We develop an agent-based, stock-ow-consistent model of an economy undergoing automation, built to ask which scal instrument reaches the durable surplus that articial intelligence creates. The model separates two channels: a competitive return on reproducible robotic capital, and a mobile, foreign-held intellectual-property rent earned by AI. Production is an endogenous nested-CES technology; wealth concentration is microfounded through heterogeneous, persistent returns to wealth; and taxation and capital mobility are modelled as behavioural responses. The central result is that the durable surplus is the foreign-held AI rent, a cross-border licence fee that corporate, robot, and compute or token taxes largely miss and that only a source-based levy (a digital-services-style tax or a withholding) reaches. The appropriate policy depends decisively on whether a country owns the automation or imports it: for a host that owns the rent the problem is domestic inequality, reached by progressive and wealth taxes; for a rent-importing host the problem is base erosion and a gradual transfer of capital ownership abroad, which a residence-based wealth tax cannot reach. We report conditional orderings, stress-tested with global (Sobol) sensitivity and a formal stability analysis, rather than point forecasts.

This paper develops a reinforcement-learning approach to continuous-time risk-sensitive benchmarked asset allocation in a partly model-based setting. The benchmarked problem does not directly fit the standard Markovian stochastic-control template: the state is uncontrolled, whereas the terminal reward contains a controlled Itô integral. We use free energy-entropy duality to reformulate the problem as a linear-quadratic-Gaussian stochastic differential game under an equivalent probability measure, yielding explicit finite- and infinite-horizon saddle-point solutions. This structure guides a continuous-time $q$-learning actor-critic method: the quadratic value function motivates the critic, while the affine saddle-point controls motivate deterministic actors for the portfolio allocation and adversarial control. The learned allocation admits an economic interpretation through fractional Kelly decompositions. A proof-of-concept implementation calibrated to U.S. equity data shows that the actors learn the optimal policy with high accuracy and reveals a favorable asymmetry: the portfolio actor receives a cleaner learning signal than the auxiliary adversarial actor.

Modeling the future requires specifying conditional laws relative to an evolving information flow and describing their movement across time. This paper provides a unified mathematical synthesis of this problem along a single spine. Filtrations encode known data; conditional expectation and regular conditional probabilities yield point and distributional forecasts; Markov kernels and semigroups propagate observables and laws; and infinitesimal generators encode local dynamics, producing Kolmogorov equations and stochastic differential equations. Along this spine, martingales isolate surprise, filtering handles partial observation, finance prices futures, stochastic control optimizes choices, and ergodic theory describes the far future. The contribution is architectural. We explicitly connect derivations that turn classical objects into a unified forecasting calculus: the tower property becomes the semigroup law; Ito's formula yields the backward equation after conditioning; integration by parts provides the forward operator; and generator perturbations become model-risk distortions. Forecasting is shown not as mere data extrapolation, but the construction of dynamically coherent conditional distributions constrained by information, geometry, and admissible models. These concepts are illustrated via Gaussian Ornstein--Uhlenbeck and non-Gaussian Cox--Ingersoll--Ross processes, demonstrating how abstract machinery produces explicit transition laws, spectral decompositions, term-structure formulae, and asymptotics in diverse geometries. We recast density evolution as a Wasserstein gradient flow, place forecasting within Hilbert, Fisher--Rao, and Wasserstein geometries, provide a discrete-time empirical dictionary, and address model-risk. The result is a compact mathematical map from information to prediction, local dynamics to global laws, and idealized models to empirical forecasting.

Complex model suites composed of multiple interacting component models are widely used in financial forecasting and risk management. In model performance testing, including in-sample backtesting (BT) and out-of-sample ongoing performance monitoring (OPM), a material gap between a model-suite forecast and the realized outcome must often be attributed to individual component models for development, validation, and regulatory review. This paper studies this gap-attribution problem in the expected loss framework, where exposure at default (EAD), prepayment or single monthly mortality (SMM), probability of default (PD), and loss given default (LGD) interact multiplicatively and are aggregated across loans and projection periods. We first formalize standard walk analysis and show why its attribution is generally order dependent. We then adapt two order-independent attribution frameworks: an augmented Logarithmic Mean Divisia Index (LMDI) approach tailored to the expected-loss structure, and a more general Shapley value approach based on averaging marginal contributions over all component orderings. We derive both elementwise and vectorized formulas to support efficient implementation, with the additional computation time for gap attribution typically limited to a few seconds in practical portfolio-scale examples. Finally, we discuss the connections among walk analysis, LMDI, and Shapley attribution, and show how the attribution framework extends to model suites with an additional Monte Carlo simulation layer.

We study the fee policy of a liquidity provider (LP) in a constant-product automated market maker (AMM) whose fee can be adjusted continuously, as enabled by programmable hooks. Building on the loss-versus-rebalancing (LVR) framework of Milionis et al. (2022) and its extension to nonzero fees by Milionis et al. (2024), we model the LP's wealth relative to the continuously rebalanced benchmark as a controlled process in which the fee governs two opposing forces: it raises revenue per uninformed trade while discouraging uninformed volume, and it widens the no-arbitrage band, which lowers the rate at which arbitrageurs extract value. Because the fee enters only the drift of relative wealth and never its diffusion, the LP's expected-utility problem reduces to an ergodic control problem whose solution is a pointwise volatility feedback. We prove that the growth-optimal fee is independent of the LP's wealth and of its constant relative risk aversion, that it collapses to a static constant when volatility is constant, and that it is strictly increasing in instantaneous variance, so that the optimal schedule is pro-cyclical. When volatility is stochastic, we characterise the optimal fee through a scalar ergodic Hamilton-Jacobi-Bellman equation and a linear Poisson equation, solved by a finite-difference scheme. We further show that the optimal fee is invariant to price jumps under logarithmic preferences, relate the optimal fee to a stylised model of competition among venues, and treat gas costs through an impulse-control dead-band. In a calibration to liquid large-capitalisation conditions, the optimal dynamic fee weakly dominates every static and volatility-linked heuristic fee on each simulated path, improving the LP's growth rate over the best static fee by a modest but uniformly positive margin, with a dead-band rendering gas costs negligible.

Using the path-integral formalism, we develop an accurate and easy-to-compute semi-analytical approximation for a general class of {default intensity} models. We illustrate the accuracy of the method by presenting results for the Black-Karasinski model for which the proposed approximation provides remarkably accurate results, even in regimes of high volatility and multi-year time horizons. The accuracy and the computational efficiency of the proposed approximation makes it a viable alternative to fully numerical schemes for a variety of applications in econometrics and derivatives pricing, including the computation of XVA for credit products. As a practical example, we consider the pricing of a quanto Credit Default Swap (CDS) under stochastic intensity of default and an FX devaluation model.

This paper extends the sufficient-statistics formula for efficient unemployment developed by Michaillat and Saez (2021) to account for part-time employment. I introduce two additional sufficient statistics that measure the share of part-time employment and part-time hours relative to full-time hours. Applying the framework to the United States (1951-2026) and Japan (1970-2025), I compare the effects of total part-time employment and involuntary part-time employment on efficient unemployment. Total part-time employment has substantially larger effects than involuntary part-time employment. While involuntary part-time employment provides information about labor-market slack, the main change in efficient unemployment comes from part-time work itself because part-time workers supply fewer market hours than full-time workers. Under the total part-time calibration, efficient unemployment averages 4.7 percent in the United States before COVID and 4.2 percent after COVID. In the Japanese application, the full-sample average is 2.7 percent. The distinction is especially important in Japan, where part-time employment is widespread and often reflects flexible work arrangements. These findings suggest that aggregate labor input, rather than involuntary part-time employment alone, is an important determinant of labor-market efficiency.

