The COVID-19 pandemic exposed critical vulnerabilities in vaccine supply chains, highlighting the need for robust manufacturing for rapid pandemic response to support CEPI's 100 Days Mission. We develop a discrete-event simulation model to analyze supply chain disruptions and enables policymakers and vaccine manufacturers to quantify disruptions and assess mitigation strategies. Unlike prior studies examining components in isolation, our approach integrates production processes, quality assurance and control (QA/QC) activities, and raw material procurement to capture system-wide dynamics. A detailed mRNA case study analyzes disruption scenarios for a facility targeting 50 million doses: facility shutdowns, workforce reductions, raw material shortages, infrastructure failures, extended procurement lead times, and increased QA/QC capacity. Three main insights emerge. First, QA/QC personnel are the primary bottleneck, with utilization reaching 84.5% under normal conditions while machine utilization remains below 33%. Doubling QA/QC capacity increases annual output by 79.1%, offering greater returns than equipment investments. Second, raw material disruptions are highly detrimental, with extended lead times reducing three-year output by 19.6% and causing stockouts during 51.8% of production time. Third, the model shows differential resilience: acute disruptions (workforce shortages, shutdowns, power outages) allow recovery within 6 to 9 weeks, whereas chronic disruptions (supply delays) cause prolonged performance degradation.

The bounded mean betting procedure serves as a crucial interface between the domains of (1) sequential, anytime-valid statistical inference, and (2) online learning and portfolio selection algorithms. While recent work in both domains has established the exponential wealth growth of numerous betting strategies under any alternative distribution, the tightness of the inverted confidence sets, and the pathwise minimax regret bounds, little has been studied regarding the asymptotics of these strategies under the null hypothesis. Under the null, a strategy induces a wealth martingale converging to some random variable that can be zero (bankrupt) or non-zero (non-bankrupt, e.g. when it eventually stops betting). In this paper, we show the conceptually intuitive but technically nontrivial fact that these strategies (universal portfolio, Krichevsky-Trofimov, GRAPA, hedging, etc.) all go bankrupt with probability one, under any non-degenerate null distribution. Part of our analysis is based on the subtle almost sure divergence of various sums of $\sum O_p(n^{-1})$ type, a result of independent interest. We also demonstrate the necessity of null bankruptcy by showing that non-bankrupt strategies are all improvable in some sense. Our results significantly deepen our understanding of these betting strategies as they qualify their behavior on "almost all paths", whereas previous results are usually on "all paths" (e.g. regret bounds) or "most paths" (e.g. concentration inequalities and confidence sets).

Growing concerns about housing affordability have prompted the adoption of rent control policies and renewed debates over their effectiveness. This paper provides the first empirical evaluation of the 2024 rent control policy implemented in Catalonia under Spain's new national housing law. To identify the causal effect of the policy on the rental market, I use municipality-level administrative data and implement several difference-in-differences strategies and event study designs. The results point to a reduction in tenancy agreements and a less robust decrease in rental price growth. While the findings highlight important short-term consequences of rent control, they also underscore the need for caution due to data limitations and limited robustness in some estimates.

We revisit the classical Merton consumption--investment problem when risky-asset returns are modeled by stochastic differential equations interpreted through a general $α$-integral, interpolating between Itô, Stratonovich, and related conventions. Holding preferences and the investment opportunity set fixed, changing the noise interpretation modifies the effective drift of asset returns in a systematic way. For logarithmic utility and constant volatilities, we derive closed-form optimal policies in a market with $n$ risky assets: optimal consumption remains a fixed fraction of wealth, while optimal portfolio weights are shifted according to $θ_α^\ast = V^{-1}(μ-r\mathbf{1})+α\,V^{-1}\operatorname{diag}(V)\mathbf{1}$, where $V$ is the return covariance matrix and $\operatorname{diag}(V)$ denotes the diagonal matrix with the same diagonal as $V$. In the single-asset case this reduces to $θ_α^\ast=(μ-r)/σ^{2}+α$. We then show that genuinely state-dependent effects arise when asset volatility is driven by a stochastic factor correlated with returns. In this setting, the $α$-interpretation generates an additional drift correction proportional to the instantaneous covariation between factor and return noise. As a canonical example, we analyze a Heston stochastic volatility model, where the resulting optimal risky exposure depends inversely on the current variance level.

