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Vietnam’s domestic gold price has persistently exceeded the world price by a wide margin, even as recent reforms have begun to relax the state’s historical monopoly over gold-bar production and imports. This paper asks why the gap persists, and whether it is confined to normal market conditions or extends into periods of extreme price movement. Using daily data spanning 2 January 2019 to 31 July 2026 (1856 trading days), covering the reform introduced by Decree No. 232/2025/ND-CP we decompose the domestic premium into a currency component and a pure physical-gold component, and use a copula-based framework to separately assess average price linkage and tail (extreme-event) co-movement between the domestic and world markets. Domestic gold bars traded at an average premium of 16.0% over import-parity world prices, of which 13.8 percentage points reflect the physical-gold component driven by constrained arbitrage, while currency factors account for only about 2 percentage points. The average linkage between the two markets is weak, indicating persistent segmentation, and this segmentation extends into the tails of the distribution for most of the sample. The premium itself carries substantial latent risk: a reversion to price parity would imply a one-off loss of about 9.6% of value, roughly eight to ten times the historical one-day 5% Value-at-Risk. Following the reform’s effective date, however, we find early evidence of emerging co-movement specifically in extreme upside price movements, even though the physical premium itself has not yet narrowed—consistent with a reform that has been enacted in law but remains at an early stage of operational implementation. The results indicate that administrative restrictions on the physical gold supply chain, rather than currency controls, are the principal source of Vietnam’s persistent gold-price gap, with direct implications for how the ongoing liberalization process should be sequenced. Full article

This paper investigates the multifaceted macroeconomic drivers of vehicle electrification in India and correlates them with micro-level corporate financial returns using a rigorous dual-stage econometric framework. Stage 1 employs a Newey–West time-series estimator on monthly observations to evaluate aggregate consumer demand elasticities across the automotive sector. Stage 2 utilizes a fixed effects panel specification with clustered standard errors across 10 major Indian automotive manufacturers over a four-year fiscal horizon. Stage 1 results demonstrate that short-run variations in Brent crude prices lack joint predictive power over domestic retail metrics ($F=0.89,p=0.4166$), supporting the thesis that state-owned OMC price-smoothing insulates short-term market dynamics from global oil shocks. Conversely, Stage 2 panel estimations prove that annual global Brent crude fluctuations yield no significant contemporaneous margin shocks. However, expanding annual EV market penetration exerts a substantive negative impact ($\beta =-2.49,p=0.107$) bordering statistical significance on corporate operating profit margins. This operational decoupling reflects a prominent industry ‘J-curve’, where accelerating consumer adoption cycles are countered by heavy front-loaded capital expenditures, asset re-tooling, and unoptimized economies of scale. These findings provide critical direct and indirect strategic insights for organizational stakeholders and policymakers navigating transitional capital cycles in emerging markets. Full article

Dynamic energy tariffs increasingly mirror renewable generation and market conditions, yet most techno-economic assessments of residential retrofit and flexibility measures still rely on simplified, static tariffs, risking-biassed cost estimates. This study systematically maps energy tariff structures across three classification tables, showing that dynamic and weather-responsive tariffs are underrepresented in ex-ante building energy modelling. Building on this gap, four ex-ante tariff-modelling methods are developed and validated, namely a fixed tariff, a time-of-use (ToU) tariff, a weather-dependent real-time-pricing (RTP) mechanism grounded in a residual-load proxy, and a capacity-based network tariff, illustrated for the Flemish context. The RTP mechanism is calibrated against two years of Belgian day-ahead prices (ENTSO-E) and Elia demand, as well as PV- and wind-generation data, reaching a monthly correlation of ρ = 0.78 (hourly ρ = 0.43, excluding 2022) and ρ = 0.71 for the final quantile-mapped tariffs, using only weather data (.epw) and three calibratable parameters. An illustrative building-level application shows that, for an identical unoptimised demand profile, a weather-driven RTP tariff changes the projected annual electricity cost by roughly 33–44%, against only 1–3% for a static ToU tariff, confirming that tariff structure must be an explicit ex-ante modelling choice. Full article