Prudence is a stability property of risk functionals recently introduced by Wang and Zitikis and subsequently studied by Amarante and Liebrich. In this paper, we first establish general relationships between prudence and other stability properties, showing, in particular, that weak prudence and prudence coincide for a broad class of convex, law-invariant functionals. We then prove that prudence is preserved by cash-additive hulls of star-shaped functionals under a simple asymptotic condition, and by inf-convolutions of convex, cash-additive, law-invariant prudent functionals. Our results provide general methods for constructing prudent risk measures from existing prudent functionals.

Has generative AI changed how labor markets value human capital? We study this question using data from Upwork, a large online labor market. Representing worker profiles with high-dimensional text embeddings, we compute the importance of human capital information and price in predicting labor demand, and incorporate these measures into a difference-in-differences design around the release of ChatGPT. We find that in more AI-exposed job categories, the importance of human capital declines and the importance of price rises, suggesting a commoditization effect of AI on labor. Two additional findings support commoditization as a mechanism: The demand premium enjoyed by workers with strong human capital declines in more AI-exposed categories, and demand reallocates toward lower-priced workers. Our results have implications for the design of online labor markets, workers' incentives to invest in human capital, and labor welfare.

Intelligent transportation systems increasingly rely on decentralized mechanisms to allocate limited resources such as freight capacity, warehouse availability, charging infrastructure, and network bandwidth. Efficient allocation requires pricing mechanisms that adapt dynamically to demand while preserving system stability. This paper investigates weighted constant-function market makers as a decentralized resource allocation mechanism for intelligent transportation systems, adapting the weighted invariant from Balancer-type automated market makers to model a generalized formulation over multiple tokenized resources. The standard literature documents exactly four resource allocation operations: proportional contribution, proportional withdrawal, single-resource contribution, and single-resource withdrawal, each obtained via separate derivations. This paper presents a single closed-form formula that unifies all four cases and extends them to two previously undocumented operations: partial-proportional contributions and fully non-proportional operations. The unified formula reveals that the conservation invariant and the allocation formula are structurally identical; the invariant itself is the general allocation formula. We prove two swap-decomposition theorems showing that, in a fee-less environment, any non-proportional operation is equivalent to an internal rebalancing swap combined with a proportional operation. Both theorems generalize previous propositions from single-resource to arbitrary multi-resource operations. The proposed framework provides a mathematically grounded mechanism for decentralized market-based coordination in transportation networks.

Sectoral default dependence is usually described by a static correlation matrix, a static copula, or a small number of common factors. Such representations, when specified separately at each observation horizon, do not by themselves explain why the effective dependence observed in monthly credit data differs from that observed after annual aggregation. This paper proposes a dynamic low-rank state-space model for monthly multi-sector default-count data and studies the dependence structure induced by temporal coarse-graining. The leading eigenvectors of the monthly sectoral default-rate correlation matrix are used as fixed loading directions for persistent AR(1) latent credit-state factors, and defaults are modeled through a binomial observation layer. Survival aggregation of monthly posterior probability paths induces horizon-dependent distributions of sectoral default-probability vectors, from which effective correlation matrices, eigenvalue spectra, and posterior-implied rank copulas are obtained. Applied to S\&P monthly sector-level default-count data from 1981--01 to 2021--09, a two-factor specification captures the dominant market-wide and sector-rotation modes, reproduces the annual amplification of the leading eigenvalues, and generates heterogeneous copula structures across sector pairs. In an annual forecast evaluation, the dynamic factor specifications reduce the under-dispersion of static binomial and beta-binomial baselines, improving interval coverage and CRPS for aggregate portfolio counts. In log-score-based forecast comparisons, the one-factor specification is highly competitive, whereas the two-factor specification improves sector-level calibration as measured by per-sector CRPS.

In this paper, we study the patterns of ethnic endogamy in Croatia in relation to six ethnic groups between 1970 and 2015. We find that, over the 45-year period analyzed, the segmentation of the Croatian marriage market was weaker between Czechs and non-Czechs, Hungarians and non-Hungarians, Italians and non-Italians, and Slovaks and non-Slovaks than between Serbs and non-Serbs or Croats and non-Croats. This finding is substantiated by survey evidence revealing similar patterns on relative social distances between different ethnic groups in Croatia and Serbia. From a methodological perspective, we show that a plausible ranking of the degree of segmentation of the Croatian marriage market along ethnic lines can be obtained only when marital sorting is characterized by a carefully selected indicator. While a recently reinvented indicator captures sensible patterns of ethnic endogamy, the commonly applied odds-ratio fails to produce results consistent with survey evidence. AI generated video summary of the paper: this https URL

Battery energy storage systems (BESS) are expected to play an important role in electricity markets with increasing shares of renewable generation. While existing research has primarily focused on price arbitrage and ancillary services, the role of grid fees in shaping BESS operation and profitability remains insufficiently understood. This article investigates how different levels of distribution fees affect the scheduling and economic viability of BESS in the day-ahead electricity market. The analysis employs a mixed-integer linear programming model of BESS operation combined with electricity price data from the German market. Four system configurations are considered: stand-alone storage and BESS combined with consumption, generation, or both. The value of storage is measured as the difference between system profits with and without BESS. In addition, a rolling-horizon optimization framework is used to evaluate the impact of forecast uncertainty and decision horizon length on operational outcomes. The results show that grid fees significantly influence both BESS profitability and operational strategies. For stand-alone storage, higher transmission charges reduce arbitrage revenues and battery utilization. When BESS is integrated with consumption and generation units, load shifting and self-consumption become the dominant sources of value, leading to a non-monotonic relationship between grid fees and storage profitability. These findings highlight the importance of considering tariff structures when evaluating storage investments and designing regulatory frameworks for electricity markets with increasing flexibility needs.

We study the evolution of transaction speed and fees from January 2024 through March 2026, comparing Ethereum Mainnet and its Layer 2 (L2) networks, as well as Solana and Polygon. Ethereum has undergone upgrades that have increased block size and blob count. These upgrades have doubled transactions per second (TPS) on both the Mainnet and the L2 networks. Mainnet median fees have fallen from over \$2 to under \$0.02, and L2 median fees have fallen more than 95% from \$0.05 to \$0.0015. We forecast that Mainnet median fees will converge with Solana in August 2027, but TPS will remain below 100 until 2034. The L1 Strawmap, proposing EIP-7938, a potential exponential increase in the gas limit, brings the Mainnet to only 100 TPS in January 2028. With continued blob expansion, L2s will surpass Solana TPS in March 2029 and have lower median fees by October 2026.

Classical option pricing models, such as Bachelier and Black--Scholes--Merton, postulate symmetric Brownian diffusion, which limits their capacity to reflect empirical phenomena including return skewness, heavy tails, and volatility asymmetry. This paper develops an innovative extension: the Geometric Asymmetric Brownian Motion (GABM), unifying asymmetric Brownian motion and random walk methodologies within the Bachelier--Black--Scholes--Merton framework. The approach harnesses the Cherny--Shiryaev--Yor invariance principle (CSYIP) to define asymmetric random walk integrals, where local time at the origin generates skewness and state-dependent risk. Closed-form option pricing formulas are derived, and a discrete-time binomial tree algorithm is constructed and shown to converge rigorously to the GABM limit. By incorporating a smoothed functional form based on the normal inverse Gaussian distribution, the model allows for flexible, state-dependent volatility calibration. Numerical experiments demonstrate the resulting option price and implied volatility surfaces, highlighting the framework's enhanced ability to capture persistent market asymmetry and complex risk behaviors observed in empirical data.

In 2019, North Dakota repealed its Sunday closing law, which had required most non-grocery stores to close between midnight and noon. Using this policy change and consumer GPS data, we study the impact of opening hours on shopping behavior and welfare. We compare visits before and after the repeal in North Dakota and neighboring states using difference-in-differences and event-study designs. The repeal caused a large increase in Sunday morning visits, originating partly from intertemporal, store-type, and cross-border substitution. The closing law's welfare loss is equivalent to increasing the travel distance to affected stores by about 1.4 miles per consumer.

