This paper studies how labor market conditions around high school graduation affect postsecondary skill investments. Using administrative data on more than six million German graduates from 1995-2018, and exploiting deviations from secular state-specific trends, I document procyclical college enrollment. Cyclical increases in unemployment reduce enrollment at traditional universities and shift graduates toward vocational colleges and apprenticeships. These effects translate into educational attainment. Using large-scale survey data, I identify changes in expected returns to different degrees as the main mechanism. During recessions, graduates expect lower returns to an academic degree, while expected returns to a vocational degree are stable.

This paper develops a theory of scientific and technological peer effects to study how individuals' productivity responds to the behavior and network positions of their collaborators across both scientific and inventive activities. Building on a simultaneous equation network framework, the model predicts that productivity in each activity increases in a variation of the Katz-Bonacich centrality that captures within-activity and cross-activity strategic complementarities. To test these predictions, we assemble the universe of cancer-related publications and patents and construct coauthorship and coinventorship networks that jointly map the collaboration structure of researchers active in both spheres. Using an instrumental-variables approach based on predicted link formation from exogenous dyadic characteristics, and incorporating community fixed effects to address endogenous network formation, we show that both authors' and inventors' outputs rise with their network centrality, consistent with the theory. Moreover, scientific productivity significantly enhances technological productivity, while technological output does not exert a detectable reciprocal effect on scientific production, highlighting an asymmetric linkage aligned with a science-driven model of innovation. These findings provide the first empirical evidence on the joint dynamics of scientific and inventive peer effects, underscore the micro-foundations of the co-evolution of science and technology, and reveal how collaboration structures can be leveraged to design policies that enhance collective knowledge creation and downstream innovation.

Rapidly evolving market conditions call for real-time risk monitoring, but its online estimation remains challenging. In this paper, we study the online estimation of one of the most widely used risk measures, Value at Risk (VaR). Its accurate and reliable estimation is essential for timely risk control and informed decision-making. We propose to use the quantile regression forest in the offline-simulation-online-estimation (OSOA) framework. Specifically, the quantile regression forest is trained offline to learn the relationship between the online VaR and risk factors, and real-time VaR estimates are then produced online by incorporating observed risk factors. To further ensure reliability, we develop a conformalized estimator that calibrates the online VaR estimates. To the best of our knowledge, we are the first to leverage conformal calibration to estimate real-time VaR reliably based on the OSOA formulation. Theoretical analysis establishes the consistency and coverage validity of the proposed estimators. Numerical experiments confirm the proposed method and demonstrate its effectiveness in practice.

Can artificial intelligence outperform humans at strategic foresight -- the capacity to form accurate judgments about uncertain, high-stakes outcomes before they unfold? We address this question through a fully prospective prediction tournament using live Kickstarter crowdfunding projects. Thirty U.S.-based technology ventures, launched after the training cutoffs of all models studied, were evaluated while fundraising remained in progress and outcomes were unknown. A diverse suite of frontier and open-weight large language models (LLMs) completed 870 pairwise comparisons, producing complete rankings of predicted fundraising success. We benchmarked these forecasts against 346 experienced managers recruited via Prolific and three MBA-trained investors working under monitored conditions. The results are striking: human evaluators achieved rank correlations with actual outcomes between 0.04 and 0.45, while several frontier LLMs exceeded 0.60, with the best (Gemini 2.5 Pro) reaching 0.74 -- correctly ordering nearly four of every five venture pairs. These differences persist across multiple performance metrics and robustness checks. Neither wisdom-of-the-crowd ensembles nor human-AI hybrid teams outperformed the best standalone model.

Do online narratives leave a measurable imprint on prices in markets for digital or cultural goods? This paper evaluates how community attention and sentiment relate to valuation in major Ethereum NFT collections after accounting for time effects, market-wide conditions, and persistent visual heterogeneity. Transaction data for large generative collections are merged with Reddit-based discourse measures available for 25 collections, covering 87{,}696 secondary-market sales from January 2021 through March 2025. Visual differences are absorbed by a transparent, within-collection standardized index built from explicit image traits and aggregated via PCA. Discourse is summarized at the collection-by-bin level using discussion intensity and lexicon-based tone measures, with smoothing to reduce noise when text volume is sparse. A mixed-effects specification with a Mundlak within--between decomposition separates persistent cross-collection differences from within-collection fluctuations. Valuations align most strongly with sustained collection-level attention and sentiment environments; within collections, short-horizon negativity is consistently associated with higher prices, and attention is most informative when measured as cumulative engagement over multiple prior windows.