The integration of photovoltaic generation, battery storage, electric vehicles, smart appliances, and dynamic electricity pricing has made residential energy management a challenging real-time optimization problem. Conventional demand-side management methods often depend on fixed rules and are less effective under uncertain solar generation, changing tariffs, and variable user demand. To address this issue, this paper proposes a Proximal Policy Optimization-based deep reinforcement learning framework for smart home energy management. The proposed PPO controller learns adaptive scheduling decisions using real-time PV output, electricity price, battery state of charge, EV charging status, and appliance operating conditions. The controller coordinates shiftable, controllable, and non-shiftable loads while reducing electricity cost and maintaining user comfort. The proposed method is compared with DDPG and TRPO. Simulation results show that PPO reduces the average daily energy cost by 4.7% compared with TRPO and 8.3% compared with DDPG. The results confirm that PPO is an effective and stable approach for real-time residential demand-side management. Full article

This study evaluates the economic feasibility of standalone Battery Energy Storage Systems (BESS) for energy arbitrage in the Portuguese day-ahead electricity market. A Mixed-Integer Linear Programming (MILP) model is developed to optimize the operation of BESS configurations with varying durations (2, 4, 6, and 8 h), using historical price data from 2020 to 2024. The model incorporates realistic operational constraints, and the resulting arbitrage revenues are analyzed under multiple cost scenarios. Additionally, the study performs a Net Present Value (NPV) analysis using average and year-specific price profiles and three different scenarios to assess long-term investment viability. Consistent with current market access conditions in Portugal, the analysis focuses exclusively on day-ahead market arbitrage, and alternative revenue streams (intraday, real-time, ancillary services) are discussed qualitatively due to limited liquidity and restricted participation rules. The results reveal that although BESS can generate positive cash flows in recent high-volatility years, all configurations yield negative NPVs under current cost structures and market conditions. Even with optimistic cost reductions, breakeven is not achieved, indicating that standalone arbitrage remains financially not viable. These findings highlight the importance of cost optimization and the need for complementary revenue streams or policy support to make such investments feasible in Portugal. Full article

This study proposes MSATE-Net for next-day stock index forecasting. The model combines parallel one-dimensional convolutions with receptive fields of 3, 7, and 15 trading days, a bidirectional LSTM operating entirely inside a historical lookback window, sample-dependent temporal attention, and a residual regularized prediction head. Here, “bidirectional” denotes paired processing of the same observed window; it does not assert time-reversal invariance of financial prices or access to observations after the forecast origin. The globally learned attention temperature controls overall selectivity and is not described as a regime-specific adaptive parameter. Experiments use S&P 500, CSI 300, and Nikkei 225 data; persistence and drift benchmarks; recent forecasting architectures; five-seed uncertainty estimates; expanding-window tests; return and directional metrics; and Diebold–Mariano comparisons. The revised evidence supports lower price-level errors, while directional and significance results are mixed across markets. Because a separate model is fitted in each market, the findings establish cross-market consistency rather than transfer learning. Full article

Artificial intelligence has transformed dynamic pricing by enabling firms to forecast demand more accurately, respond to market uncertainty, and optimize prices in real time. Existing reviews remain fragmented, typically focusing on a single industry or on isolated methodological streams like reinforcement learning or time-series forecasting. To address this gap, this study provides a comprehensive, cross-industry synthesis of AI-based Dynamic Pricing through a systematic bibliometric analysis of 1301 Scopus-indexed publications from January 2005 to August 2025. E-commerce and digital platforms serve as the study’s central analytical lens because they frequently combine real-time transactional data, rapid price adjustment, customer-level behavioral information, platform competition, and algorithmic repricing. The analysis also extends to other digitally mediated pricing environments, including energy, mobility, electric-vehicle charging, hospitality, transportation, and retail, allowing the study to examine how methods, adoption patterns, and governance concerns vary across sectors. Using VOSviewer and CiteSpace, the study maps the intellectual structure of the field and identifies eight major research clusters. The findings reveal a clear methodological shift from rule-based and econometric approaches toward deep learning, multi-agent reinforcement learning, and simulation-driven decision systems. They also show that data-intensive and platform-mediated sectors are becoming increasingly prominent in the development and application of advanced AI-based pricing methods, while established revenue-management domains such as airlines and hospitality remain important foundations of the field. Building on these patterns, the study outlines future research opportunities centered on interpretable and uncertainty-aware pricing models, ethical and fair pricing mechanisms, and cross-industry transfer of methods and regulatory practices. This synthesis provides a structured foundation for advancing theory, methodology, and practice in AI-based DP. Full article