India's post-liberalisation higher education expansion was premised on widening credential access for historically excluded groups. We show that the groups most expected to benefit - Scheduled Caste and Scheduled Tribe (SC/ST) workers - instead bore a disproportionate share of the resulting wage cost, a pattern we term the double whammy. We merge eight rounds of the NSS Employment-Unemployment Survey (1987-2011) with a district-level measure of college-expansion intensity built from the All India Survey on Higher Education (AISHE) and estimate reduced-form triple- and quadruple-difference wage specifications across 91 districts in six states (N = 79,904), interacting graduate status, expansion intensity, and post-expansion cohort. The human capital return to a degree remains large and positive throughout (about 1.08 log points), yet the graduate wage premium erodes for post-2004 cohorts in high-expansion districts: non-SC/ST graduates earn roughly 9 per cent less than comparable graduates in low-expansion districts at mean intensity, and SC/ST graduates face an additional penalty of about 34 per cent (a combined shortfall near 43 per cent). The SC/ST differential is statistically indistinguishable from zero before the expansion and emerges only afterwards. Non-graduate placebo and pre-trend tests are broadly consistent with a credential-signalling channel, though we flag the limits of the design rather than claim clean identification. The results suggest that expanding access without commensurate investment in institutional quality can deepen, rather than narrow, labour-market inequality for disadvantaged groups.

Forecasting benchmarks for retrieval-augmented LLMs routinely confound model capability with information leakage: features labeled with a target's timestamp are often not observable at the system's decision time. We study leakage-controlled equity factor ranking with a retrieval-augmented 7B open-source LLM forecaster. At each month-end from 2023-04 to 2026-03, the forecaster observes only decision-time information: lag-shifted FRED macro variables, recent macro-event summaries, and the Cleveland Fed's archived daily CPI nowcast for unreleased current-month inflation. A macro-analog retrieval module selects historical states, a critic LLM compresses them into one tactical rule, and an actor LLM maps the current state and recent rules into scores for seven U.S. equity style factors. The full pipeline obtains a median monthly Spearman rank IC of +0.154, with positive means across three non-overlapping contiguous 12-month subwindows; the mean IC remains statistically underpowered, with a bootstrap 95% confidence interval that includes zero. Non-LLM baselines under the same decision-time constraint demonstrate that a kNN macro-analog model recovers a comparable median IC, indicating that real-time inflation information and macro-similar retrieval explain much of the median signal. The LLM pipeline retains higher mean IC and a stronger long-short allocation sanity check, suggesting that any marginal benefit is concentrated in the extreme rankings that drive long-short portfolio formation. A descriptive audit of the 36 critic rules and per-month case studies appears in the appendix.

We propose a deterministic adversarial market model in which apparent randomness emerges endogenously from the interaction between a market mechanism and a population of predictive traders. Unlike a classical generative adversarial network, the model does not attempt to imitate an external empirical data distribution and does not inject random noise into a generator. The market is represented by a deterministic binary return path, while traders learn predictive strategies from observed in-sample history and trade on an out-of-sample continuation. The market then adapts against the traders by reducing their predictive and trading edge. The central experiment begins with a smooth, highly predictable market path. Traders with multiple lookback windows and multiple holding periods learn to predict future cumulative returns. Initially, these traders earn large out-of-sample profits. After adversarial market adaptation, their out-of-sample profitability collapses toward zero. Importantly, in the final clean specification, no explicit sign-balance, transition-rate, or autocorrelation penalties are imposed. Nevertheless, the out-of-sample return sequence becomes balanced, has transition rate close to one half, has low autocorrelation, and passes block-based distributional diagnostics. In a medium-size experiment with $T_{\mathrm{IS}}=2000$ and $T_{\mathrm{OOS}}=10000$, the out-of-sample positive-return fraction is $0.5010$, the transition rate is $0.4896$, and the maximum absolute autocorrelation is $0.0275$. Binary return blocks transformed into dyadic variables are close to uniform on $[0,1]$, and normalized block sums are broadly consistent with a standard normal law. These results support the hypothesis that market randomness can arise as the endogenous residue of arbitrage pressure rather than from exogenous stochastic shocks.

The Black-Scholes model has been extensively used for option pricing, but exhibits limitations in its reliance on geometric Brownian motion and fixed volatility assumptions. This paper proposes an enhanced model incorporating stochastic volatility with jumps modeled by a Lévy process. Leveraging multidimensional Itô calculus, we derive a pricing formula for European call options under the new framework. Additionally, Malliavin calculus enables the derivation of an exact expression for at-the-money implied volatility. The proposed model is shown to better capture empirical features like volatility smiles. Analysis of VIX data demonstrates the model's ability to match observed market volatility. The integration of Lévy processes and Malliavin calculus represents a valuable advancement in addressing deficiencies in the classic Black-Scholes model. Further empirical testing is warranted to validate the approach across varying market conditions and option types.

Automation and artificial intelligence (AI) are reshaping labor demand unevenly across space, creating an urgent imperative for place-sensitive education and workforce policy. This study asks whether regional exposure to automation and to AI relates to local employment and wages in opposite ways, and whether those relationships differ between urban and rural regions -- two questions whose answers carry direct implications for how skills training and digital education should be targeted. Using a region-by-year panel and shift-share measures of technological exposure built from baseline industry and occupation composition, we estimate two-way fixed-effects and instrumental-variable models that interact exposure with an urban indicator. The framework distinguishes automation exposure, concentrated in routine work, from AI exposure, concentrated in cognitive work -- a distinction that maps directly onto the types of skills that education systems need to develop or preserve. Estimates show automation exposure lowering employment and wages, with the employment loss cushioned in cities, while AI exposure raises wages and concentrates in urban regions. Technology therefore reshapes, rather than simply widens, the divide. The findings argue for place-sensitive policy: weighting reallocation and reskilling support toward routine-exposed rural regions, while extending digital infrastructure and AI-complementary skills outward so that rural workers can share AI's wage gains rather than absorb only automation's losses.

Value-at-Risk (VaR) is a standard regulatory risk measure, and its failure of subadditivity is well known. Much less appreciated is that for sufficiently heavy-tailed losses, VaR can be superadditive uniformly across all probability levels, a phenomenon strictly stronger than the asymptotic superadditivity studied in extreme value theory. We call this property universal VaR superadditivity (UVS). We study UVS and its stronger weighted version (WUVS) as properties of random vectors rather than of marginal distributions. This perspective unifies and extends a recent line of work on iid infinite-mean models. UVS, except for trivial cases, imposes an infinite-mean structure. We establish preservation properties of UVS and WUVS under increasing and convex transformations, weak convergence, and certain distributional mixtures, and use these tools to prove UVS and WUVS for non-identically distributed risks in several large families including completely subscalable, super-Cauchy, and inverted subadditive risks, extending results previously available only in the iid case. In many results, we also establish strict versions of UVS and WUVS, which lead to stronger decision-theoretic implications. As a consequence, for any portfolio satisfying WUVS, every distortion risk measure is superadditive, so an optimal allocation concentrates on a single asset, and diversification is never beneficial.

This paper studies centralized risk sharing with endogenous prices. Multiple policyholders transfer risks to a central insurer through indemnity decisions, while prices are determined by pricing functionals applied to ceded risks. The resulting problem is multiobjective, with Pareto optimality as the natural efficiency criterion. We show that classical Pareto optimality may fail to reveal whether all agents are represented in a balanced decision process that scalarized objectives may assign zero weight to some agents, and group aggregates may obscure individual risk positions. Motivated by bilateral Pareto characterizations through sequential optimization, we introduce inclusive and fair Pareto optimality, a representation-based refinement requiring every agent to appear exactly once, either individually or as part of a group, in a finite ordered sequence of optimizations. Our main result proves equivalence between this concept and balanced sequential optimization, placing it between Geoffrion-proper Pareto optimality and classical Pareto optimality. An illustrative example demonstrates the framework using the Expected Shortfall.

