We propose a framework for designing Target-Date Funds (TDFs) around an explicit return objective while controlling risk directly at the portfolio level through a declining Conditional Value-at-Risk (CVaR) constraint. In this approach, the regulator or sponsor specifies a CVaR glidepath that gives the portfolio manager enough flexibility to reach a target return with a reasonably high probability. The target return is determined exogenously from pension-design inputs such as retirement age, contribution rate, working years, life expectancy, and replacement-rate goals. This differs from conventional TDF design, where age-dependent asset-class limits are set without an explicit link to a required return. A key feature of the method is that it does not assume the manager selects an optimal portfolio each period. Instead, each month the manager draws an allocation from the set of portfolios satisfying the CVaR constraint. This yields a conservative evaluation of each glidepath: success probabilities are averages over admissible allocations, rather than best-case outcomes. We introduce two figures of merit: the probability of meeting the target return and the cumulative risk assumed over the life of the TDF. As a proof of concept, we apply the framework to Chile's 2025 pension reform using nine Chilean and global asset classes and a 40-year accumulation horizon. The results show that the transition age at which risk starts to decline is the most consequential design parameter, and that contribution density acts as a hard constraint: below a critical threshold, portfolio design alone cannot compensate for structurally low contributions. The framework is general and can be applied to any TDF designed around an explicit return objective.

An important question for an algo trader working an order is to understand if their actions are moving the market against them -- i.e., causing market impact. The conventional answer usually is one of two: (i) monitor price slippage in real-time, potentially reducing adverse activity with increased slippage, or (ii) do away with dynamic trading adjustments and rely on semi-static rules based on ex-post estimates of slippage over a large sample of events. Realtime monitoring fails because reliably estimating slippage is statistically expensive -- it requires hundreds of fills before it can be told apart from background volatility. More fundamentally however, it does not establish causality. Observed adverse price moves may be caused by the trader's own actions, or by an unrelated participant competing for the same liquidity and capturing the same alpha. The optimal response (say, slow down vs.\ speed up) is opposite in the two cases. We propose a method that detects price impact, on a per-action basis, by measuring the timing synchronicity between a trader's actions and subsequent adverse market events. The method at heart is a test for statistical \emph{surprise} in the timing of adverse events post trader action. We must be clear in that we do make a leap of faith here and assume that surprisingly fast adverse market events are evidence of causation and that the action triggered them -- a direct signature of impact and information leakage. Validating it requires real execution data; we set out the empirical tests that would do so.

Short-dated index options make scheduled macro-announcement risk visible in market prices, but identification is nontrivial: a flexible no-event surface fitted to event-spanning quotes can absorb event premia, while a jump calibrated without event-spanning quotes is unidentified. We therefore model Federal Open Market Committee (FOMC) decisions, Consumer Price Index (CPI) releases, and nonfarm payroll (NFP) reports as deterministic-time jumps in risk-neutral option pricing and propose a non-spanning identification protocol. Non-spanning expiries identify the no-event volatility surface, event-spanning training quotes calibrate the scheduled jump, and held-out event-spanning quotes are used only for pricing evaluation. On PM-settled S\&P 500 index (SPX) options from May 2022 to August 2025, Gaussian and two-component mixture jumps improve held-out event-spanning pricing, with the clearest gains in robust median pricing errors and in event-volatility option combinations (straddles and strangles) rather than directional risk reversals. A contaminated-surface stress test confirms the identification concern: allowing event-spanning training quotes into the no-event surface fit produces strong held-out performance by absorbing event premia rather than identifying scheduled jump risk. An amortized mixture density network (MDN) benchmark shows limited cross-event transfer: pure leave-one-event-out amortization reduces implied-volatility errors but not mean dollar or mean spread-normalized pricing errors, while the scale-calibrated variant restores Gaussian-level performance yet remains below event-specific mixture calibration. Scheduled-jump identification is strongest for CPI and FOMC and weaker for NFP.

Given risk-neutral densities of a tradeable forward, fitted as $N$-component mixtures at a finite set of expiration pillars, we look for a continuous-time interpolation that (i) stays inside the mixture family (it remains a mixture of the same kernel, though generically with more components than either pillar), and (ii) is the marginal flow of a Markov martingale, equivalently carries a non-negative Dupire local volatility. The second requirement is the peacock (convex-order) property. For full-support kernels (Gaussian, lognormal) a peacock corresponds to a unique continuous local-volatility diffusion (Lowther). We give a constructive interpolation that stays in a fixed $2N$-component family, note as an open question whether $N$ components suffice, and describe the main practical difficulty: in strongly bimodal regimes the local volatility stays finite but becomes badly conditioned.