Around three quarters of Bitcoin transactions take place off-chain. Despite their significance, the vast majority of the empirical literature on cryptocurrencies focuses on on-chain transactions. This paper presents one of the first analysis of both on- and off-chain demand- and supply-side factors. Two hypotheses relating on-chain and off-chain demand and supply drivers to the Bitcoin price are tested in an ARDL model with daily data from 2019 to 2024. Our estimates document the differential contributions of on-chain and off-chain drivers on the Bitcoin price. Off-chain demand pressures have a significant impact on the Bitcoin price in the long-run. In the short-run, both demand and supply drivers significantly affect the Bitcoin price. Regarding transactions on the blockchain, only on-chain demand pressures are statistically significant - both in the long- and short-run. These findings confirm the dual nature of the Bitcoin price dynamics, where also market fundamentals affect the Bitcoin price in addition to speculative drivers. Bitcoin whale trading has less significant impact on price in the long-run, while is more pronounced contemporaneously and one-period lag.

Asset Liability Management (ALM) represents a fundamental challenge for financial institutions, particularly pension funds, which must navigate the tension between generating competitive investment returns and ensuring the solvency of long-term obligations. To address the limitations of traditional frameworks under uncertainty, this paper implements Distributionally Robust Optimization (DRO), an emergent paradigm that accounts for a broad spectrum of potential probability distributions. We propose and evaluate three distinct DRO formulations: mixture ambiguity sets with discrete scenarios, box ambiguity sets of discrete distribution functions, and Wasserstein metric ambiguity sets. Utilizing empirical data from the Canada Pension Plan (CPP), we conduct a comparative analysis of these models against traditional stochastic programming approaches. Our results demonstrate that DRO formulations, specifically those utilizing Wasserstein and box ambiguity sets, consistently outperform both mixture-based DRO and stochastic programming in terms of funding ratios and overall fund returns. These findings suggest that incorporating distributional robustness significantly enhances the resilience and performance of pension fund management strategies.

Modeling time series with long- or short-memory characteristics is a fundamental challenge in many scientific and engineering domains. While fractional Brownian motion has been widely used as a noise source to capture such memory effects, its incompatibility with Itô calculus limits its applicability in neural stochastic differential equation~(SDE) frameworks. In this paper, we propose a novel class of noise, termed Neural Network-kernel ARMA-type noise~(NA-noise), which is an Itô-process-based alternative capable of capturing both long- and short-memory behaviors. The kernel function defining the noise structure is parameterized via neural networks and decomposed into a product form to preserve the Markov property. Based on this noise process, we develop NANSDE-Net, a generative model that extends Neural SDEs by incorporating NA-noise. We prove the theoretical existence and uniqueness of the solution under mild conditions and derive an efficient backpropagation scheme for training. Empirical results on both synthetic and real-world datasets demonstrate that NANSDE-Net matches or outperforms existing models, including fractional SDE-Net, in reproducing long- and short-memory features of the data, while maintaining computational tractability within the Itô calculus framework.

This paper examines how gender and residential socioeconomic status shape hiring outcomes in the information technology sector using a field experiment from the city of Karachi, Pakistan. Employers in Pakistan can openly state preferences regarding gender, residential location, and other characteristics, but the majority in the information technology sector choose not to do so. This creates an opportunity to examine whether discrimination persists when such biases are not explicitly stated. An analysis of explicitly gender-targeted job ads shows that men are preferred over women across most occupations, even in traditionally pink-collar roles. Moreover, results from a resume audit experiment, submitting 2,032 applications to 508 full-time job openings, show that men receive more callbacks for job interviews than women, even in the absence of explicit gender preferences in job ads. The study also indicates a significant premium favoring candidates from high-income areas, who receive 45 percent more callbacks than applicants from low-income neighborhoods. This advantage remains robust even after controlling for commuting distance. Qualitative interviews with human resource officials suggest that employers associate productivity with both gender and neighborhood socioeconomic status. Residential address acts as a proxy for class background and signals education, skills, and perceived "fit" in professional settings. These perceptions may reinforce stereotypes, disadvantaging women and candidates from low-income backgrounds.