Automated Market Makers based on concentrated liquidity, such as Uniswap v3, significantly improve capital efficiency but expose Liquidity Providers (LPs) to adverse selection costs, formalized as Loss-Versus-Rebalancing (LVR). While theoretical literature quantifies these costs, the interplay between realistic blockchain microstructure and endogenous pricing mechanisms remains under-explored. This paper develops a granular Agent-Based Model of a Uniswap v3 pool interacting with a stochastic reference market governed by Heston volatility dynamics. The framework incorporates discrete block propagation, mempool latency, and a heterogeneous population of agents, including latency-sensitive arbitrageurs, smart routers, Maximal Extractable Value searchers, and active LPs benchmarked against a frictionless rebalancing strategy. We propose and evaluate dynamic fee schedules driven by volatility and order-flow toxicity proxies intended to compensate LPs for adverse-selection losses. Our simulations investigate the conditions under which LPs can achieve positive hedged Profit and Loss (fees minus LVR). The analysis suggests that dynamic fee adjustments can improve hedged LP profitability mainly by increasing fee income in states associated with stale-price risk. Depending on the configuration, these rules may also affect realized LVR, but the current aggregate results support compensation for LVR more directly than a reduction of LVR itself.

Non-price interventions targeting specific household water uses are increasingly central to conservation policy, but whether end-use savings translate into lower aggregate demand remains unresolved. This paper reports evidence from a pre-registered field experiment in which 775 Finnish households were randomized to a shower timer, a water-saving shower head, or the same shower head with real-time feedback. Utility-grade water meters measure household-level effects, while shower-level data provide complementary end-use evidence for the two shower-head treatments. The shower timer has no detectable effect. In contrast, the water-saving shower head reduces daily household demand by about 5%, and pairing it with real-time feedback doubles this reduction to about 10%. The convergence between shower- and meter-based estimates shows that end-use savings largely pass through to aggregate demand rather than being offset elsewhere in the home. Cost-benefit analysis indicates that combining technological constraint with salient point-of-use feedback dominates reminder-based strategies.

Institutional rebalancing is a batched optimization workload with a hard operating deadline: hundreds of accounts need new weights under budget, turnover, exposure, exclusion, and tax-aware controls before trading can proceed. This paper evaluates Asymmetry PRISM, a CPU/GPU portfolio optimization engine, through a public evaluation boundary; problem data in, and returned weights, status codes, timings, memory class, external feasibility diagnostics, eligible objective comparisons, and audit records out. Within that boundary, the evaluation protocol fixes hardware and software versions, declares timing lanes, separates cold single calls from repeated workloads, and admits objective-gap claims only where an eligible reference solver completed. On completed multi-solver rows from N=100 to N=2,000, Asymmetry PRISM-CPU is 4.5x to 24.1x faster than the fastest completed reference row in the same lane. In the production queue study, Asymmetry PRISM-GPU completes 500/500 accounts over a 10,000-instrument universe in 109.5 s within a declared 25-minute operating window, with zero missed deadlines and an audit record for every solve; the recorded OSQP queue baseline completes 4/500. On an operationally constrained real-data suite (tax-motivated transition penalties, restriction caps, turnover controls, batches), Asymmetry PRISM clears constrained solves 3.4x to 126.7x faster than the best completing incumbent at certified-equal objectives, and the GPU route widens to 8.8x over the CPU route at N=384,800. Rows without a completed reference are reported as feasibility, timing, memory, and failure-status evidence.

We introduce \emph{Equilibrium World Models} (EWMs), a deep-learning method for globally solving dynamic stochastic models that feature rare disasters, binding constraints, and counterfactual states. Standard unsupervised neural-network-based solvers impose equilibrium conditions only on states generated by their own simulated policy. Their solutions can therefore be self-confirming: accurate on the simulated path, but untested off it, sensitive to initialization, and costly when expectations must be recomputed at each step. EWMs change the computational representation, not the economics. They enforce the model's exact equilibrium conditions on a broader, model-generated distribution of ordinary, rare, stressed, and counterfactual states. They carry the continuation with a learned surrogate, but certify the resulting policy strictly against the true equilibrium conditions. We provide an error decomposition, an off-path residual bound, and a convergence result linking self-confirming solutions to rational-expectations equilibria. We demonstrate EWMs through a sequence of test cases that isolate the main pathologies of classical deep-learning solvers and then scale them to richer economies. In a rare-disaster Brock--Mirman laboratory, coverage reduces disaster-region residuals by an order of magnitude. In a high-dimensional international real-business-cycle model, classical deep-learning solvers fail from all random starts, whereas EWMs converge from nearly all and evaluate continuations up to two orders of magnitude less often. When actions move transition measures, EWMs use action-conditioned continuations to recover the relevant policy margin. In a heterogeneous-agent economy with aggregate risk, EWMs compress the numerical representation of the wealth distribution by at least 25x while imposing exact full-distribution rational-expectations conditions.

Synthetic generators of daily equity returns let practitioners stress test, backtest, and design scenarios that a single realized market history cannot supply, but only if the generator reproduces the stylized facts of real returns: heavy tails, negligible linear autocorrelation, and slow decay of the absolute-return autocorrelation. Hidden Markov models with few Gaussian states were long thought unable to reproduce that slow decay, and the standard fix was to abandon them for more complex hidden semi-Markov models. We revisit this issue with a continuous hidden Markov model whose regime chain governs the autocorrelation while per-regime densities govern the marginal, separating the temporal and distributional sides of the original failure. A unified expectation-maximization framework fits Gaussian, Student-t, Laplace, and generalized-error emissions under shared forward-backward recursions and quantile-based initialization, and a spectral identity bounds the number of decay modes by the rank of the centred transition matrix. Across SPY walk-forward folds, a sector-balanced 30-ticker panel, a CRSP cross-decade transfer, and a six-asset basket, that bound was not binding once a few states were used: heavy-tailed marginals, not additional decay modes, closed most of the fit gap, recovering volatility clustering above the i.i.d. baseline and narrowing the kurtosis gap without a tuning hyperparameter. The original failure is therefore distributional, not temporal. On daily US equities, a simple, interpretable Markov model suffices, and unlike a bootstrap or semi-Markov fit that wins only on a single-window fit, the fitted model also yields a regime-conditional Value-at-Risk that passes a joint conditional-coverage test and a copula that reproduces cross-asset correlations: one interpretable generator serving both path simulation and downstream risk and portfolio tasks.

In this paper, we consider pricing a Bermudan swaption with a small number of exercise dates. We begin with the case of two exercise dates. In this limit, we show that the Bermudan price decomposes into the sum of short-dated European swaptions, setting an upper bound, minus a correction term. This correction is expressed as an integral involving a forward volatility agreement type payoff with start at the first exercise date, and it can be evaluated in closed form. The magnitude of the correction is smaller when variance is front loaded and larger when it is back-loaded. We extend to three-exercise Bermudans via backward induction under rolling forward measures. A key feature is boundary linearity enabling further analytic steps. The exercise boundary of options splits into a strike-dependent term and a variance term; together they determine optimal exercise. The linear term is negative, supressing the exponentials in subsequent steps and aiding analytic calculations. This boundary linearity extends to multiple exercise dates and yields pricing formulas with the same decomposition, showing how optionality accumulates across exercise dates. We conclude that the Bermudan can be reconstructed by adding, at each exercise date, the initial short swaption with an increasingly higher strike and subtracting the integrated payoffs of all forward-starting receiver swaptions starting at that date. The corresponding double and higher-order integrals decrease rapidly and, in the presence of only a few exercise dates, can be safely neglected without materially impacting the valuation. The general case is discussed at the end.

I ask whether a factor model that prices the aggregate market also prices the market's internal components. I construct a CRSP investible market portfolio and split it into size-ranked body and tail legs that exactly recombine to the market. All models pass the aggregate market test. Yet q5 leaves systematic, offsetting alphas: negative in the body and positive in the tail. Random splits remove the rejection. The evidence suggests that the market can appear priced because internal pricing errors cancel.