Disamenity and amenity facilities pull property values in opposite directions, yet prior studies have treated disamenities as a homogeneous category and have been constrained by data scattered across agencies and by mismatched spatial scales. This study integrates multi-source spatial data, comprising 3426 sub-districts (eup-myeon-dong) nationwide, a 500 m population grid of 376,668 cells, and the actual point locations of individual facilities, into a single spatial framework and applies a multi-method design that combines a spatial Durbin model (SDM), explainable machine learning (XGBoost–SHAP), an environmental-justice analysis, and a micro-siting analysis. Four findings emerge. First, not all disamenities are associated with lower prices: only pollution-emitting facilities with a clear emission signature, namely, sewage treatment plants and incinerators, capitalize robustly as cross-sectional associations, into both housing prices and officially assessed land values, with the effect extending into neighboring sub-districts rather than staying purely local. The other disamenities show no robust total effect on either price; their estimates are small or unstable across specifications. Second, capitalization of pollution-emitting facilities is pronounced in the urban sample and disappears in the rural one, and the coefficient structure differs overall between urban and rural areas (global Chow test, p < 10⁻²¹). Third, micro-siting analysis shows that where a facility is placed (siting) and what it leaves behind in prices (capitalization) are distinct layers, and that capitalization tracks the population a facility keeps nearby rather than its nuisance type: pollution-emitting facilities retain enough nearby housing for their burden to register, whereas funerary facilities sited far from people do not. Fourth, the environmental inequity of pollution exposure runs along income rather than age and concentrates in cities rather than in rural areas: the exposure rate among residents of low-income urban sub-districts (49%) is 2.5 times that of the urban population as a whole (19%), while the greater Seoul metropolitan area remains comparatively insulated. These layered findings were possible only once the dispersed spatial data had been integrated and standardized, and the study demonstrates that fusing and efficiently managing spatial data is a precondition for evidence-based land-use and environmental policy. Full article

The Corporate Sustainability Reporting Directive (CSRD) extends mandatory, assured and standardised sustainability reporting to a European reporting population several times larger than that of its predecessor, on the premise that such disclosure is priced by capital markets. This paper examines whether equity prices responded to the five legislative and standard-setting milestones through which the mandate became public between April 2021 and July 2023. German DAX constituents falling within the scope of Article 19a are compared with matched S&P 500 firms, using an annual difference-in-differences design and a daily market model event study. The annual estimator yields a positive and significant coefficient of +0.204 that is robust to alternative specifications, standard error corrections and influence diagnostics. Four diagnostics nevertheless indicate that it does not identify a regulatory effect: the same design applied to year pairs containing no CSRD or ESRS event yields estimates of comparable magnitude and mixed sign; parallel pre-trends are rejected; the coefficient is concentrated among poorly matched firm pairs and falls to +0.046 once a caliper is imposed; and the design is underpowered for effects of the magnitude it reports. The event study, which measures each firm against its own home market and therefore does not rely on the cross-country comparison, detects no abnormal return at any milestone once multiple testing and cross-sectional dependence are taken into account: the cumulative 3-day reaction across all five events is +0.8 percentage points, with a 95% confidence interval of [−2.5, +4.0], which excludes a repricing of the magnitude the annual estimate implies. The paper contributes a set of design diagnostics that distinguish an identified estimate from one that is merely stable, and shows that the two-country annual comparisons common in this literature do not survive them. Full article

The increasing integration of distributed energy resources, renewable generation, and flexible loads has made Virtual Power Plant (VPP) market participation highly exposed to price volatility and operational uncertainty. This paper proposes Graph-X, a unified graph-structured deep learning and stochastic optimization framework for day-ahead electricity price forecasting and risk-aware VPP bidding. Unlike conventional temporal forecasting models, Graph-X captures structural market-clearing behavior by converting raw bids into continuous differentiable curves represented on a discrete price–quantity graph. The proposed architecture combines sparse graph convolutions, recurrent temporal learning, dilated temporal convolutions, and cyclical calendar encodings to model spatial, temporal, and operational market dependencies. Forecasts are further integrated with a stochastic bidding model governed by a coherent spectral risk measure to align prediction accuracy with financial performance. The framework is validated using historical hourly data from the ISO New England day-ahead electricity market. Results show that Graph-X achieves an MAE of 1.81 $/MWh and an R² score of 0.944, outperforming GNN, LSTM, Transformer, and ARIMA baselines. In VPP bidding, Graph-X delivers an average daily profit of 52.3 k$, improving profitability by 18.1% (equivalent to $2.92 annually) over the GNN baseline, with an average inference time of 12.5 ms per forecast. Full article

