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Calendar choice changes the financial terms assigned to energy projects. This paper measures that change using 423 enterprise and institutional distributed-photovoltaic projects in two Shanghai subsidy directories, then trac…
We study how common policy cycles and country-specific conditions enter sovereign yield curves, conditional valuation and market selection. Across six markets, the common share of two-year yield changes rises by 11.27 percen…
Front-end rates provide a conditional reference for relative value between ten-year and thirty-year Treasuries. We evaluate the full link from conditional flatness to spread adjustment, portfolio expression, and supply sensi…
Strong AI usage coexists with differentiated service prices, capacity rents, and memory costs. We connect these signals through exact spending accounting and conditional capital recovery. Between September and October 1-30, …
The AI investment boom brings capital spending forward against expected future demand. This paper applies financial-accelerator and credit-cycle logic to corporate budgets, finance leases, contracted compute demand and third…
Artificial intelligence links seven infrastructure layers into a persistent, uneven equity-risk system. Using 33 firms, a fixed 28-stock comparison and strictly lagged factor adjustment through August 2026, return connectedn…
Yen appreciation is not a sufficient statistic for a carry unwind. Official intervention, monetary repricing, global-dollar movements and portfolio adjustment can generate similar currency outcomes while implying different f…
I show that domestic government-bond acquisition coexisted with continued overseas investment and changes in external investment form during Japan’s monetary normalization. Monthly transactions, quarterly financial accounts,…
Banking crises leave a persistent recovery gap between bank shareholder wealth, credit, and production. Across 119 crises in 39 countries, these dimensions occupy different positions relative to their pre-crisis levels in 57…
Climate scenario analysis is increasingly common, but its translation into issuerspecific financial effects remains uneven. We examine this reporting gap and the pricing of the resulting disclosures using 267 reports from 14…
Artificial intelligence (AI) booms can generate industrial price cycles even when frontier demand remains permanently elevated. I develop a two-product model in which high-bandwidth memory and conventional dynamic random-acc…
Credit support shapes both the financing of AI infrastructure and the distribution of its downside exposure. We combine a 30-project powered-land inventory, a purposive register of 15 financing instruments across four parent…
Personal demand for generative AI connects workplace adoption to household budgets. We study this connection in eleven weekly ConsumerInsight surveys from South Korea, comprising 8,800 records and a main occupational sample …
How does carbon border adjustment shape corporate green transformation through firms' international market connections? We examine its investment dimension by reconstructing annual project additions from Chinese listed firms…
Better predictions and broader allocation flexibility create complementary economic value. I measure their returns in 25 Chicago food-inspection queues from 2013 to 2026, covering 27,637 inspections and 6,473 failures. Predi…
This paper identifies the allocation architecture that converts climate need into development finance across Asia and the Pacific. It assembles 2,920 Asian Development Bank financing transactions covering 1,409 projects comm…
Language-model extraction of ESG data is scored against human references that are often revised after the model has run, yet those revisions are seldom versioned or measured. We propose a versioned gold-standard protocol: a …
AI expands candidate drug design faster than validation capacity. I develop a bottleneck-relocation framework in which experimental, manufacturing, and clinical capabilities become scarce complements to computational search.…
AI infrastructure investment creates a capital cycle in which demand commitments, construction funding, physical delivery and cash recovery mature on different schedules. We measure this cycle across compute, networking, dat…
An archival study of Chinese medium-term notes examines how public climate information appears at the issuer and debt-rating levels. It separates concentration in issuer grades, finer debt-grade resolution, and the effects o…
This paper studies the financing side of large, preannounced index additions. Space Exploration Technologies Corporation entered the Nasdaq-100 before the market open on 7 July 2026, creating a 6 July implementation event. I…
The European Union Carbon Border Adjustment Mechanism converts EU allowance auction prices into regulated import liabilities. This paper measures regulatory window risk: the pricing interval determines how far an auction-day…
This paper tests whether dispersion in market-implied Federal Open Market Committee outcomes contains information about the long-run component of U.S. equity volatility. For 38 meetings, I construct the crossoutcome variance…
Adaptive financial AI creates a system-level question that single-model evaluation cannot answer: what happens when multiple agents learn from the same experience? I identify this externality with a marginal-preserving exper…