In this paper, we provide a sufficient condition for the absolute continuity of one-dimensional push-forwards of dependent random vectors. Suppose that $X$ has an absolutely continuous distribution and that the conditional distribution of an $\mathbb{R}^d$-valued random vector $Y$ given $X=x$ is nondecreasing in $x\in \mathbb{R}$ in the usual stochastic order. For Borel maps $g\colon \mathbb{R}\times\mathbb{R}^d\to\mathbb{R}$ satisfying a coordinatewise monotonicity condition in $Y$ and a uniform lower-increment condition in $X$, we prove that $g(X,Y)$ has an absolutely continuous distribution. The result requires neither independence nor a joint density, and allows the marginal law of $Y$ to be completely arbitrary. Moreover, the result remains valid if $\mathbb{R}^d$ is replaced by an arbitrary measurable space endowed with a reflexive binary relation. We discuss consequences for monotone risk aggregation and extensions of the familiar regularization by convolution beyond independent random variables.

We introduce the zero-one censored transformed normal (ZOC-TN) model for proportional responses with potential probability mass at the boundaries 0 and 1. The model combines a censored Gaussian variable with a two-parameter affine-logit transformation on the interior (0,1). We characterize the transformation parameters, establish large-sample properties, and relate the affine-logit specification to broader classes of interior distributions. Theoretical and experimental results demonstrate that the proposed model can capture a wider range of qualitative density shapes than several benchmark models while remaining parsimonious, computationally efficient, and numerically stable. Furthermore, the ZOC-TN model can be extended (i) to account for nonlinearities and interactions in a tree-boosting machine learning framework and (ii) to explicitly model residual spatio-temporal variability. We apply the ZOC-TN model to loss given default (LGD) modeling for a large dataset of U.S. residential mortgages and compare it to multiple benchmark models. We find that a tree-boosted ZOC-TN model with a spatio-temporal frailty Gaussian process delivers the strongest out-of-sample performance, indicating that mortgage losses are shaped by nonlinear covariate effects and by unaccounted-for space-time variation.

Simulating financial markets at scale with multi-agent (Agent-Based) models is critical for market design, regulatory stress-testing, and reinforcement learning, but traditional CPU simulators are bottlenecked by sequential processing while vectorized GPU frameworks suffer from kernel-launch overhead and redundant global-memory round-trips. We formalize, analyze, and evaluate a reusable parallel design pattern: persistent, state-carrying clearing for iterative multi-agent reductions. By caching mutable simulation state in thread-block shared memory across step boundaries, aggregating agent actions via shared-memory atomics, and resolving the clearing function cooperatively, the pattern reduces the per-step critical-path depth from Theta(L+A) for sequential clearing (L price-grid ticks, A agents) to Theta(log L + ceil(A/L)) and makes global-memory traffic independent of the step count. We implement this in KineticSim, a lightweight GPU execution engine that simulates massive ensembles of limit-order books in parallel, reaching a peak throughput of over 54.7 billion agent-events per second. On a fixed workload it delivers speedups of 3406x over CPU (NumPy), 27.8x over PyTorch GPU, 42.8x over JAX GPU, and 8.4x over a naive custom CUDA baseline, while using roughly an order of magnitude less GPU memory than PyTorch. Across 53 configurations the two custom CUDA engines produce bitwise-identical order books, and aggregate statistics match the CPU reference to within 0.1%. The pattern generalizes to other iterative multi-agent workloads requiring state-persistent, block-localized reductions.

Empirical economists inherit a toolbox. Shared packages, replication archives, and circulated guides etc. Theorists largely start from a blank page. By 2026, large language models can produce and check nontrivial mathematics, so the binding constraint on machine-assisted theory is no longer production but trust: a fluent model will prove a false theorem as readily as a true one. I propose a verification-first protocol for doing economic theory with a language model and instantiate it as three reusable methods that differ on a single axis, how the work is checked: a single disciplined pass, an adversarial prover-verifier pair (Claude Opus~4.8 proposing, OpenAI Codex refuting, the author triaging), and a structured multi-agent project with a reviewer gate. I evaluate the protocol on one open worked example: designing a Groves/Pigouvian incentive mechanism for the Gans-Kominers eigengrade model of grade inflation; none of the three runs produced a strict direct-revelation VCG/Clarke mechanism, a point the adversarial pass itself established. The evidence is a single worked example with one model pairing run by one operator, so what follows are demonstrations rather than measured effects. Three phenomena recur. First, convergent discovery: two runs derive the same effective-resistance externality kernel on opposite margins. Second, adversarial verification is load-bearing: the pair caught three of its own false claims and the gate rejected a sub-goal. Third, polish is not rigor: the most finished-looking output was the least verified. The methodological takeaway is that external verification, not model capability, is the design variable.

This paper extends the approximate Bayesian estimation framework for Stochastic Volatility in Mean (SVM) models to accommodate heavy-tailed distributions from the Scale Mixture of Normals (SMN) family. To overcome the computational challenges arising from these models, we propose a numerically stable estimation procedure that exploits special functions to eliminate the need for direct numerical integration. Furthermore, the implementation incorporates parallel computing strategies that substantially reduce computational costs. Simulation studies and empirical applications demonstrate that the proposed approach delivers accurate inference while achieving computational times that are approximately an order of magnitude smaller than those required by conventional Markov chain Monte Carlo (MCMC) methods.

Large language models are increasingly deployed as autonomous decision makers, yet the behavioral mapping they exhibit can vary substantially across decision environments that are payoff-equivalent by construction-environments that share identical payoff-relevant structure but differ in surface presentation. This sensitivity renders suite-based evaluation fragile and raises a fundamental question of behavioral portability: how well does a behavioral mapping learned in one decision environment informative on another that preserves the same underlying incentive structure? We introduce a formal framework to measure this property. Our protocol fits an interpretable behavioral model on data pooled from a set of source environments and evaluates its out-of-sample predictive performance in a held-out target environment, benchmarking against an oracle trained directly on target data. Portability is quantified via a loss-agnostic measure that delivers worst-case bounds on the performance of the induced prediction-action mapping in the target environment. In controlled experiments spanning seven canonical economic decision problems, we document substantial and systematic portability losses, suggesting that behavioral characterizations of LLMs obtained in one decision environment cannot be assumed to transfer reliably to structurally equivalent alternatives.

Finance Agent v2 (by Vals AI) has emerged as the reference benchmark for evaluating both Anthropic Claude and OpenAI ChatGPT frontier language models on financial tasks. However, it narrowly deals with periodic reporting from publicly traded companies (SEC 10-K and 10-Q filings), and its agentic harness relies on naive, unenriched chunk retrieval. Neither the task design nor the retrieval approach addresses the distinct challenges of IPO due diligence. SEC S-1 filings combine historical financial statements, governance structures, pro forma and common-control accounting treatments, capital-formation narratives, and underwriting-sensitive risk disclosures within substantially longer documents than typical periodic filings. That is why we introduce IPO Finance Agent, which extends the Finance Agent v2 framework along two directions: task domain and retrieval architecture. During our experiments, the original Finance Agent v2 harness basically failed to deliver any output related to the SpaceX S-1 filing, due to document length. We therefore had to improve the agentic harness with contextual retrieval, a more realistic and industry-standard approach for long documents. We also built a dataset of 1,000 IPO-diligence questions, and publicly release 70 questions on the SpaceX (SPCX) S-1 filing to support reproducibility, while the remainder are held private to guard against benchmark contamination. In addition, we introduce an evaluator-optimizer pipeline to automatically generate evaluation rubrics for the benchmark: candidate facts are extracted from an ensemble of independently-generated model answers to each question, consolidated into draft criteria, then automatically audited for omissions, hallucinations, mistiered items, and redundancy, with LLM feedback driving iterative repair, targeted enrichment, and deduplication. Human experts only review final rubrics before deployment. Results show that the best-performing evaluated model, Alibaba Qwen 3.7 Max, reaches 79.4% accuracy at $0.30 per query, and the most cost-efficient model on the resulting Pareto frontier, Xiaomi MiMo-2.5 Pro, reaches slightly lower accuracy (76.8%) at $0.05 per query. Both exceed the current Finance Agent v2 leaderboard ceiling-Google Gemini 3.5 Flash at 57.9% for $2.51 per querywhile undercutting even FABv2's cheapest entry (MiniMax M3: 48.3% at $0.32) on cost-efficiency. Code and data are released on GitHub: this https URL