Practitioners allocate capital with forecast-light rules such as equal weight, inverse volatility, risk parity, HRP, and return-adjusted HRP (RA-HRP). This paper develops \emph{Heuristic Portfolio Optimization} (HPO): an information-restricted projection of the Markowitz/tangency solution onto a stable rule class. The implied-return principle, $\w$ is maximum-Sharpe iff $\bmu_e\propto\bSigma\w$, gives closed-form optimality sets for leading heuristics and exposes the Schur-complement substitutions behind HRP. For RA-HRP, we introduce fixed-tree cluster-Sharpe recursion, unit-free HRP--RA-HRP interpolation, tangency conditions, conditional-risk splits, and pathwise/KL decompositions of weight distortion. First-order Sharpe calculus expresses the marginal value of return information as nodewise alphas against HRP and yields a linear KL trust budget. We formalize generic HPO maps, define the implied-return defect, prove that it equals squared Sharpe inefficiency, characterize tree-HPO coincidence by nodewise mass ratios, and give a bias--variance decomposition for estimated rules. Finally, HPO is embedded into Reinforcement Learning Portfolio Optimization (RLPO): every HPO map induces a deterministic stationary policy; static HPO is the $\gamma=0$ no-friction face of the Bellman problem; RA-HRP supplies a hierarchical policy prior; and dynamic improvement is warranted when continuation value exceeds myopic HPO defect plus frictions. A performance-difference identity prices the myopic value gap, gives an $\varepsilon/(1-\gamma)$ myopia bound, and identifies nodewise alphas as policy-gradient coordinates of the hierarchical actor. Thus HPO is the static optimality layer and RLPO the dynamic control layer. The conditions are GRS-testable, extend to mean--CVaR and expected utility under ellipticity, and become Kelly-growth conditions in diffusion limits.

Global Business Services (GBS) have emerged as a "living laboratory" for the Twin Transition of Green and Digital Transformation, as multinational corporations (MNCs) face increasing pressure to harmonize digital efficiency with environmental stewardship. Aiming to derive a socio-technical framework, this paper synthesizes Technology Roadmapping (TRM) with the International Telecommunication Union (ITU) ICT-centric innovation ecosystem toolkit. A bibliometric analysis of research clusters reveals an evolutionary shift from basic process automation toward "Sustainable Intelligence," identifying the GBS unit as a central "operational airlock" that mediates between landscape pressures -- such as the EU's dual mandate and Carbon Border Adjustment Mechanisms -- and niche innovations in AI-native workflows. The study further maps these clusters onto a stakeholder engagement canvas, highlighting how resilient "Middle Power" hubs in Poland, Portugal, and Malaysia are bypassing the middle-income trap to provide a "third way" for global value chains amidst a bifurcated geopolitical cloud. The results offer a data-driven design approach for leaders and entrepreneurial support networks to orchestrate talent and supply chain flows, thereby enriching the conceptual understanding of Industry 5.0 and the role of GBS as a primary mechanism for navigating a volatile, multipolar digital economy.

We study a forward-time formulation of the Black-Scholes equation with state-dependent volatility. In contrast to the classical terminal-value pricing problem, where the option payoff is prescribed at maturity and the price is computed backward in time, the present problem prescribes the current option-price profile and seeks to recover the option-price profile at the expiration date T. This formulation is ill-posed, since the equation evolves in the unstable direction of the parabolic operator and high-frequency perturbations in the initial data may be strongly amplified. To address this difficulty, we introduce a price-dimensional reduction based on shifted Legendre polynomials. The original Black-Scholes equation is projected onto a finite-dimensional Legendre basis in the asset-price variable, leading to a system of ordinary differential equations in time for the expansion coefficients. This reduction acts as a spectral cutoff and also relaxes the degeneracy caused by the factor S^2 at the zero-price boundary. The main reconstruction method is a dimension-reduced Legendre--Tikhonov method. We prove existence, uniqueness, data stability, and convergence for each fixed truncation level. We also include a reduced PINN solver as a secondary computational comparison after the Legendre reduction. Numerical experiments with smooth, butterfly-spread, and European put payoffs show that the Legendre--Tikhonov method recovers the terminal option-price profile from noisy initial data, while the reduced PINN solver provides a useful additional benchmark. Comparisons with the conventional physical-space quasi-reversibility method demonstrate the stabilizing effect of the Legendre reduction.