We study the estimation of repeatedly nested expectations (RNEs) with a constant horizon (number of nestings) using quantum computing. We propose a quantum algorithm that achieves $\varepsilon$-error with cost $\tilde O(\varepsilon^{-1})$, up to logarithmic factors. Standard lower bounds show this scaling is essentially optimal, yielding an almost quadratic speedup over the best classical algorithm. Our results extend prior quantum speedups for single nested expectations to repeated nesting, and therefore cover a broader range of applications, including optimal stopping. This extension requires a new derandomized variant of the classical randomized Multilevel Monte Carlo (rMLMC) algorithm. Careful de-randomization is key to overcoming a variable-time issue that typically increases quantized versions of classical randomized algorithms.

The mixed logit model is a flexible and widely used demand model in pricing and revenue management. However, existing work on mixed-logit pricing largely focuses on unconstrained settings, limiting its applicability in practice where prices are subject to business or regulatory constraints. We study the constrained pricing problem under multinomial and mixed logit demand models. For the multinomial logit model, corresponding to a single customer segment, we show that the constrained pricing problem admits a polynomial-time approximation scheme (PTAS) via a reformulation based on exponential cone programming, yielding an $\varepsilon$-optimal solution in polynomial time. For finite mixed logit models with $T$ customer segments, we reformulate the problem as a bilinear exponential cone program with $O(T)$ bilinear terms. This structure enables a Branch-and-Bound algorithm whose complexity is exponential only in $T$. Consequently, constrained pricing under finite mixtures of logit admits a PTAS when the number of customer segments is bounded. Numerical experiments demonstrate strong performance relative to state-of-the-art baselines.

This article identifies the factors that drove house prices in 13 advanced countries over the past 35 years. It does so based on Breiman s (2001) random forest model. Shapley values indicate that annual house price growth across countries is explained first and foremost by price momentum, initial valuations (proxied by price to rent ratios) and household credit growth. Partial effects of explanatory variables are also elicited and suggest important non-linearities, for instance as to what concerns the effects of CPI inflation on house price growth. The out-of-sample forecast test reveals that the random forest model delivers 44% lower house price variation root square mean errors (RMSEs) and 45% lower mean absolute errors (MAEs) when compared to an OLS model that uses the same set of 10 pre-determined explanatory variables. Notably, the same model works well for all countries, as the random forest attributes minimal values to country fixed effects.

Law-invariant functionals are central to risk management and assign identical values to random prospects sharing the same distribution under an atomless reference probability measure. This measure is typically assumed fixed. Here, we adopt the reverse perspective: given only observed functional values, we aim to either recover the reference measure or identify a candidate measure to test for law invariance when that property is not {\em a priori} satisfied. Our approach is based on a key observation about law-invariant functionals defined on law-invariant domains. These functionals define lower (upper) supporting sets in dual spaces of signed measures, and the suprema (infima) of these supporting sets -- if existent -- are scalar multiples of the reference measure. In specific cases, this observation can be formulated as a sandwich theorem. We illustrate the methodology through a detailed analysis of prominent examples: the entropic risk measure, Expected Shortfall, and Value-at-Risk. For the latter, our elicitation procedure initially fails due to the triviality of supporting set extrema. We therefore develop a suitable modification.

The study of probabilistic models for the analysis of complex networks represents a flourishing research field. Among the former, Exponential Random Graphs (ERGs) have gained increasing attention over the years. So far, only linear ERGs have been extensively employed to gain insight into the structural organisation of real-world complex networks. None, however, is capable of accounting for the variance of the empirical degree distribution. To this aim, non-linear ERGs must be considered. After showing that the usual mean-field approximation forces the degree-corrected version of the two-star model to degenerate, we define a fitness-induced variant of it. Such a `softened' model is capable of reproducing the sample variance, while retaining the explanatory power of its linear counterpart, within a purely canonical framework.