We extend the classical theory of affine processes to a path-dependent setting by introducing path-dependent coefficients and provide analytic formulas for their Fourier--Laplace transform in terms of generalized Riccati-type equations. In the proposed framework, we define path-dependent affine processes through their exponential-affine Fourier--Laplace transform on the path space and establish a characterization theorem. Conversely, for path-dependent stochastic differential equations with affine path-dependent coefficients, we also provide explicit exponential-affine representations of the Fourier--Laplace functional in terms of those Riccati equations. Moreover, we derive a condition ensuring non-negativity of the path-dependent diffusion coefficient, guaranteeing well-posedness of the model. Finally, we apply these results to a path-dependent volatility model and a path-dependent extension of the Heston model, including a delayed Heston model as a special case.

Frequency combs are discrete, equally spaced, phase-coherent spectral lines that emerge from nonlinear mode coupling in physical systems. We show that the incommensurate fractional-order financial model of Huang, Li, Ma, and Chen, whose Caputo derivatives encode macroeconomic long-range memory, generates an analogous structure in its steady-state spectrum. The comb appears only over specific values and ranges of the saving amount $a$, the investment cost $b$, and the demand elasticity $c$, outside which the spectral lines lose their equal spacing. It persists across extended parameter regimes and stays invariant to perturbations in the initial interest rate $x_0$ and investment demand $y_0$, while distinct spectral regimes appear at different initial price levels $z_0$. The comb is generated only when the fractional-order exponents $q_1$, $q_2$, and $q_3$ associated with interest rate, investment demand, and price index are above the critical threshold values. At even higher values of these exponents, the frequency comb transitions into chaos. These findings show that the long-run cyclic structure of a memory-bearing financial economy organises into a discrete, deterministic spectral fingerprint rather than a stochastic continuum.

Ethereum's beacon chain hosts over 920,000 active validators, a number inflated by the legacy 32 ETH stake cap. The Pectra upgrade (May 2025) addresses this by introducing 0x02 compounding validators, raising the maximum stake per validator from 32 to 2,048 ETH and enabling automatic reward reinvestment. This paper examines how compounding affects consensus-layer rewards, whether higher balances provide execution-layer advantages, and whether the APR uplift justifies migration for different staker types. We analyse adoption patterns across solo stakers and staking providers, investigate the role of consolidation (merging multiple 32 ETH validators into one) in early migration, and identify barriers slowing the transition. Through simulation, we find that compounding provides roughly +5% relative consensus-layer APR uplift for small balances, diminishing to under 1% for large staking providers. Empirical analysis of all active beacon chain validators shows 0x02 validators achieving modestly higher median CL APR. Solo stakers show higher relative adoption but face operational barriers, whilst providers cite infrastructure costs and protocol constraints. The results suggest that without improved reward accessibility and stronger economic incentives, 0x02 migration will remain gradual despite its network efficiency benefits.

A set of exposure scores calculated in 2023 has become a central empirical input to the future of work debate. Produced by Eloundou et al. (2023) and referred to here as the GPTs are GPTs scores, they define exposure as the share of occupational tasks a large language model can assist with. This work is a genuine methodological contribution, but as the scores travel from the time and place they were produced, the limitations the authors named do not always travel with them. Two gaps have widened as a result. The first is structural, between what static exposure scores measure and what policy questions actually require. Taking the diffusion of these scores as a case study, we show how their temporal, geographic, and ontological limitations compound in policy-facing analyses, and we survey five families of research responding to these limits: dynamic and benchmark-based measures, ensemble methods, task-framework extensions, worker-centered metrics, and adoption and usage data. The second gap is the one we argue needs more attention: the coordination between researchers and policymakers. The policy-relevant work which ask who is harmed, who benefits, how, and when, continues to reference the static GPTs are GPTs scores without engagement with the methodological updates that would let these questions be answered more reliably. We then ask what additional steps towards navigating uncertainty remain: ex-post frameworks and the deliberate, political work of reimagining what futures are worthy of building towards are. Closing the research-policy gap is a shared task: policymakers must widen their evidence base, engage workers as epistemic partners, and shift from prediction to preparedness; researchers must build data infrastructure, adopt participatory methods, and write with policymakers in mind. Better measurement matters, but it will not close the second gap alone.

Connections appear to be helpful in many contexts such as obtaining a job, a promotion, a grant, a loan, or publishing a paper. This may be due to favoritism or to information conveyed by connections. Building on earlier work on discrimination, we propose a new method that identifies these channels using data observed at the time of promotion. The method exploits distinct implications of the two effects on the relationship between observables and success. We show that extra information on connected candidates generates excess variance in latent errors while favors yield different promotion thresholds. We characterize the conditions under which both effects are identified and operationalize these ideas econometrically within a semiparametric framework. We also derive testable restrictions of the model and show how to account for connection endogeneity. We reanalyze data on academic promotions in Spain and Italy and political promotions in China. We detect evidence of favoritism for all types of candidates and of information effects for candidates applying to junior positions. We find strong support for the model's testable restrictions.

This paper investigates well posedness of utility maximization problems for financial markets where stock returns depend on a hidden Gaussian mean-reverting drift process. Since that process is potentially unbounded, well posedness cannot be guaranteed for utility functions which are not bounded from above. For power utility with relative risk aversion smaller than that of log-utility this leads to restrictions on the choice of model parameters such as the investment horizon and parameters controlling the variance of the asset price and drift processes. We derive sufficient conditions to the model parameters leading to bounded maximum expected utility of terminal wealth for models with full and partial information.

Proponents of participatory democracy praise Liquid Democracy: decisions are taken by referendum, but voters delegate their votes freely. When better informed voters are present and the electorate is finite, we show theoretically that delegation can always strictly increase the probability of a correct decision. However, delegation must be used sparingly because it reduces the information aggregated through voting. In two different experiments -- a tightly controlled lab experiment and a perceptual task run online -- we find that subjects choose very high rates of delegation, and the theoretically possible improvements fail to materialize. The experimental evidence favors Direct Democracy, whether with or without abstention. We study the perceptual task, where signals' precisions are not known, both as a test of the robustness of the lab results and as an independent methodological contribution. We argue that tests under ambiguous information are valuable and under-used tools in studying collective decision-making.

Efficiently pricing multi-asset options poses a significant challenge in quantitative finance. Fourier methods leverage the regularity properties of the integrand in the Fourier domain to accurately and rapidly value options that typically lack regularity in the physical domain. However, most of the existing Fourier approaches face hurdles in high-dimensional settings due to the tensor product (TP) structure of the commonly employed numerical quadrature techniques. To overcome this difficulty, this work advocates using the randomized quasi-MC (RQMC) quadrature to improve the scalability of Fourier methods with high dimensions. The RQMC technique benefits from the smoothness of the integrand and alleviates the curse of dimensionality while providing practical error estimates. Nonetheless, the applicability of RQMC on the unbounded domain, $\mathbb{R}^d$, requires a domain transformation to $[0,1]^d$, which may result in singularities of the transformed integrand at the corners of the hypercube, and hence deteriorate the performance of RQMC. To circumvent this difficulty, we design an efficient domain transformation procedure based on boundary growth conditions on the transformed integrand. The proposed transformation preserves sufficient regularity of the original integrand for fast convergence of the RQMC method. To validate our analysis, we demonstrate the efficiency of employing RQMC with an appropriate transformation to evaluate options in the Fourier space for various pricing models, payoffs, and dimensions. Finally, we highlight the computational advantage of applying RQMC over MC or TP in the Fourier domain, and over MC in the physical domain for options with up to 15 assets.