We propose a microstructural model for the order flow in financial markets that distinguishes between {\it core orders} and {\it reaction flow}, both modeled as Hawkes processes. This model has a natural scaling limit that reconciles a number of salient empirical properties: persistent signed order flow, rough trading volume and volatility, and power-law market impact. In our framework, all these quantities are pinned down by a single statistic $H_0$, which measures the persistence of the core flow. Specifically, the signed flow converges to the sum of a fractional process with Hurst index $H_0$ and a martingale, while the limiting traded volume is a rough process with Hurst index $H_0-1/2$. No-arbitrage constraints imply that volatility is rough, with Hurst parameter $2H_0-3/2$, and that the price impact of trades follows a power law with exponent $2-2H_0$. The analysis of signed order flow data yields an estimate $H_0 \approx 3/4$. This is not only consistent with the square-root law of market impact, but also turns out to match estimates for the roughness of traded volumes and volatilities remarkably well.

How a shock to an individual sector propagates to the prices of other sectors and aggregates to GDP depends on how easily sectoral goods can be substituted in production, which is determined by the intermediate input substitution elasticity. Past estimates of this parameter in the US have been restrictive: they have assumed a common elasticity across industries, and have ignored the use of imports in production. This paper uses a novel empirical strategy to produce new estimates without these restrictions, by exploiting variation in import ratios and input expenditure shares from the BEA Input-Output Accounts. I find that sectors differ meaningfully in their ability to substitute inputs in production, and that the uniform estimate of the intermediate input substitution elasticity is biased downwards relative to the median sector-specific estimate. Relative to imposing the uniform elasticity, sector-specific substitution causes domestic prices to rise more in response to oil import shocks and less in response to semiconductor import shocks. It also implies the average GDP response to a sectoral business cycle is 0.35% higher, making sectoral business cycles 17.7% less costly.

Reliable estimates of volatility and correlation are fundamental in economics and finance for understanding the impact of macroeconomics events on the market and guiding future investments and policies. Dependence across financial returns is likely to be subject to sudden structural changes, especially in correspondence with major global events, such as the COVID-19 pandemic. In this work, we are interested in capturing abrupt changes over time in the dependence across US industry stock portfolios, over a time horizon that covers the COVID-19 pandemic. The selected stocks give a comprehensive picture of the US stock market. To this end, we develop a Bayesian multivariate stochastic volatility model based on a time-varying sequence of graphs capturing the evolution of the dependence structure. The model builds on the Gaussian graphical models and the random change points literature. In particular, we treat the number, the position of change points, and the graphs as object of posterior inference, allowing for sparsity in graph recovery and change point detection. The high dimension of the parameter space poses complex computational challenges. However, the model admits a hidden Markov model formulation. This leads to the development of an efficient computational strategy, based on a combination of sequential Monte-Carlo and Markov chain Monte-Carlo techniques. Model and computational development are widely applicable, beyond the scope of the application of interest in this work.

We consider a novel use case for the Double Heston model (Christoffersen et al,, 2009), where the two Heston sub-variances have different spot/volatility correlations but the same volatility of volatility and mean reversion speed. This parameterization generalizes the traditional Heston stochastic volatility model (Heston, 1993) to include stochastic spot/volatility correlation. It is an affine model, allowing European options to be priced efficiently by numerically integrating over a closed-form characteristic function. This model incorporates a key dynamic relevant for pricing barrier derivatives in the foreign exchange markets: a positive correlation between moves in implied volatility skew and moves in the spot price. We analyze that correlation and its impact on both barrier option pricing and volatility swap pricing. Those price impacts are comparable to or larger than the bid/ask spreads for these products. Adding stochastic spot/volatility correlation increases the prices of out-of-the-money knockout options and one touch options, assuming that the model is calibrated to market vanilla option prices. It also increases the fair strike of volatility swaps compared to the Heston model.

In this paper, we present a methodology for measuring the impact of scenarios on the expected losses of exposures by leveraging the existing provisioning infrastructure within financial institutions, where scenario effects are captured through changes in probabilities of default. We then describe how to design and implement a scenario test where risk drivers are given for standardized groupings of exposures, and the groupings are defined based on common features of the exposures. The methodology presented served as a theoretical foundation for the standardized climate scenario exercise conducted in 2024 by the Office of the Superintendent of Financial Institutions of Canada and Quebec's Autorite des Marches Financiers.

