we extend an Extreme Value Theory (EVT) approach for CVaR optimization to high-dimensional neural-network policies. Using a peaks-over-threshold representation and method-of-moments estimation, we obtain a differentiable tail-risk objective and show that, conditional on the selected threshold, its finite-
arXiv — gestion des risques
Conformal Kelly: When Marginal Coverage Transfers and the Economics Do Not
arXiv:2608.01494v3 Announce Type: replace-cross Abstract: We size portfolio positions from conformal prediction intervals, reading the interval half-width as the scale in a capped fractional-Kelly rule (Conformal Kelly), and evaluate the rule under a pre-registered protocol. The development record is an autonomous LLM-agent search over roughly 200 configurations on a single six-year window (2016-2021) of eight US-listed ETFs. All later data was sealed before the search, and two final configurations, nine comparison books and the mapping from outcomes to conclusions were registered before unsea