Social media influencers account for a growing share of marketing worldwide. We demonstrate the existence of a novel form of market failure in the advertising market: influencer cartels, where groups of influencers collude to increase their advertising revenue by inflating their engagement. Our theoretical model shows that influencer cartels can improve consumer welfare if they expand social media engagement to the target audience, or reduce welfare if they divert engagement to less relevant audiences. Drawing on the model's insights, we empirically examine influencer cartels using novel datasets and machine learning tools, and derive policy implications.

Response times contain information about economically relevant but unobserved variables like willingness to pay, preference intensity, quality, or happiness. We provide a general characterization of the properties of latent variables that can be detected using response time data. Our theoretical framework unifies and generalizes existing results in the literature and gives rise to many new applications. We illustrate the novel insights that the method can deliver through three empirical applications: identifying an optimal nudge, testing decreasing marginal happiness of income, and predicting treatment heterogeneity.

This paper provides new evidence on spot gig work platforms for individuals seeking flexible, short-term jobs with minimal educational or experience requirements in Japan. Using proprietary data from Timee, a private matching platform, the study analyzes trends in active users, vacancies, hires, and labor market tightness, compared to part-time data from Hello Work, a public employment service. Applying a nonparametric approach, it finds that the private platform exhibits substantially higher matching efficiency, especially after 2022. Elasticities also differ across platforms: for Hello Work, the user elasticity fluctuates around 0.3--0.5, while the vacancy elasticity ranges roughly from 0.4 to slightly above 1.0; for the private platform, the user elasticity remains around 0.2--0.3, while the vacancy elasticity ranges from 0.7 to 1.1. At the prefecture level, the three prefectures exhibit broadly similar movements early in the sample, followed by divergence and partial re-convergence later on, while elasticities remain stable and similar across regions. These results reveal how digital platforms reshape job matching dynamics relative to traditional systems.

Startup accelerators are a leading gateway to venture capital, but top programs often require founders to relocate to a venture hub. From a hand-collected census of U.S. accelerator startups (2008-2011) followed for five years, we estimate a two-sided matching model that separates two channels behind the gender funding gap, geographic mobility and sorting across accelerator tiers. Women raise about 60% less than men over five years; the gap concentrates among non-relocating women, is largest at active-childrearing ages, and vanishes for relocators, while the mobility cost is near zero for men. Removing mobility frictions raises women's match quality but not their tier; reaching the high-funding top tier also requires removing the sorting disadvantage that women face. The 2012 JOBS Act eased the legal barrier and capacity grew tenfold, yet the U.S. VC dollar gap still tripled (2011-2020): closing it needs mobility, sorting, and capacity together.

This paper proposes an innovative Transformer model, Single-directional representative from Transformer (SERT), for US large capital stock pricing. It also innovatively applies the pre-trained Transformer models under the stock pricing and factor investment context. They are compared with standard Transformer models and encoder-only Transformer models in three periods covering the entire COVID-19 pandemic to examine the model adaptivity and suitability during the extreme market fluctuations. Namely, pre-COVID-19 period, COVID-19 period and 1-year post-COVID-19. The best proposed SERT model achieves the highest out-of-sample $R^2$, 11.94\% and 11.47\% respectively, when extreme market fluctuation takes place, followed by pre-trained Transformer models (11.13\% and 9.72\%). Their Trend-following-based strategy's performance also proves their excellent capability for hedging downside risks during market shocks. The proposed SERT model achieves a Sortino ratio 47\% higher than the buy-and-hold benchmark in the equal-weighted portfolio and 28\% higher in the value-weighted portfolio in the static transaction cost scenario when the pandemic period is considered. It proves that Transformer models have a strong ability to capture patterns of temporal sparsity in asset pricing factor models, especially with high volatility. I also find the softmax signal filter as the common configuration of Transformer models in alternative contexts, which only eliminates differences between models, but does not improve strategy-wise performance, while increasing attention heads improves the model performance insignificantly and applying the 'layer normalization first' method does not boost the model performance in our case.

We amend and extend the Chiarella model of financial markets to deal with arbitrary long-term value drifts in a consistent way. This allows us to improve upon existing calibration schemes, opening the possibility of calibrating individual monthly time series instead of classes of time series. The technique is employed on spot prices of four asset classes from ca. 1800 onward (stock indices, bonds, commodities, currencies). The so-called fundamental value is a direct output of the calibration, which allows us to (a) quantify the amount of excess volatility in these markets, which we find to be large (e.g. a factor $\approx$ 4 for stock indices) and consistent with previous estimates; and (b) determine the distribution of mispricings (i.e. the difference between market price and value), which we find in many cases to be bimodal. Both findings are strongly at odds with the Efficient Market Hypothesis. We also study in detail the 'sloppiness' of the calibration, that is, the directions in parameter space that are weakly constrained by data. The main conclusions of our study are remarkably consistent across different asset classes, and reinforce the hypothesis that the medium-term fate of financial markets is determined by a tug-of-war between trend followers and fundamentalists.

In this paper we study the quality of model-free valuation approaches for financial derivatives by systematically evaluating the difference between model-free super-hedging strategies and the realized payoff of financial derivatives using historical option prices from several constituents of the S&P 500 between 2018 and 2022. Our study allows in particular to describe the realized gap between payoff and model-free hedging strategy empirically so that we can quantify to which degree model-free approaches are overly conservative. Our results imply that the model-free hedging approach is only marginally more conservative than industry-standard models such as the Heston-model while being model-free at the same time. This finding, its statistical description and the model-independence of the hedging approach enable us to construct an explicit trading strategy which, as we demonstrate, can be profitably applied in financial markets, and additionally possesses the desirable feature with an explicit control of its downside risk due to its model-free construction preventing losses pathwise.

This paper examines how natural gas supply shocks affect Italian firms' pricing decisions and inflation expectations using quarterly survey data from the Bank of Italy's Survey on Inflation and Growth Expectations (SIGE). We identify natural gas supply shocks through an external IV-VAR approach exploiting likely unexpected news about interruption to gas supplies to Europe. Our findings show that although gas supply shocks do not have huge effects on gas quantity and only modest effect on gas inventories, they are quickly transmitted to spot electricity prices with persistent effects. We then estimate a proxy internalizing BVAR incorporating firm-level variables from SIGE, documenting that gas supply shocks raise firms' current and expected prices as well as inflation uncertainty. Finally, we uncover substantial nonlinearities using state-dependent local projections: under high inflation uncertainty, firms successfully pass cost increases on to consumers, sustaining elevated prices; under low uncertainty, recessionary effects dominate, leading firms to cut prices below baseline.

What was the origin of modern economic growth? Joel Mokyr has argued that self-sustained modern economic growth originated from a feedback loop between propositional (theoretical) and prescriptive (applied) knowledge, which turned positive in the eighteenth century during the "Industrial Enlightenment". While influential, this thesis has never been directly tested. This paper provides the first quantitative evidence by estimating the impact of knowledge spillovers between propositional and prescriptive knowledge on innovation in England, 1600-1800. For this, it introduces two new text-based measures for 1) the innovativeness of publications and 2) knowledge spillovers. The paper finds strong evidence that a feedback loop between propositional and prescriptive knowledge became positive during the second half of the eighteenth century. It also documents that this process had positive effects on the real economy as measured through patents. Overall, the findings provide empirical support for Mokyr's original hypothesis.