Benoit Mandelbrot's scientific legacy spans an extraordinary range of disciplines, from linguistics and fluid turbulence to cosmology and finance, suggesting the intellectual temperament of a "fox" in Isaiah Berlin's famous dichotomy of thinkers. This essay argues, however, that Mandelbrot was, at heart, a "hedgehog": a thinker unified by a single guiding principle. Across his diverse pursuits, the concept of scaling -- manifested in self-similarity, power laws, fractals, and multifractals -- served as the central idea that structured his work. By tracing the continuity of this scaling paradigm through his contributions to mathematics, physics, and economics, the paper reveals a coherent intellectual trajectory masked by apparent eclecticism. Mandelbrot's enduring insight in the modeling of natural and social phenomena can be understood through the lens of the geometry and statistics of scale invariance.

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 depends on knowledge 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.

We document stable cross-asset patterns in cryptocurrency limit-order-book microstructure: the same engineered order book and trade features exhibit remarkably similar predictive importance and SHAP dependence shapes across assets spanning an order of magnitude in market capitalization (BTC, LTC, ETC, ENJ, ROSE). The data covers Binance Futures perpetual contract order books and trades on 1-second frequency starting from January 1st, 2022 up to October 12th, 2025. Using a unified CatBoost modeling pipeline with a direction-aware GMADL objective and time-series cross validation, we show that feature rankings and partial effects are stable across assets despite heterogeneous liquidity and volatility. We connect these SHAP structures to microstructure theory (order flow imbalance, spread, and adverse selection) and validate tradability via a conservative top-of-book taker backtest as well as fixed depth maker backtest. Our primary novelty is a robustness analysis of a major flash crash, where the divergent performance of our taker and maker strategies empirically validates classic microstructure theories of adverse selection and highlights the systemic risks of algorithmic trading. Our results suggest a portable microstructure representation of short-horizon returns and motivate universal feature libraries for crypto markets.

This study examines the effects of Trump-era tariffs on financial market efficiency by applying multifractal detrended fluctuation analysis to the return and absolute return time series of six major financial assets: the S\&P 500, SSEC, VIX, BTC/USD, EUR/USD, and Gold. Using the Hurst exponent $h(2)$ and multifractal strength, we assess how market dynamics responded to two major global shocks: the COVID-19 pandemic and the implementation of the Trump tariff policy in 2025. The results show that COVID-19 induced substantial changes in both the Hurst exponent and multifractal strength, particularly for the S\&P 500, BTC/USD, EUR/USD, and Gold. In contrast, the effects of the Trump tariffs were more moderate but still observable across all examined time series. The Chinese market index (SSEC) remained largely unaffected by either event, apart from a distinct response to domestic stimulus measures. In addition, the VIX exhibited anti-persistent behavior with $h(2) < 0.5$, consistent with the rough volatility framework. These findings underscore the usefulness of multifractal analysis in capturing structural shifts in market efficiency under geopolitical and systemic shocks.

We investigate the ordering between two fundamental measures of dispersion for real-valued risks: the standard deviation (SD) and the Gini mean difference (GMD). Our analysis is driven by a single structural object, namely the mean excess function of the pairwise difference $|X - X'|$. We show that its monotonicity is determined by the tail behavior of the underlying distribution, giving rise to two distinct dispersion regimes. In a heavy-tailed regime, characterized by decreasing hazard rates or increasing reverse hazard rates, the SD dominates the GMD. Conversely, when both tails of the distribution are light, the GMD dominates the SD. These dominance regimes are shown to be stable under truncation, convolution, and mixtures. Discrete analogues of the main results are also developed. Overall, the results provide an intuitive interpretation of the dispersion ordering phenomena that goes beyond the existing general comparisons, with direct relevance for risk modeling and actuarial applications.