Anthropogenic greenhouse gases (GHGs) and emissions from maritime transport represent a significant challenge for atmospheric monitoring and control. The Istanbul Strait, characterized by its narrow, winding geography and high traffic density, presents a unique chokepoint where these emissions directly impact local air quality. This study proposes a gas-focused integrated framework that combines Sentinel-5 Precursor (Sentinel-5P) TROPOspheric Monitoring Instrument (TROPOMI) satellite observations with Automatic Identification System (AIS) data to analyze atmospheric trace pollutant time series in the Istanbul Strait during 2025. A bottom-up emission methodology based on the IMO 4th GHG Study was employed, yielding annual gaseous pollutant totals of 213,678 tons of carbon dioxide (CO₂), 5970 tons of nitrogen oxides (NO_x), and 686 tons of sulfur oxides (SO_x). Time-series and cross-correlation analyses demonstrated a quantifiable relationship between AIS-derived NO_x estimates and TROPOMI NO₂ tropospheric column densities (r = 0.76, p < 0.05, n = 12), validating the use of satellite sensors for marine atmospheric monitoring. A decision support system (DSS) proof of concept (PoC) was developed to evaluate emission control scenarios through speed optimization. The results indicate that implementing a 10% speed reduction strategy could reduce CO₂ emissions by 18% (38,462 tons) and generate net economic savings of EUR 3.07 million under the European Union Emissions Trading System (EU ETS) carbon pricing framework. Furthermore, a scenario with a 20% speed reduction resulted in a 35% decrease in CO₂ emissions. The findings underscore the potential of integrating satellite-based gas remote sensing with AIS data, thereby facilitating real-time atmospheric monitoring and strengthening emission control policy enforcement in maritime chokepoints. Full article

Centralized hot water systems in university dormitories can provide significant energy flexibility through demand response (DR). Thus far, however, existing studies have mainly focused on system-level optimization and have failed to provide a quantitative framework that accounts for individual users’ DR participation behavior and the associated uncertainty. In this paper, we develop a data-driven approach to evaluate demand-side flexibility and quantify the uncertainty that arises as a result of user participation. A clustering-based stochastic load prediction model is proposed and validated using real operational data, serving as the baseline for DR load shifting. Four DR strategies for students are designed based on time-of-use pricing and/or academic credit incentives. A survey of nearly 1000 students is used to calibrate participation probabilities, while a binomial distribution model characterizes the uncertainty of user participation, allowing us to derive the probability distribution and expected value of the system’s flexibility potential. Compared with the no-DR baseline, the combined price–credit incentive yields the highest flexibility, achieving a peak-shaving rate of 62.66% and thus significantly outperforming the price-only strategy. Notably, the academic credit incentive alone increases students’ willingness to participate more effectively than price signals. Furthermore, when the number of participating users exceeds 648, the fluctuation range of the estimated flexibility potential falls below 10.5%, enabling stable flexibility evaluation with a moderately large user sample. Full article

Grounded in the theoretical frameworks of safe haven and hedging asset theory, alongside wavelet-based time-frequency analysis, this study investigates the dynamic interconnectedness between three major cryptocurrencies and six global stock markets spanning both developed and emerging economies. Unlike prior wavelet studies that rely predominantly on graphical interpretation, this paper advances the literature by complementing graphical outputs with numerical results, offering a more rigorous and reproducible analytical foundation. Using daily price data from January 2018 to October 2024, the study applies both univariate and multivariate wavelet techniques to capture return co-movements across multiple time horizons. The univariate analysis reveals significant variance in stock returns concentrated at high frequencies, particularly over 2–4-day cycles, with pronounced fluctuations during the COVID-19 pandemic. In emerging markets such as Nigeria, additional volatility is attributed to political instability and macroeconomic crises. The multivariate analysis further demonstrates that observed co-movements between cryptocurrencies and stock markets are largely driven by interdependence rather than contagion. The paper’s findings are relevant to portfolio diversification strategies across both developed and emerging markets for investors combining stock and cryptocurrency assets. Full article