Obtaining utility maximizing optimal portfolios in closed form is a challenging issue when the return vector follows a more general distribution than the normal one. In this note, we give closed form expressions, in markets based on finitely many assets, for optimal portfolios that maximize the expected exponential utility when the return vector follows normal mean-variance mixture models. We then consider large financial markets based on normal mean-variance mixture models also and show that, under exponential utility, the optimal utilities based on small markets converge to the optimal utility in the large financial market. This result shows, in particular, that to reach optimal utility level investors need to diversify their portfolios to include infinitely many assets into their portfolio and with portfolios based on any set of only finitely many assets, they never be able to reach optimum level of utility. In this paper, we also consider portfolio optimization problems with more general class of utility functions and provide an easy-to-implement numerical procedure for locating optimal portfolios. Especially, our approach in this part of the paper reduces a high dimensional problem in locating optimal portfolio into a three dimensional problem for a general class of utility functions.

This paper addresses a key challenge in CDO modeling: achieving a perfect fit to market prices across all tranches using a single, consistent model. The existence of such a perfect-fit model implies the absence of arbitrage among CDO tranches and is thus essential for unified risk management and the pricing of nonstandard credit derivatives. To address this central challenge, we face three primary difficulties: standard parametric models typically fail to achieve a perfect fit; the calibration of standard parametric models inherently relies on computationally intensive simulation-based optimization; and there is a lack of formal theory to determine when a perfect-fit model exists and, if it exists, how to construct it. We propose a theoretical framework to overcome these difficulties. We first introduce and define two compatibility levels of market prices: weak compatibility and strong compatibility. Specifically, market prices across all tranches are said to be weakly (resp. strongly) compatible if there exists a single model (resp. a single conditionally i.i.d. model) that perfectly fits these market prices. We then derive sufficient and necessary conditions for both levels of compatibility by establishing a relationship between compatibility and LP problems. Furthermore, under either condition, we construct a corresponding concrete copula model that achieves a perfect fit. Notably, our framework not only allows for efficient verification of weak compatibility and strong compatibility through LP problems but also facilitates the construction of the corresponding copula models that achieve a perfect fit, eliminating the need for simulation-based optimization. The practical applications of our framework are demonstrated in risk management and the pricing of nonstandard credit derivatives.

Genetic Programming yields interpretable programs, but small syntactic mutations can induce large, unpredictable behavioral shifts, degrading locality and sample efficiency. We frame this as an operator-design problem: learn a continuous program space where latent distance has behavioral meaning, then design mutation operators that exploit this structure without changing the evolutionary optimizer. We make locality measurable by tracking action-level divergence under controlled latent perturbations, identifying an empirical trust region for behavior-local continuous variation. Using a compact trading-strategy DSL with four semantic components (long/short entry and exit), we learn a matching block-factorized embedding and compare isotropic Gaussian mutation over the full latent space to geometry-compiled mutation that restricts updates to semantically paired entry--exit subspaces and proposes directions using a learned flow-based model trained on logged mutation outcomes. Under identical $(μ+λ)$ evolution strategies and fixed evaluation budgets across five assets, the learned mutation operator discovers strong strategies using an order of magnitude fewer evaluations and achieves the highest median out-of-sample Sharpe ratio. Although isotropic mutation occasionally attains higher peak performance, geometry-compiled mutation yields faster, more reliable progress, demonstrating that semantically aligned mutation can substantially improve search efficiency without modifying the underlying evolutionary algorithm.

Bank failures can stem from runs on otherwise solvent banks or from losses that render banks insolvent, regardless of withdrawals. Disentangling the relative importance of liquidity and solvency in explaining bank failures is central to understanding financial crises and designing effective financial stability policies. This paper reviews evidence on the causes of bank failures. Bank failures -- both with and without runs -- are almost always related to poor fundamentals. Low recovery rates in failure suggest that most failed banks that experienced runs were likely fundamentally insolvent. Examiners' postmortem assessments also emphasize the primacy of poor asset quality and solvency problems. Before deposit insurance, runs commonly triggered the failure of insolvent banks. However, runs rarely caused the failure of strong banks, as such runs were typically resolved through other mechanisms, including interbank cooperation, equity injections, public signals of strength, or suspension of convertibility. We discuss the policy implications of these findings and outline directions for future research.