We introduce heterogeneous R&D productivities into an endogenous R&D network formation model, generalizing the framework of Goyal and Moraga-González (2001). Heterogeneous productivities endogenously create asymmetric gains from collaboration: less productive firms benefit disproportionately from links, while more productive firms exert greater R&D effort and incur higher costs. When productivity gaps are sufficiently large, more productive firms experience lower profits from collaborating with less productive partners. As a result, the complete network -- stable under homogeneity -- becomes unstable, and the positive assortative (PA) network, in which firms cluster by R&D productivity, emerges as pairwise stable. Using simulations, we show that the clustered structure delivers higher welfare than the complete network; nevertheless, welfare under this formation follows an inverted U-shape as the fraction of high-productivity firms increases, reflecting crowding-out effects at high fractions. Altogether, we uncover an R&D productivity puzzle: economies with higher average R&D productivity may exhibit lower welfare through (i) the formation of alternative stable networks, or (ii) a crowding-out effect of high-productivity firms. Our findings show that productivity gaps shape the organization of innovation by altering equilibrium R&D alliances and effort. Productivity-enhancing policies must therefore account for these endogenous responses, as they may reverse intended welfare gains.

The Bank of Korea (BoK) regularly publishes the Economic Outlook, offering forecasts for key macroeconomic variables such as GDP growth, inflation, and unemployment rates. This study examines whether the BoK's inflation forecasts exhibit bias, specifically a tendency to align with its inflation target. We extend the Holden and Peel (1990) test to incorporate state-dependency, defining the state of the economy based on whether realized inflation falls below the target at the time of the forecast. Our analysis reveals that the BoK's inflation forecasts are biased under this state-dependent framework. Furthermore, we examine a range of bias correction strategies based on AR(1) and mean error models, including their state-dependent variants. These strategies generally improve forecast accuracy. Among them, the AR(1)-based correction exhibits relatively stable performance, consistently reducing the root mean square error.

Time series forecasting is important in finance domain. Financial time series (TS) patterns are influenced by both short-term public opinions and medium-/long-term policy and market trends. Hence, processing multi-period inputs becomes crucial for accurate financial time series forecasting (TSF). However, current TSF models either use only single-period input, or lack customized designs for addressing multi-period characteristics. In this paper, we propose a Multi-period Learning Framework (MLF) to enhance financial TSF performance. MLF considers both TSF's accuracy and efficiency requirements. Specifically, we design three new modules to better integrate the multi-period inputs for improving accuracy: (i) Inter-period Redundancy Filtering (IRF), that removes the information redundancy between periods for accurate self-attention modeling, (ii) Learnable Weighted-average Integration (LWI), that effectively integrates multi-period forecasts, (iii) Multi-period self-Adaptive Patching (MAP), that mitigates the bias towards certain periods by setting the same number of patches across all periods. Furthermore, we propose a Patch Squeeze module to reduce the number of patches in self-attention modeling for maximized efficiency. MLF incorporates multiple inputs with varying lengths (periods) to achieve better accuracy and reduces the costs of selecting input lengths during training. The codes and datasets are available at https://github.com/Meteor-Stars/MLF.

Deep Learning is evolving fast and integrates into various domains. Finance is a challenging field for deep learning, especially in the case of interpretable artificial intelligence (AI). Although classical approaches perform very well with natural language processing, computer vision, and forecasting, they are not perfect for the financial world, in which specialists use different metrics to evaluate model performance. We first introduce financially grounded loss functions derived from key quantitative finance metrics, including the Sharpe ratio, Profit-and-Loss (PnL), and Maximum Draw down. Additionally, we propose turnover regularization, a method that inherently constrains the turnover of generated positions within predefined limits. Our findings demonstrate that the proposed loss functions, in conjunction with turnover regularization, outperform the traditional mean squared error loss for return prediction tasks when evaluated using algorithmic trading metrics. The study shows that financially grounded metrics enhance predictive performance in trading strategies and portfolio optimization.

We study staged entry with costly gatekeeping in a differentiated-products economy: entrepreneurs observe noisy signals before paying a resource-intensive activation cost. Precision improves selection but requires more resources, reducing entry and variety: welfare need not rise with precision. Under CES preferences, the activation cutoff is efficient as profit displacement offsets the consumer-surplus gain from variety. Welfare losses arise from verification costs shrinking the feasible set of varieties, not from misaligned incentives. Because the market responds efficiently to any given regime, these losses cannot be corrected via Pigouvian taxes.

We construct a deep learning-based numerical algorithm to solve path-dependent partial differential equations arising in the context of rough volatility. Our approach is based on interpreting the PDE as a solution to an BSDE, building upon recent insights by Bayer, Qiu and Yao, and on constructing a neural network of reservoir type as originally developed by Gonon, Grigoryeva, Ortega. The reservoir approach allows us to formulate the optimisation problem as a simple least-square regression for which we prove theoretical convergence properties.