Background: Pirtobrutinib has recently emerged as a promising first-line treatment option for chronic lymphocytic leukemia (CLL). Unlike currently established regimens, which are generally based on doublet combinations, pirtobrutinib can be administered as monotherapy. No head-to-head trials comparing pirtobrutinib with contemporary first-line combinations are currently available; hence, indirect comparative evidence may help define its potential role. Methods: A non-anchored indirect comparison based on reconstructed individual patient data (IPD) was conducted using published Kaplan–Meier curves from randomized controlled trials evaluating first-line treatments for CLL. Progression-free survival (PFS) was the endpoint of interest. Reconstructed IPD were generated using WebPlotDigitizer and the IPDfromKM algorithm. Then, Kaplan–Meier curves were plotted based on these patients, and the values of the restricted mean survival time (RMST) at 36 months were determined. Regarding PFS, pirtobrutinib monotherapy was compared indirectly with acalabrutinib plus obinutuzumab, venetoclax plus obinutuzumab, and venetoclax plus ibrutinib. Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated using univariate Cox models. The non-inferiority of pirtobrutinib monotherapy versus doublet regimens was assessed according to a non-inferiority margin set at HR = 1.15. Finally, the value-based prices of the new treatments were estimated based on a willingness-to-pay threshold of euro 30,000 per disease-free year gained and compared with the corresponding real prices in the Italian market. Results: The analysis included four randomized trials. Compared with pirtobrutinib monotherapy, HRs for PFS were 0.5544 (95%CI, 0.2696–1.1397) versus venetoclax plus obinutuzumab, 0.4583 (95%CI, 0.2066–1.0200) versus venetoclax plus ibrutinib, and 1.4453 (95%CI, 0.6684–3.1240) versus acalabrutinib plus obinutuzumab. Pirtobrutinib met the non-inferiority criterion compared with venetoclax plus obinutuzumab and venetoclax plus ibrutinib, but not with acalabrutinib plus obinutuzumab; however, these results were negatively affected by the small number of events in the pirtobrutinib arm, which generated wide CIs. In the preliminary pharmacoeconomic analysis, venetoclax plus obinutuzumab showed the most favorable profile in the comparison of value-based price vs. real price. Conclusions: This exploratory non-anchored analysis suggests that pirtobrutinib monotherapy may provide PFS outcomes broadly comparable to current first-line combination regimens for CLL. Given the methodological limitations inherent to indirect comparisons, prospective head-to-head studies are needed to clarify the optimal positioning of pirtobrutinib in treatment-naïve CLL. Full article

Although stablecoins occupy a segment of digital-asset markets in which price stability is central by design, their temporary departures from reference values may reveal important information about latent risk and market stress. In this paper, we examine whether bubble and crash signals extracted from traditional cryptocurrencies and stablecoins improve volatility, Value-at-Risk, and Expected Shortfall forecasting and, in connection with these forecasting gains, contribute to the assessment of cross-asset contagions. The analysis applies the Bubble Crash–GARCH models, in which extreme price phases are identified through the Phillips, Shi, and Yu real-time monitoring procedure and incorporated into the conditional mean of returns through event-based dummy variables. For stablecoins, extreme episodes are not inferred from price dynamics in isolation but from deviations between the observed price and the asset-specific reference value. The empirical investigation focuses on Bitcoin, Ethereum, Tether’s USD-pegged (USDT), and Tether Gold and evaluates asset-specific bubble–crash effects and bidirectional contagion channels between traditional cryptocurrencies and stablecoins, using Bitcoin and Tether as the leading representatives of the two market segments. The findings indicate that accounting for bubble and crash episodes leads to more accurate volatility forecasts than standard GARCH benchmarks. For Value-at-Risk and Expected Shortfall, the bubble–crash specifications can improve tail risk forecasting at several tail probability levels through more accurate coverage, lower quantile loss, and stronger ESR backtesting performance. The results also reveal different degrees of price exuberance across the two asset categories: while extreme price dynamics are more evident among traditional cryptocurrencies, deviations from fundamentals are rare for stablecoins. Among stablecoins, USDT exhibits limited but detectable exuberance, whereas Tether Gold does not display extreme price episodes. However, when such deviations occur, as in the case of USDT, they generate significant contagion effects on major cryptocurrencies. Notably, extreme episodes originating in USDT have a stronger impacts on Bitcoin and Ethereum than the reverse spillovers from traditional cryptocurrencies to USDT. Overall, the evidence suggests that stablecoins are not merely passive instruments within the digital-asset ecosystem. Even temporary deviations from their reference values contain valuable information for risk forecasting and contagion monitoring. Full article