This paper introduces a physics-informed generative framework that resolves the conflict between statistical flexibility and rigorous fixed-income modeling. A two-stage architecture—Stage A CVAEsT + LS (Student-t Conditional VAE with Dynamic Level Injection) and Stage B a continuous-time Neural SDE penalized by a No-Arbitrage PDE—yields an arbitrage-free, heavy-tailed yield-curve manifold. Empirical results across USD, GBP, and JPY show markedly reduced out-of-sample errors (6.58 bps Mean Tenor RMSE), overcoming issues seen in classical HJM and unconstrained baselines, while enabling regime detection and robust scenario generation [page::1]
<paper_abstract>This work recasts look-ahead bias as temporal non-interference on a time-indexed information lattice, defines look-ahead-freedom, and develops a time-indexed pipeline calculus with a value-independent fragment for which a sound, linear-time type-and-effect system certifies look-ahead-freedom. It proves undecidability for the full language, validates a linear-time checker on pipelines within the fragment, and demonstrates empirical advantages over detectors in catching leaks while quantifying the cost of false positives. The results yield a certificate of absence of look-ahead leakage for admissible pipelines and delineate the boundary beyond which certification is impossible in general [page::1][page::5][page::18].
The paper introduces a Crypto-Microeconomic Observability Framework to analyze Bitcoin wealth disparities across five labeled agent classes: Service, Abuse, Malware, Individuals, and Benign. Using descriptive statistics, inequality metrics (Gini, Top-K shares), and time-series analysis, it finds persistent wealth concentration across classes (the Whale-Effect), with Service holding 75.15% of BTC and Abuse 24.26%, while within-class inequality is near maximal (e.g., Individuals Gini ≈ 0.9993). The framework highlights structural unevenness and persistent concentration across major market events, persisting over time [page::1].
We derive an operational-time variance kernel for a latent-order-book reaction boundary and show how calendar-time local volatility can be obtained via clock projection and pricing-measure choice. The boundary displacement arises from a damped Abel response to signed order-flow, with the variance realized as a finite-scale cumulant rather than a primitive diffusion coefficient. A deterministic activity clock yields a benchmark projection, while non-unique clocks move incompleteness to the projection layer; adjoint consistency provides a reality constraint ensuring coherent pricing across calendar time [page::1].
Power Purchase Agreements (PPAs) are bilateral over-the-counter contracts central to renewable energy financing. While their capacity to stabilise revenues and hedge price risk is well recognised, their OTC structure exposes both parties to counterparty credit risk. This is a dimension yet to be explored in the literature, particularly given the dual price and volumetric uncertainty inherent in renewable sources. This paper develops a framework for the pricing and valuation of wind power PPAs and for quantifying this risk through Credit Valuation Adjustment (CVA) and Debit Valuation Adjustment (DVA). We model the joint dynamics of electricity spot prices and renewable output, incorporate default probabilities, and compute valuation adjustments that reflect the fair value of bilateral credit risk. The framework provides market participants with a transparent metric for PPA valuation under counterparty risk. While initiatives such as the European Investment Bank’s pilot guarantee scheme aim to mitigate credit risk for certain offtakers, such interventions do not cover all PPA transactions. Rigorous internal credit risk assessment therefore remains indispensable for lenders, producers, and offtakers alike. [page::1]
本研究在受控实验中发现,当将明确的非诊断线索嵌入叙事框架时,参与者的 belief 更新会系统性地回撤至最大不确定性0.5,削弱先前累积的诊断信息影响;在等效的抽象情境下及 Story No Info 条件中,该效应显著减弱甚至消失。通过蒙特卡洛模拟,研究进一步显示此信念回撤会延缓信息收敛、增加决策前需处理的信息量,具有经济和组织层面的重要含义 [page::0]
Strategic Information Disclosure in Algorithmic Pricing examines how third-party information disclosure shapes Q-learning-powered pricing under stochastic demand. We analyze three rules: no disclosure, full disclosure, and upper censorship. Upper censorship, which truthfully reveals low-demand states while pooling high-demand ones, delivers higher profits than full disclosure, consistent with theoretical predictions. However, we uncover a robust profit reversal: when the discount factor is high, no disclosure yields higher profits than full disclosure, whereas when the discount factor is low, full disclosure performs better. This pattern is exactly the opposite of classical collusion theory. These findings indicate that Q-learning agents respond systematically to information structure and that restricting information sharing may backfire when algorithms are patient, underscoring the need to reassess regulatory approaches in AI-mediated markets [page::1][page::6][page::14][page::14][page::17].
本研究在单期限价市场中引入知情交易参与者数量的随机性,探讨流动性提供方对知情交易强度的不确定性下的均衡定价与成交成本。核心在于通过一个边际成本函数 F 的不动点积分方程刻画均衡,并由定价算子ϕF 重建限价簿。结果显示:当资产价值分布具有端点尾部时,价格冲击呈幂律型,其指数由资产尾部性质与知情交易参与者数量分布共同决定,且不仅取决于知情者的期望数量;在端点尾部较轻的情形,价格冲击为对数型。数值实验验证理论的渐近结果,并揭示知情交易参与者数量的分散程度如何影响簿平整度与比价。迄今结论还包括:对存在不确定性的内幕交易者而言,内在利润与买卖价差的关系以及分散度对执行成本的影响等。[page::14][page::16][page::24][page::32]
We propose a multivariate, eigenstructure-indexed generalisation of the Lo–MacKinlay variance-ratio, decomposing long-horizon equity-return dynamics into parallel return-channel and volatility-channel memory components. The framework yields a parsimonious five-factor multi-memory model—comprising persistent and antipersistent fractional Brownian factors, an ARFIMA component, a Markov-switching multifractal (MSM) volatility cascade, and a transitory volatility-of-volatility factor—that fits four U.S. panel and a Europe cross-section while recovering seven stylised facts of long-horizon dynamics across all panels. A rolling-window analysis locates a late-1980s regime transition in market-mode volatility, while a cross-channel β-inversion test falsifies a single shared cross-asset loading driving both linear memory and volatility cascade. The results imply that cross-sectional eigenstructure and horizon-dependent variance decomposition encode universal multi-memory structure, with distinct channels carrying return- and volatility-dynamics and with regime shifts that matter for risk and pricing. [page::1]
本研究围绕 Türkiye 的农业、工业、建筑和服务四大行业在可持续发展指数中的长期关系,采用2000-2022年年度数据,先进行单位根检验(ADF、PP、Zivot–Andrews),再进行Johansen共整合检验,最后用DOLS估计长期系数,FMOLS作为稳健性检验。结果显示四个行业份额在长期均与可持续发展指数正相关,其中服务业的系数最大;DOLS结果为服务0.882、农业0.302、工业0.265、建筑0.193;FMOLS稳健性检验支持DOLS结果。研究表明Türkiye的可持续发展策略应实现产业间的平衡发展,结合绿色转型来提升整体 SDG 绩效。[page::1]