Do leading LLM developers possess a proprietary ``secret sauce'', or is LLM performance driven by scaling up compute? Using training and benchmark data for 809 models released between 2022 and 2025, we estimate scaling-law regressions with release-date and developer fixed effects. We find clear evidence of developer-specific efficiency advantages, but their importance depends on where models lie in the performance distribution. At the frontier, 80-90% of performance differences are explained by higher training compute, implying that scale--not proprietary technology--drives frontier advances. Away from the frontier, however, proprietary techniques and shared algorithmic progress substantially reduce the compute required to reach fixed capability thresholds. Some companies can systematically produce smaller models more efficiently. Strikingly, we also find substantial variation of model efficiency within companies; a firm can train two models with more than 40x compute efficiency difference. We also discuss the implications for AI leadership and capability diffusion.

Clinical trials shape medical evidence and determine who gains access to experimental therapies. Whether participation in these trials reflects the global burden of disease remains unclear. Here we analyze participation inequality across more than 62,000 randomized controlled trials spanning 16 major disease categories from 2000 to 2024. Linking 36.8 million trial participants to country-level disease burden, we show that global inequality in clinical trials participation is overwhelmingly shaped by country rather than disease burden. Country-level factors explain over 90% of variation in participation, whereas disease-specific effects contribute only marginally. Removing entire disease categories-including those traditionally considered underfunded-has little effect on overall inequality. Instead, participation is highly concentrated geographically, with a small group of countries enrolling a disproportionate share of participants across nearly all diseases. These patterns have persisted despite decades of disease-targeted funding and increasing alignment between research attention and disease burden within diseases. Our findings indicate that disease-vertical strategies alone cannot correct participation inequality. Reducing global inequities in clinical research requires horizontal investments in research capacity, health infrastructure, and governance that operate across disease domains.

We propose a novel transformer-based neural network architecture (ICON-OCnet) for solving optimal order execution problems in the presence of unknown price impact. Our architecture facilitates data-driven in-context operator learning for the incurred price impact by merging offline pre-training with online few-shot prompting inference. First, the operator learning component (ICON) learns the prevailing price impact environment from only a few executed trade and price impact trajectories (time series data) provided as context. Second, we employ ICON as a surrogate operator to train a neural network policy (OCnet) for the optimal order execution strategy for the price impact regime inferred from the in-context examples. We study the efficiency of our approach for linear propagator models with path-dependent transient price impact and explicitly known optimal execution strategies. In this model class, price impact persists and decays over time according to some propagator kernel. We illustrate that ICON is capable of accurately inferring the underlying price impact model from the data prompts, even for propagator kernels not seen in the training data. Moreover, we demonstrate that ICON-OCnet correctly retrieves the exact optimal order execution strategy for the model generating the in-context examples. Our introduced methodology is very general, offering a new approach to solving path-dependent optimal stochastic control problems sample-based with unknown state dynamics.

The Global Minimum Tax (GMT) is applied only to firms above a certain size threshold, permitting countries to set differential tax rates for small and large firms. We analyse tax competition among multiple tax havens and a non-haven country for heterogeneous multinationals to evaluate the effects of this partial coverage of GMT. Upon the introduction of a low but binding GMT rate, the havens commit to the single uniform GMT rate for all multinationals. However, gradual increases in the GMT rate induce the havens, and subsequently the non-haven, to adopt discriminatory, lower tax rates for small multinationals. Our calibration exercise shows that introducing a GMT rate close to 15\% results in a regime where only the havens adopt split tax rates. Welfare and tax revenues fall in the havens but rise in the non-haven, yielding a positive net gain worldwide.