Financial markets exhibit temporal organization that is not fully captured by volatility measures or linear correlation structure. We study a null validated topological approach for quantifying market complexity and apply it to Bitcoin daily log returns. The analysis uses the $L^1$ norm of persistence landscapes computed from sliding-window delay embeddings. This quantity shows strong co-movement with stochastic volatility during periods of market stress, but remains intermittently elevated during low volatility regimes, indicating dynamical structure beyond fluctuation scale. Rolling correlation analysis reveals that the dependence between geometry and volatility is not stationary. Surrogate based null models provide statistical validation of these observations. Rejection of shuffle surrogates rules out explanations based on marginal distributions alone, while departures from phase randomized surrogates indicate sensitivity to nonlinear and phase dependent temporal organization beyond linear correlations. These results demonstrate that persistence landscape norms provide complementary information about market dynamics across market conditions.

This paper implements an efficient numerical algorithm for the time-fractional Black-Scholes model governing European options. The proposed method comprises the Crank-Nicolson approach to discretize the time variable and exponential B-spline approximation for the space variable. The implemented method is unconditionally stable. We present few numerical examples to confirm the theory. Numerical simulations with comparisons exhibit the supremacy of the proposed approach.

We study whether generative AI can automate feature discovery in U.S. equities. Using large language models with retrieval-augmented generation and structured/programmatic prompting, we synthesize economically motivated features from analyst, options, and price-volume data. These features are then used as inputs to a tabular machine-learning model to forecast short-horizon returns. Across multiple datasets, AI-generated features are consistently competitive with baselines, with Sharpe improvements ranging from 14% to 91% depending on dataset and configuration. Retrieval quality is pivotal: better knowledge bases materially improve outcomes. The AI-generated signals are weakly correlated with traditional features, supporting combination. Overall, generative AI can meaningfully augment feature discovery when retrieval quality is controlled, producing interpretable signals while reducing manual engineering effort.

Generative AI has the potential to transform how firms produce output. Yet, credible evidence on how AI is actually substituting for human labor remains limited. In this paper, we study firm-level substitution between contracted online labor and generative AI using payments data from a large U.S. expense management platform. We track quarterly spending from Q3 2021 to Q3 2025 on online labor marketplaces (such as Upwork and Fiverr) and leading AI model providers. To identify causal effects, we exploit the October 2022 release of ChatGPT as a common adoption shock and estimate a difference-in-differences model. We provide a novel measure of exposure based on the share of spending at online labor marketplaces prior to the shock. Firms with greater exposure to online labor adopt AI earlier and more intensively following the shock, while simultaneously reducing spending on contracted labor. By Q3 2025, firms in the highest exposure quartile increase their share of spending on AI model providers by 0.8 percentage points relative to the lowest exposure quartile, alongside significant declines in labor marketplace spending. Combining these responses yields a direct estimate of substitution: among the most exposed firms, a \$1 decline in online labor spending is associated with approximately \$0.03 of additional AI spending, implying order-of-magnitude cost savings from replacing outsourced tasks with AI services. These effects are heterogeneous across firms and emerge gradually over time. Taken together, our results provide the first direct, micro-level evidence that generative AI is being used as a partial substitute for human labor in production.

This paper examines whether a major U.S. regulatory clarification coincided with cross-border spillovers in crypto-asset entrepreneurial finance. We study the Securities and Exchange Commission's July 2017 DAO Report, which clarified the application of U.S. securities law to many initial coin offerings, and analyze how global issuance activity adjusted across regions. Using a comprehensive global dataset of ICOs from 2014 to 2021, we construct a region-month panel and evaluate issuance dynamics around the announcement. We document a substantial and persistent reallocation of ICO activity toward Europe following the DAO Report. In panel regressions with region and month fixed effects, Europe experiences an average post-2017 increase of approximately 14 additional ICOs per region-month relative to other regions, net of global market cycles. The results are consistent with cross-border regulatory spillovers in highly mobile digital-asset markets.

