Whether institutionalisation has reshaped how cryptocurrencies discover prices, transmit volatility, and price systematic risk relative to traditional markets remains an open question for financial economists, investors, and regulators. Prior research shows Bitcoin’s connection to traditional markets is real, time-varying, and regime-dependent, but does not resolve whether an institutional milestone produced a genuine structural change in price discovery, volatility co-movement, and risk pricing. We address this gap using the January 2024 U.S. spot Bitcoin ETF approval as a proxy for institutionalisation, applying a Hasbrouck information share decomposition, a GJR-GARCH/DCC framework, and Fama-MacBeth tests with GMM stochastic discount factor estimation on a daily panel spanning 2020 to 2025. A block-bootstrap test documents a statistically significant post-ETF reorganisation of price discovery. Ethereum’s informational leadership declined significantly, and equity markets absorbed a small but robust and significant share of common information. Bitcoin’s own share rose in the same direction but falls short of significance. Regime-conditional DCC-GARCH tests find no significant rise in Bitcoin-equity correlation during high-volatility states, ruling out contagion and challenging safe-haven claims made for Bitcoin. Cross-sectional factor risk premia are not statistically detectable at the full-sample level, and an apparent post-ETF size-factor effect does not survive a formal difference test. Cryptocurrency risk pricing has not converged toward conventional factor structures despite institutional inflows. These results give the first pre/post-milestone evidence that institutionalisation reorganised price discovery, rule out contagion, and confirm risk pricing remains unconventional. Cryptocurrency markets are institutionally transitioning rather than mature, with implications for informed trading theory and integration-versus-contagion debates.
Statistical forecast loss and regulatory tail coverage can rank volatility models differently. We study 19 equity indices from 2000 to 2025 (seven developed and twelve emerging markets) under one univariate daily-close rolling design. We score three objects on the same origins: one-step QLIKE, the 95 h=10 ). GJR-GARCH records the lowest QLIKE in 16–19 indices. At that horizon, exponentially weighted moving average and filtered historical simulation lead the 95
This paper investigates how Environmental, Social and Governance (ESG) capability operates as a prudentially relevant meta-capability in European banking, transforming disclosure-driven compliance into strengthened capital resilience, efficiency and innovation. Grounded in the resource-based view and dynamic capabilities theory, it addresses an untested regulatory gap on whether ESG integration reinforces core supervisory levers- capital adequacy, technology-oriented intangible investment and cost efficiency- and how these levers transmit into market valuation and innovation that regenerate future ESG capability. Using a balanced panel of 117 listed banks from Germany, France, Spain, the Netherlands and the United Kingdom over 2015–2024, the paper estimates an eight-equation dynamic system via two-step System GMM to model persistence, unobserved heterogeneity and endogeneity across ESG scores, prudential ratios, efficiency, market valuation and R D effort. Findings reveal that higher ESG scores are significantly associated with stronger capital adequacy, greater technology-oriented intangible investment and lower cost-to-income ratios; prudential strength, technology-oriented intangible investment and efficiency are rewarded through higher Tobin’s Q; and stronger valuation relaxes financing constraints, raising R D intensity that subsequently upgrades ESG capability. To address measurement concerns regarding the intangibles-based technology proxy, the design adds a dedicated robustness check using RegTech disclosure intensity as an alternative technology-channel proxy. Overall, ESG emerges as a prudential and technology-related capability rather than a cosmetic reporting construct, creating a self-reinforcing ESG-risk-innovation cycle in a highly regulated banking setting. The paper provides dynamic, multi-stage evidence that ESG capability supports core supervisory objectives while simultaneously enhancing market-based competitiveness and innovation capacity in European banks, offering actionable insights for the implementation of the Corporate Sustainability Reporting Directive and Basel-aligned supervisory review processes.
AI allows lenders to identify physical climate exposure at scale. But when a high-risk signal raises the price of the same credit needed to finance adaptation, better measurement can leave the identified risk less—not more—managed. We call this the risk-identification paradox. This sequencing problem extends beyond AI, but AI makes it economically salient because exposure analytics scale faster than borrower-specific resilience verification and lender recognition. We develop a competitive lending model in which borrowers differ in physical exposure and adaptation productivity, information about these attributes arrives sequentially, and credit terms affect investment in risk reduction. A high-exposure signal raises the lending spread when the incremental exposure charge exceeds the recognized resilience credit. Tighter finance then reduces capital adaptation and the complementary operational response, causing risk pricing to feed back into future physical loss. The welfare effect depends on which borrowers are penalized. Greater precision is most damaging when high exposure coincides with high adaptation productivity; it can instead screen inefficient hardening when the penalized borrowers have low productivity and maladaptation is sufficiently costly. Resilience verification closes the financing wedge only when lenders assign the evidence sufficient contractual weight. Certification can improve evidence quality, but it does not automatically improve financing terms. Effective climate-risk management therefore requires institutional recognition of verified risk reduction to advance alongside exposure analytics.
Value-at-Risk (VaR), the most widely used measure of market risk, is typically evaluated through backtesting of point forecasts. Such procedures, however, say little about the uncertainty of the estimated quantile. Existing interval methods are each tied to a specific model class and fail when its underlying assumptions are violated. We propose Quantile Dynamically-Tuned Adaptive Conformal Inference (QDtACI), a model-agnostic conformal calibration layer that constructs finite-sample intervals around any VaR forecast, using only the return series and the forecast itself. QDtACI adapts dynamically-tuned adaptive conformal inference to the quantile setting through two components: a pinball-loss nonconformity score aligned with the quantile objective and an asymmetric interval construction, and a multi-speed expert-aggregation mechanism driven by a smoothed violation error and a composite loss on coverage, width, and stability. On synthetic GARCH data, where the true VaR is observable, QDtACI attains near-nominal coverage of the true VaR when the underlying forecast is well-specified, and its coverage degrades in a controlled way as the forecast is misspecified. Against the Delta method, a bootstrap, and the DtACI baseline, it achieves coverage closer to nominal at comparable or better interval quality (Winkler score). Applied to a portfolio of 24 fixed-income assets (2016–2024) with VaR forecasts from CAViaR, DCC-GARCH, and copula models, the intervals are stable in calm periods and widen sharply during stress, including the COVID-19 shock and the 2022–2023 monetary tightening. Because true coverage cannot be measured on real data, we further provide a return-only diagnostic that indicates when interval calibration can be trusted as a proxy for coverage of the true VaR. QDtACI thus offers a single, broadly applicable procedure for uncertainty quantification in VaR, whose reliability tracks the quality of the underlying forecast.