We show that persistent dynamics of a latent default-probability path can generate effective default correlation through temporal coarse-graining. In the OU--Binomial baseline, monthly defaults are conditionally independent given this latent path, but aggregating monthly default probabilities into long-horizon probabilities induces a scale-dependent effective mixing distribution for aggregated default counts. Applied to corporate default-count data, this mechanism explains long-horizon overdispersion, autocorrelation, and the emergence of effective default correlation. We then examine Davis--Lo-type contagion and Vasicek-type common-factor extensions. Direct fitting at each aggregation scale assigns increasing residual covariance shares to instantaneous dependence, but worsens the per-block expected log predictive density. In contrast, when monthly posterior latent paths are first coarse-grained and residual-dependence parameters are estimated conditional on these paths, the residual covariance contributions remain small while the predictive density improves. Thus, temporal coarse-graining provides a scale-consistent baseline that regularizes the attribution of variance and improves identifiability by suppressing the over-allocation of long-horizon fluctuations to contagion or asset-correlation parameters.

Maritime chokepoints concentrate shipping traffic. Disruptions to this traffic can have a widespread impact on the global economy. However, the way in which these impacts are shaped by the shipping sector's adaptive behavior is not well understood. Here, we introduce an empirically calibrated full-scale agent-based model of the global commercial shipping fleet, representing 35,954 active ships moving among 1,651 ports. We use the model to quantify how rerouting changes arrival losses under chokepoint closures. Static route exposure alone does not predict realized losses. In the adaptive model, rerouting reduces losses at some directly exposed ports, while delayed vessel cycles create losses at later port calls and in dependent regions. Cumulative net shipping-day losses therefore continue to rise with closure duration because longer routes keep ships delayed after the initial adjustment. Each additional closure day reduces global shipping arrivals by 3.0% for Suez and 7.7% for simultaneous Suez, Panama, and Malacca closures. These losses are unevenly distributed in exposed regions and ports. Disruptions with known duration show different loss profiles from unexpected shocks with unknown duration, revealing that end-date information can reduce avoidable short-run losses. The results show that chokepoint risk is a dynamic problem of routing, timing, and regional exposure and not a static property of maritime-network topology.

Ship finance is a data-intensive and document-heavy segment of asset-based lending, requiring the integration of financial, technical, contractual, and regulatory information from heterogeneous and largely unstructured sources. Increasing environmental regulation and ESG reporting requirements are adding further complexity to underwriting and loan-origination processes. Recent advances in artificial intelligence (AI), particularly large language models (LLMs), create new opportunities for processing and analysing such information. This paper reviews potential applications of AI in ship finance, with a particular focus on LLM-based systems for document comprehension, information extraction, and workflow automation. We present this http URL, a modular agentic architecture to support loan application workflows in ship finance. The proposed system combines an LLM-based extraction module, financial analysis components, external maritime data services, and a controlled document-generation module with a chatbot interface to support the preparation of standardized financing applications. The paper discusses the key challenges for using such models in production. We argue that AI-assisted systems can support maritime finance professionals in managing increasingly complex information and reporting requirements.

In this paper we study optimal exit strategies of gamblers with cumulative prospect theory (CPT) preferences in games where the expected payoff is strictly negative at each play, and formulate the problem as optimal stopping on asymmetric random walks. Applying a geometric transformation of the underlying cumulative gain/loss process, engaging randomized strategies and changing the decision variable from stopping times to probability distribution of the accumulated gain or loss at exit time, we solve the problem via the Skorokhod embedding. Drastically different from the fair gamble problem studied by He et al. (2019a), we show that the unfair problem in the infinite time horizon has finite values for a wide range of CPT parameter specifications. We then present the analytical solutions in the case of piece-wise power utility and power probability distortion functions. Compared to the strategies used in fair gambling, the CPT gamblers in unfair gambles are less loss-tolerant and choose not to gamble at all when the games are sufficiently unfavorable.