We develop a novel deep learning approach for pricing European basket options written on assets that follow jump-diffusion dynamics. The option pricing problem is formulated as a partial integro-differential equation, which is approximated via a new implicit-explicit minimizing movement time-stepping approach, involving approximation by deep, residual-type Artificial Neural Networks (ANNs) for each time step. The integral operator is discretized via two different approaches: (a) a sparse-grid Gauss-Hermite approximation following localised coordinate axes arising from singular value decompositions, and (b) an ANN-based high-dimensional special-purpose quadrature rule. Crucially, the proposed ANN is constructed to ensure the appropriate asymptotic behavior of the solution for large values of the underlyings and also leads to consistent outputs with respect to a priori known qualitative properties of the solution. The performance and robustness with respect to the dimension of these methods are assessed in a series of numerical experiments involving the Merton jump-diffusion model, while a comparison with the deep Galerkin method and the deep BSDE solver with jumps further supports the merits of the proposed approach.

We study how religious competition-defined as the entry of a religious organization with innovative worship practices into a predominantly Catholic municipality-affects domestic violence. Using municipality-level data from Colombia and a two-way fixed effects design, we find that the arrival of the first non-Catholic church leads to a significant reduction in reported cases of domestic violence. We argue that religious competition incentivizes churches to adopt and diffuse norms and practices that more effectively discourage such violence. Effects are largest in municipalities with smaller, younger, and more homogeneous populations-contexts that facilitate both intense competition and norm diffusion. Consistent with this mechanism, areas with more new non-Catholic churches exhibit greater rejection of domestic violence-particularly among the religiously observant-and higher female labor force participation. These findings contribute to the literature on the cultural determinants of domestic violence by identifying religious competition as a catalyst for cultural change.

This study investigates the impact of integrating gender equality into the Colombian constitution of 1991 on attitudes towards gender equality, experiences of gender-based discrimination, and labor market participation. Using a difference-in-discontinuities framework, we compare individuals exposed to mandatory high school courses on the Constitution with those who were not exposed. Our findings show a significant increase in labor market participation, primarily driven by women. Exposure to these courses also shapes attitudes towards gender equality, with men demonstrating greater support. Women report experiencing less gender-based discrimination. Importantly, our results suggest that women's increased labor market participation is unlikely due to reduced barriers from male partners. A disparity in opinions regarding traditional gender norms concerning household domains is observed between men and women, highlighting an ongoing power struggle within the home. However, the presence of a younger woman in the household appears to influence men's more positive view of gender equality, potentially indicating a desire to empower younger women in their future lives. These findings highlight the crucial role of cultural shocks and the constitutional inclusion of women's rights in shaping labor market dynamics.

Ethnic-based political inequality is widespread, yet its underlying drivers remain poorly understood. This paper shows that an ethnic group's relative size is a key correlate of its access to central executive power. Using data on 575 groups across 181 countries from 1946 to 2021, I document a robust inverted-U-shaped relationship: groups of intermediate size are significantly more likely to gain political inclusion than both very small and very large ones. A simple model explains this pattern as the result of elite trade-offs between the risks of conflict from exclusion and the costs of sharing political rents. The model further predicts-and the data confirm-that the inverted-U is most pronounced in countries with historically competitive institutions. These findings offer new insight into the joint role of ethnic composition and institutions in shaping patterns of ethnic political inclusion.

I construct a Market Stress Probability Index (MSPI) that estimates the probability of high stress in the U.S. equity market one month ahead using information from the cross-section of individual stocks. Using CRSP daily data, each month is summarized by a set of interpretable cross-sectional fragility signals and mapped into a forward-looking stress probability via an L1-regularized logistic regression in a real-time expanding-window design. Out of sample, MSPI tracks major stress episodes and improves discrimination and accuracy relative to a parsimonious benchmark based on lagged market return and realized volatility, delivering calibrated stress probabilities on an economically meaningful scale. Further, I illustrate how MSPI can be used as a probability-based measurement object in financial econometrics. The resulting index provides a transparent and easily updated measure of near-term equity-market stress risk.

Finding similar bonds remains challenging in fixed-income analytics, as numerical financial attributes often overshadow categorical non-financial ones such as issuer sector and domicile. This paper shows that these categorical attributes dominate the predictability of spread curves and proposes embedding models to capture their semantic similarities, outperforming one-hot and many other baselines. Evaluated via sparse-issuer augmentation, the approach improves risk modeling and curve construction.