Investor protection in corporate spin-offs is often coded as a binary feature: an assured entitlement exists or it does not. In a cross-market transaction, a right may attach to one shareholder market but not another, exist …
This paper studies the institutional redesign proposed by the Stock Exchange of Hong Kong on 21 September 2026 for corporate spin-offs. The proposal would permit qualifying Main Board parents to self-assess compliance with P…
Oil-price innovations are positively associated with the Treasury term premium, but the composition of the associated yield movement changes across periods. Monthly local projections compare 2010–2019 with 2022–August 2026 u…
This paper measures how quickly higher interest rates can pass through Japan’s government debt and the Bank of Japan’s balance sheet without treating those channels as one consolidated fiscalcost estimate. It separates four …
The market impact of the Federal Reserve's dot plot depends on the expectations formed before its release. Using 43 Summary of Economic Projections meetings from September 2015 through June 2026, this paper measures surprise…
Distributional-regime methods often summarize market geometry with a small set of reference distributions. In the Distributional Regime Persistence (DRP) framework, the maintained representation has been a calm/stress pair o…
Claims that Japanese capital is ‘coming home’ are usually made from aggregate cross-border flows. I ask whether that aggregate sign is a sufficient statistic for repatriation. Using monthly Ministry of Finance transactions i…
Formal voting authority can matter even when it does not visibly change what a central banker says. We exploit the mechanical annual rotation of voting rights among Federal Reserve Bank presidents and ask whether otherwise c…
Experiments on shared learning histories must distinguish cross-agent dependence from changes in the learning task assigned to each agent. We describe a block construction that varies exact public-history overlap while prese…
Forecast disagreement is observable before a volatility forecast is evaluated, but it is not a sufficient model-selection rule. Across eight equity indices from 2006 to 2018, a frozen rule replaces XGBoost with HAR when disp…
Predictive accuracy and decision quality can rank volatility forecasts differently because portfolio maps are invariant to some forecast errors and sensitive to others. This paper formalizes decision equivalence: statistical…
The paper examines why climate-related information does not consistently become a traceable signal in corporate bond prices, contracts, and secondary-market resilience. It defines transmission adequacy as the ability to conn…
This paper asks whether rolling roughness estimates that label volatility states also rank the shape of near-term tail variation. The design freezes the January 2020–July 2026 Binance BTCUSDT and ETHUSDT tape, 9,560 six-hour…
Public discussion of artificial intelligence and employment has split between a jobs apocalypse and an AI jobs boom. Both claims are easier to make than to count because creation is often measured with broad infrastructure e…
Adaptive forecasting systems are usually evaluated by predictive loss. This paper asks a different question: what happens to collective model diversity when forecasting systems retain different amounts of historical informat…
Volatility persistence is commonly summarized by the persistence parameter of a conditional variance recursion or by the autocorrelation of realized volatility. Neither statistic directly answers whether an empirical return …
Volatility persistence is usually represented by a scalar conditional variance, even though return distributions can remain abnormal through dispersion, center shifts, shape distortions, or continued movement. This paper dev…
This paper asks whether service-territory heat stress and disclosed data-center power-demand exposure jointly amplify the volatility of U.S. listed electric utilities. Its principal finding is methodological but empirically …
Yield-curve risk is often assembled in two steps: estimate separate volatilities for Nelson–Siegel factors, then map those volatilities back to individual maturities. The convenient diagonal map silently treats level, slope,…
Agentic trading systems are usually evaluated by the accuracy of individual forecasts. That evaluation misses a distinct systemic-risk channel: several agents can be individually well calibrated while forming correlated post…
Continual learning requires a choice about how quickly old information should lose influence. This paper studies the tail-risk consequences of that choice in adaptive volatility forecasting. Using daily historical replay for…
Empirical associations involving estimated volatility roughness may depend on the construction of the measured path. This study compares an increment-variance slope on log realized variance, the same slope after pre-averagin…
This design paper starts from a simple empirical difficulty: the same yen appreciation can be produced by very different shocks. Market commentary often compresses official intervention, a carry-trade unwind, JGB repricing, …
Funding rates on perpetual futures are directional transfers, yet empirical work often enters them as absolute values. That convention can hide an economically relevant asymmetry. This paper returns to the frozen Binance BTC…