Prediction markets offer a natural testbed for trading agents: contracts have binary payoffs, prices can be interpreted as probabilities, and realized performance depends critically on market microstructure, fees, and settlement risk. We introduce PredictionMarketBench, a SWE-bench-style benchmark for evaluating algorithmic and LLM-based trading agents on prediction markets via deterministic, event-driven replay of historical limit-order-book and trade data. PredictionMarketBench standardizes (i) episode construction from raw exchange streams (orderbooks, trades, lifecycle, settlement), (ii) an execution-realistic simulator with maker/taker semantics and fee modeling, and (iii) a tool-based agent interface that supports both classical strategies and tool-calling LLM agents with reproducible trajectories. We release four Kalshi-based episodes spanning cryptocurrency, weather, and sports. Baseline results show that naive trading agents can underperform due to transaction costs and settlement losses, while fee-aware algorithmic strategies remain competitive in volatile episodes.

Pricing advanced data products - particularly in complex fields such as semiconductor manufacturing - is a fundamentally challenging task due to the sparsity of publicly available transaction data, and its frequent heterogeneity and confidentiality. While data value depends on multiple interacting factors, such as technical sophistication, quality, utility, and licensing rights, traditional pricing methods tend to rely on ad-hoc heuristics or require massive amounts of historical transaction data. In an increasingly data-based economy, we introduce a prior-predictive Monte Carlo framework that enables the generation of fair, consistent, and justified price ranges for data products in the absence of empirical data. By simulating many plausible pricing 'worlds' and deal configurations, the framework produces stable probabilistic price bands (e.g., P5/P50/P95) rather than single point estimates, creating an auditable and repeatable probabilistic pricing system with business realism enforced via constraint-truncated priors. The proposed model bridges traditional data pricing rooted in professional experience with a data-based approach that also allows for classical Bayesian updating as more transaction data is accumulated.

Decentralized Finance (DeFi) has revolutionized financial markets by enabling complex asset-exchange protocols without trusted intermediaries. Automated Market Makers (AMMs) are a central component of DeFi, providing the core functionality of swapping assets of different types at algorithmically computed exchange rates. Several mainstream AMM implementations are based on the constant-product model, which ensures that swaps preserve the product of the token reserves in the AMM -- up to a \emph{trading fee} used to incentivize liquidity provision. Trading fees substantially complicate the economic properties of AMMs, and for this reason some AMM models abstract them away in order to simplify the analysis. However, trading fees have a non-trivial impact on users' trading strategies, making it crucial to develop refined AMM models that precisely account for their effects. We extend a foundational model of AMMs by introducing a new parameter, the trading fee $ϕ\in(0,1]$, into the swap rate function. Fee amounts increase inversely proportional to $ϕ$. When $ϕ= 1$, no fee is applied and the original model is recovered. We analyze the resulting fee-adjusted model from an economic perspective. We show that several key properties of the swap rate function, including output-boundedness and monotonicity, are preserved. At the same time, other properties - most notably additivity - no longer hold. We precisely characterize this deviation by deriving a generalized form of additivity that captures the effect of swaps in the presence of trading fees. We prove that when $ϕ< 1$, executing a single large swap yields strictly greater profit than splitting the trade into smaller ones. Finally, we derive a closed-form solution to the arbitrage problem in the presence of trading fees and prove its uniqueness. All results are formalized and machine-checked in the Lean 4 proof assistant.

The Fundamental Review of the Trading Book (FRTB) poses a significant challenge for exotic derivatives pricing, particularly for non-modelable risk factors (NMRF) where sparse market data leads to infinite audit bounds under classical Martingale Optimal Transport (MOT). We propose a unified Rough Martingale Optimal Transport (RMOT) framework that regularizes the transport plan with a rough volatility prior, yielding finite, explicit, and asymptotically tight extrapolation bounds. We establish an identifiability theorem for rough volatility parameters under sparse data, proving that 50 strikes are sufficient to estimate the Hurst exponent within $\pm 0.05$. For the multi-asset case, we prove that the correlation matrix is locally identifiable from marginal option surfaces provided the Hurst exponents are distinct. Model calibration on SPY and QQQ options (2019--2024) confirms that the optimal martingale measure exhibits stretched exponential tail decay ($\sim\exp(-k^{1-H})$), consistent with rough volatility asymptotics, whereas classical MOT yields trivial bounds. We validate the framework on live SPX/NDX data and scale it to $N = 30$ assets using a block-sparse optimization algorithm. Empirical results show that RMOT provides approximately \$880M in capital relief per \$1B exotic book compared to classical methods, while maintaining conservative coverage confirmed by 100-seed cross-validation. This constitutes a pricing framework designed to align with FRTB principles for NMRFs with explicit error quantification.