This version corrects and supersedes an earlier preprint (arXiv:2601.12541) whose central impossibility theorem was incorrect; the nature of the error and its correction are stated explicitly in Section 1.1. We retain the parts that are valid - the local reduction of pricing to the natural price filtration and its stability properties - and we replace the erroneous global non-existence claim with the statement that is actually true. Two facts are established. First, when the information structure is treated as an admissible (immersion-preserving) enlargement, local martingale pricing reduces to the natural price filtration, and this reduction is stable under restriction and under aggregation when a common pricing measure exists. Second, non-anticipativity of information does not aggregate: there exist signals, each individually and pairwise non-anticipative with respect to the reference Brownian filtration, whose joint observation reveals a function of a future increment; the failure first occurs at order three and is invisible to every lower-order test. We show that this aggregation failure requires dependence among the signals (a masking relation), not independence, and that it is an obstruction at the level of information admissibility (immersion), not at the level of no-arbitrage: the enlarged market continues to admit a local martingale deflator. We situate the results relative to the filtration-reduction program of Grigorian and Jarrow and relate the admissibility notion to predictive (Granger) and interventionist (Pearl) causality.

We price European options in a class of models in which the volatility of the underlying risky asset depends on the short rate of interest. Our study results in an explicit pricing formula that is expressed in terms of a characteristic function. We provide examples of models in which the characteristic function can be computed analytically and, thus, the value of European options is explicit. Numerical implementation to produce the implied volatility is also presented.

This paper develops a dynamic equilibrium model of the insurance market that jointly characterizes insurers' underwriting, investment, recapitalization, and dividend policies under model uncertainty and financial frictions. Competitive insurers maximize shareholder value under a subjective worst-case probability measure, giving rise to liquidity-driven underwriting cycles and flight-to-quality behavior. Model uncertainty acts as an informational friction on insurers' risk-taking behavior and helps regularize a finite-barrier verification system in settings with external financial investment opportunities. We further show that robustness concerns do not eliminate the investment-hedging channel in insurance pricing: when underwriting surplus and financial returns are sufficiently negatively correlated, the hedging value of financial investment can be passed through to policyholders, leading to lower insurance prices and, in high-capacity states, negative equilibrium loadings. Thus, underwriting losses may arise endogenously even when insurers price rationally under model uncertainty, rather than necessarily reflecting mispricing or irrational underwriting behavior.

Generating stochastic trajectories for asset classes is an increasingly relevant task in quantitative finance. Traditional approaches, such as the stationary bootstrap, preserve by construction the empirical distribution of asset-class returns, but do not ensure that each individual simulated path is economically realistic: scenarios may be valid in distribution while single trajectories fail to represent plausible states of the world. To address this limitation, we review semiparametric simulation methodologies that combine a parametric structure, which enforces realistic dynamics, with the resampling of model residuals, which preserves the stochastic component observed in historical data. The issue is particularly acute for interest rates, where direct resampling of rate changes may produce implausible yield-curve evolutions despite correct distributional properties. Our empirical analysis shows the effectiveness of semiparametric bootstrap methods based on autoregressive or mean-reverting specifications. In the fixed-income setting, combining these methods with fully parametric term-structure models yields more coherent and realistic simulations of yield-curve dynamics.

Who is exposed to generative AI in a developing-country labour market? We map three occupational AI-exposure indices to India's redesigned Periodic Labour Force Survey (2025) and document a steep caste gradient among 83,000 employed graduates: graduates from the Scheduled Castes and the Scheduled Tribes are 0.24--0.37 standard deviations less exposed than upper-caste graduates within the same district. Two channels drive the gap: one in four SC and one in three ST graduates work in farm or elementary occupations untouched by AI, and those in white-collar work are underrepresented in managerial, software, and finance occupations. Because exposure commands a wage premium of up to 20 per cent, generative AI stands to widen, not narrow, India's caste earnings gap.

Starting with a coupled discrete reaction--diffusion formulation for the lit and latent order books with non-uniformly sampled event times and meta-order source terms we show how two familiar market-microstructure regularities can emerge from this framework: the long-memory of trade signs associated with the Lillo--Mike--Farmer (LMF) theory and the square-root law (SQRL) of meta-order impact. This uses the locally linear order book and constant participation-rate execution in the front dynamics to reduce the dynamics to a Volterra equation whose leading-order solution then yields the well know result of concave impact trajectory, and a completion impact proportional to the square root of the meta-order size. We then use the interface representation to show how heavy-tailed Pareto meta-order lengths generate power-law trade-sign autocorrelations through the source term. These are familiar derivations, what is slightly different here is that we reinterpret these known derivations to make it clear that LMF law is an event-time sign-memory statement, whereas the square-root law is a physical-time viability statement where subordination can alter the calendar-time impact trajectories depending on the mappings and interpolation used to set continuum operational time.

Real-time market prediction services need correct predictions before a decision deadline; a correct prediction delivered late is not a usable service output. TIP-Search studies time-predictable inference scheduling over fixed market predictors under uncertain load. It filters conformal latency-quantile feasible models, dispatches over finite workers, and uses shielded constrained online experts to trade accuracy, queue pressure, and deadline risk. The official systems-replay controller is OCO-ACPO, a projected-dual shielded expert selector; SA-OCO-ACPO is a nonstationary stress extension that records interval stress, regret proxies, and constraint-violation proxies while preserving the CPO safety shield. On the optimized deployable pool, TIP-Search reaches 0.994 raw accuracy and 0.991 timely accuracy. On official TLOB FI-2010 h=10, TIP-Search++ raises timely accuracy from 0.156 to 0.239 and deadline satisfaction from 0.391 to 0.962. In the matched h10 profiled systems replay, OCO-ACPO reaches 0.303 timely accuracy and 0.951 deadline satisfaction, with paired condition gains over RAMSIS/SneakPeek/utility-style comparators of $+0.00285$ timely accuracy ($p=0.0118$) and $+0.0146$ deadline satisfaction ($p=1.5{\times}10^{-5}$). SA-OCO-ACPO improves timely/deadline service by 0.188--0.417 over CPO under nonstationary stress. The claim is a systems scheduling result, not a broad LOB classifier leaderboard.

This study develops an interpretable machine learning framework to forecast startup outcomes, including funding, patenting, and exit. A firm-quarter panel for 2010-2023 is constructed from Crunchbase and matched to U.S. Patent and Trademark Office (USPTO) data. Three horizons are evaluated: next funding within 12 months, patent-stock growth within 24 months, and exit through an initial public offering (IPO) or acquisition within 36 months. Preprocessing is fit on a development window (2010-2019) and applied without change to later cohorts to avoid leakage. Class imbalance is addressed using inverse-prevalence weights and the Synthetic Minority Oversampling Technique for Nominal and Continuous features (SMOTE-NC). Logistic regression and tree ensembles, including Random Forest, XGBoost, LightGBM, and CatBoost, are compared using the area under the precision-recall curve (PR-AUC) and the area under the receiver operating characteristic curve (AUROC). Patent, funding, and exit predictions achieve AUROC values of 0.921, 0.817, and 0.872, providing transparent and reproducible rankings for innovation finance.

Global food security depends on tightly coupled international supply chains encompassing natural gas, mineral fertilizers, and staple crops. Earlier research has examined the potential consequences of disruptions in each of these domains separately, but not from a systemic perspective. Here we integrate bilateral trade in natural gas, nitrogen, phosphorus, and potassium fertilizers, and eleven staple crops -- accounting for approximately 70% of plant-based calories -- into a cascading-impact model spanning 208 countries, 20 geopolitical blocs, and the period 1992--2023. Under complete trade isolation, up to 22% of global caloric consumption would be lost, with a peak in the most recently evaluated years. Structural vulnerabilities vary considerably. Regions largely lacking some segments of the supply chain face near-total crop supply collapse, while few countries can cover the entire nexus through domestic resource endowments and production capacities. Temporal trends highlight a substantial increase in vulnerability globally, most prominently in the EU, with a near two-fold increase since the 1990s. Market power is most concentrated and most volatile in the upstream gas layer and has risen in the fertilizer layers since the 2000s; shocks propagate downstream from these tightening upstream layers, driving the system's fragility. Food stocks provide only limited resilience, with half of humanity living in countries holding stocks lasting fewer than three months. Our results identify upstream supply chains as the structural bottlenecks of the global agrifood system and propose leverage points to enhance resilience.