Japanese foreign-exchange intervention is selected in unusual market conditions and followed by operations, information releases, and subsequent news. This paper organizes those stages using 69 pre-2026 episodes, daily USD/J…
Machine-learning volatility forecasts are usually judged by average out-of-sample accuracy, but their practical value depends on whether relative performance survives stress. This paper studies state-dependent forecast ranki…
Using 69 Japanese intervention episodes, this paper separates event-day USD/JPY squared-return risk from subsequent windows. Frozen multiplicity-adjusted tests detect neither pooled level differences nor delayed risk transfe…
Market commentary often compresses a rise in Japanese government bond yields into a single causal chain: yen funding becomes more expensive, carry trades unwind, and global risk assets reprice. This paper tests the empirical…
The results identify a conditional cross-market portability pattern for an online Wasserstein stress coordinate. Eight equity indices are evaluated strictly out of sample from January 2007 through June 2026 under a design fr…
A low Hurst estimate from realized volatility can describe latent variance, measurement error, or both. This paper asks a narrower empirical question: does the apparent roughness of realized volatility align with a directly …
Japanese foreign-exchange intervention is often evaluated by the exchange-rate movement on the intervention day. This paper argues that the relevant policy outcome is the persistence of the intendeddirection response after t…
Contractual lock-ups make previously restricted shares eligible to trade on dates fixed months in advance. This paper asks whether the trading response to a Hong Kong IPO cornerstone release depends on other releases reachin…
Do repeated IPO intermediaries exhibit stable contracting histories, and do those histories improve genuine out-of-time prediction? We study 930 Hong Kong Main Board operating-company IPOs from 2018 through August 2026, 1,52…
The results show that an online Wasserstein stress coordinate contributes to one-step tail forecasting after current volatility scale is filtered. Daily returns for the S&P 500, FTSE 100, Nikkei 225, and Hang Seng from 2000 …
U.S. regulators are adopting large-load tariffs to allocate the financial risk created when utilities build long-lived infrastructure for data centers and other concentrated loads. This paper studies 15 regulator-verified, e…
Rising Japanese government bond (JGB) yields are often treated as direct evidence that yen-funded carry trades are becoming uneconomic and that Japanese capital will return from foreign assets. That interpretation conflates …
A futures contract has a legal maturity, but it can lose economic relevance before that date. This paper develops the concept of contract half-life for derivatives written on rapidly evolving technological vintages. Contract…
Cornerstone investors in Hong Kong initial public offerings receive guaranteed allocations in exchange for a disclosed six-month lock-up, so each tranche converts a known share quantity into a contractually dated expansion o…
Hong Kong’s August 2025 IPO reform introduced a minimum 40% non-cornerstone bookbuilding allocation and a choice between a clawback-based public tranche and a no-clawback alternative. Using prospectus-level coding for all 16…
This paper examines whether climate risk reaches the primary bond market through contract margins that coupon regressions do not observe: the amount placed, the initial maturity, the use of perpetual structures, and the deci…
Financial AI is commonly evaluated at the model or forecasting level, while its economically consequential effects arise through portfolio construction, market interaction, and stress-state behavior. Evidence at these layers…
Shared technology suppliers can create common operational exposure across banks, but public evidence rarely identifies when that exposure becomes a cascade or financial contagion. This paper develops a layered, disclosure-ba…
This study examines how far public information allows external observers to move from a bank's visible artificial-intelligence (AI) use to disclosure relevant to dependency resilience. We audit all 29 banks on the Financial …
Benchmark design shapes whether a derivatives market forms and how much disruption it can withstand. We develop a participation model of compute derivatives in which benchmark granularity improves exposure matching but divid…
Assets under management (AUM) is widely used to describe the reach of fiduciary institutions, yet it measures the volume placed inside an arrangement rather than the quality with which entrusted functions are performed. This…
Bond-market climate research usually asks whether exposure changes yields. That is incomplete because a yield is only one point in a chain through which losses may be recognized, delayed, insured, guaranteed, refinanced or t…
China’s trust industry has returned to rapid asset growth, yet the fee income generated by that asset base has continued to contract. This article asks whether the divergence is visible within institutions and whether the po…
Volatility models are normally selected by likelihood, information criteria, residual diagnostics, and forecast loss. These criteria need not reveal whether a model reproduces the temporal direction of risk. We compare Gauss…
Artificial intelligence can improve each institution's risk forecast while reducing the financial system's ability to diversify forecast errors. This paper separates these two objects. Individual accuracy is a marginal prope…
Reversing a return series leaves its empirical distribution unchanged. Its mean, variance, skewness, kurtosis, quantiles, and Wasserstein distance from the original sample are therefore identical. Yet the risk process need n…
Bankruptcy service trusts have become an increasingly common component of large corporate restructurings in China. Among the 144 listed companies whose reorganization plans were approved by courts between 2007 and 2025, the …
Artificial intelligence can shorten investors’ responses to public news without making their desired positions more alike. This paper isolates the consequences of changing when a fixed information-driven order reaches the ma…
We study the institutional channels through which climate risk is absorbed before reaching the primary-market coupon of Chinese interbank medium-term notes (MTNs). We develop a three-layer absorption frameworkcredit ratings…
Physical climate risk can raise expected credit losses without necessarily causing an immediate breakdown in market functioning. This paper studies whether localized extreme-heat and extreme-rain episodes disrupt transaction…
Empirical climate-finance results often travel from geographic proxies to firm-level claims without a direct audit of what changes during that transport. This paper conducts such an audit for Chinese corporate bonds. Its sta…
Machine-learning models often improve volatility forecasts, but it is unclear whether their predictive information can be translated into stable economic mechanisms. This paper treats that distinction as an empirical identif…
Climate stress tests translate scenario paths into portfolio losses, but the scenario horizon can outlive the instruments being stressed. This paper isolates one implementation choice: whether sector-level shocks are censore…
Boards derive value from plural judgment, yet conventional independence measures observe directors rather than the information infrastructures that shape their decisions. This paper develops a theory of synthetic consensus: …
An 80% or 90% minimum bill is not necessarily 80% or 90% protection over the life of a grid asset. This paper documents two linked losses in the conversion of AI investment narratives into infrastructure commitment: disclosu…
Applied volatility research routinely infers state dependence by estimating a model separately in subsamples defined by a conditioning variable and comparing slopes. This paper shows that in mixedfrequency volatility models …
We revisit return-distribution and volatility differences between a broad U.S. ESG ETF and the market, and between clean- and fossil-energy ETFs, using a fully reproducible daily sample from January 2017 through July 2026. T…
U.S. financial statements contain no standardized “AI investment” line item, although market commentary routinely compares firms’ AI spending. This paper studies the resulting measurement problem using 40 earnings events for…
Serving a hyperscale data center can require irreversible utility investment before the customer operates. If the project is delayed, downsized, or cancelled, who pays for unused capacity? This paper studies largeload electr…
We develop a real-time framework for monitoring market stress through the geometry of rolling return distributions. A Wasserstein score locates each current distribution relative to low- and high-stress prototypes learned fr…
What happens to global information when a local equity market is closed but foreign markets continue to trade? We use exchange-calendar differences to construct UTC-stamped information windows from the last local close befor…
Public disclosure is not governed by a single timestamp. In the U.S. Securities and Exchange Commission's EDGAR system, the time a submission is accepted, the date legally assigned to it, and the time it becomes publicly ava…
This document establishes the Artificial Intelligence Regulatory Uncertainty (AIRU) Standard, an open and version-controlled measurement standard for institutional uncertainty in AI governance. AIRU treats regulatory transit…
Global financial stress can synchronize market movements while generating distinct loss distributions. We establish this separation using rolling empirical distributions for the S&P 500, FTSE 100, Nikkei 225 and Hang Seng fr…
We define distributional regime persistence as the duration for which a rolling empirical return distribution remains geometrically aligned with a high-volatility prototype. Building on Wasserstein market-regime clustering, …
The rapid integration of artificial intelligence across financial markets has generated a heterogeneous literature whose findings are frequently compressed into conflicting partial verdicts about whether AI stabilizes or des…
This paper documents a sharp escalation in intra-sector conditional correlation among large-cap KOSPI equities coinciding with South Korea's introduction of single-stock 2× leveraged exchange-traded funds (ETFs) in 2026. Usi…
Do market-wide trading halts stabilize financial markets, or do they amplify market-wide stress once trading resumes? This paper addresses this question using an unprecedented sequence of nine institutional trading halts imp…
We develop Institutional Synchronization Theory (IST), an equilibrium framework in which market-wide synchronization arises endogenously as the consequence of institutional optimization under a common constraint. Heterogeneo…
This paper examines whether standard GJR-GARCH volatility specifications remain structurally stable when subjected to abrupt institutional shocks, using China’s January 2016 market-wide circuit breaker episode as a natural e…
Does the volatility relevance of central-bank language travel across institutions, measures, and policy regimes? Using a harmonized text-scoring and GARCH-MIDAS pipeline, we study 362 scheduled statements from the Federal Re…
We propose a Constraint Geometry State-Space Model (CGSSM) for dynamic correlation. The CGSSM replaces the correlation matrix Rt with an interpretable two-part latent state: a scalar market-wide synchronization level λt and …
Market synchronization can emerge independent of contemporaneous information arrival, driven instead by the common component of institutional optimization under shared constraints. This paper develops the constraint geometry…
Do exogenous climate regimes reorganize the dependence structure of commodity markets, or do they merely shift the general level of co-movement? We introduce the concept of Market Architecture — defined as the persistent top…
Where an uncertainty variable enters a volatility model determines the estimand, yet this distinction is often blurred. We combine a reproducible review of 36 GARCH-family applications with a controlled expanding-window demo…
This paper examines whether Federal Open Market Committee (FOMC) statement language is associated with the long-run component of equity market volatility. We construct a dataset of 89 scheduled post-meeting statements issued…
The past two decades have witnessed a structural transformation in financial market microstructure: human traders have been displaced by algorithmic agents operating at microsecond timescales. This paper provides a systemati…
This paper examines whether the expansion of AI-related computing demand has created a new financial linkage between major technology equities and energy infrastructure assets. Using 817 daily observations from March 2023 to…
Central bank communication has evolved from a peripheral policy instrument into its primary transmission mechanism. This survey treats "Fed speak" as a narrative-economic phenomenon, applying Shiller's (2017) framework to br…
This paper investigates whether global supply chain pressure exerts a measurable influence on the long-run volatility of industrial metal futures, and whether this influence is conditional on the geopolitical environment. Us…
This paper embeds the Oceanic Niño Index (ONI) into a two-stage DCC-MIDAS model to examine how ENSO drives long-run correlation between soybean futures and WTI crude oil over September 2000 to June 2026 (N = 6,465 daily obse…
This paper documents a two-stage volatility transmission chain running from sovereign bond market stress, through the core equity assets of the artificial intelligence (AI) super-cycle, into physical energy infrastructure ma…
Against the backdrop of intensifying global climate variability, extreme weather events are increasingly inflicting physical disruptions on agricultural supply chains; yet traditional financial asset pricing models predomina…
The period from January 2023 to July 2026 witnessed an unprecedented surge in investor enthusiasm surrounding generative artificial intelligence (AI), propelling technology-sector equities—most notably NVIDIA Corporation (NV…
Hybrid GARCH–deep learning models are often presented as a cumulative progression in which econometric structure and neural flexibility jointly improve volatility forecasts. This critical review asks a narrower question: wha…
This paper examines whether investor sentiment shocks affect conditional volatility asymmetrically in China’s equity markets, using a sample of 32 AH dual-listed firm-market pairs over 2018–2024. We augment the standard GJR-…
Artificial intelligence depends on a production chain connecting semiconductors, data centres, electrical equipment, generation, nuclear supply, cooling and gas infrastructure. This paper shows how that physical chain become…
This paper develops a unified methodological framework for studying cointegration, timevarying hedge ratios, and GARCH-type volatility dynamics in mean-reverting financial spreads. When asset prices are integrated of order o…
This paper tests whether standard GJR-GARCH and DCC specifications calibrated on developed-market equity markets can be directly transplanted to China's A-share market, where two structural constraints — a ±10%/±5% daily pri…
This paper examines whether carbon price uncertainty transmits to stock market volatility in China's high-emission sectors following the launch of the national Emissions Trading Scheme (ETS) in July 2021. Using 1,072 daily o…
This paper examines how volatility model rankings depend on the choice of loss function when the true conditional variance is unobservable and forecast evaluation must rely on noisy volatility proxies. Using daily data for E…
The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) framework introduced by Bollerslev remains one of the standard tools for modeling conditional volatility in financial time series, value-at-risk measureme…
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