Financial market risk arises from adverse changes in asset values and is commonly assessed through volatility, Value at Risk (VaR), and tail risk. This literature review compares the variance–covariance method, Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models, historical simulation, and Extreme Value Theory (EVT). Quantile regression is considered as a supplementary approach to dynamic VaR estimation. The methods are evaluated according to their statistical assumptions, predictive accuracy, tail-risk identification, data requirements, computational complexity, and practical suitability. The literature indicates that the variance–covariance method is efficient but vulnerable to distributional and linearity assumptions. GARCH models respond to time-varying volatility, although their performance depends on model specification and the innovation distribution. Historical simulation is transparent and accommodates portfolio revaluation, but relies on the relevance of past observations. EVT provides a direct framework for extreme losses but is sensitive to threshold selection and limited tail data. Backtesting remains necessary because theoretical flexibility does not ensure accurate forecasts. No method consistently dominates across all market conditions. Reliable measurement requires model selection to reflect return characteristics, portfolio structure, forecast purpose, and data availability. In some applications, combined methods provide a more appropriate representation of volatility dynamics and tail severity.

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The global automotive industry is undergoing a profound transition driven by electrification, software-defined vehicles, and intensifying regional competition, which places sustained pressure on the margins and capital returns of incumbent manufacturers. This study examines the financial performance of Volkswagen Group over fiscal years 2021–2025 and evaluates the sustainability of its NEW AUTO strategy. Financial statement trend analysis, ratio analysis, and DuPont decomposition are applied to data drawn from the group's annual reports. Four research questions are addressed: the evolution of profitability, the DuPont driver behind the decline in returns, the contribution of NEW AUTO to long-term competitiveness, and the resulting investment stance. The results show that sales revenue rose from €250.2 billion in 2021 to €321.9 billion in 2025, while earnings after tax fell from €15.4 billion to €6.9 billion. Return on assets declined from 2.9% to 1.1% and return on equity from 10.6% to 3.4%, driven almost entirely by a fall in net profit margin from 6.2% to 2.1%, whereas asset turnover and financial leverage remained broadly stable. The analysis concludes that transformation costs have materialized ahead of their returns, and a conditional hold position on Volkswagen shares is supported.

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Against the backdrop of China's carbon peaking and carbon neutrality goals, green credit has become an important policy instrument for directing financial resources toward low-carbon industries. This study examines financing-cost patterns among 15 Chinese A-share listed firms in the new energy vehicle (NEV) supply chain from 2015 to 2022. Using 115 firm-year observations, the analysis combines descriptive statistics, period comparison, pooled OLS regression, and two-way fixed-effects estimation. The results show that average financing costs are lower in 2019-2022 than in 2015-2018, but the difference is not statistically significant. In the pooled OLS model, firm size is negatively associated with financing cost, whereas the later-period indicators, profitability, and debt-to-equity ratio, are statistically insignificant. After controlling for firm and year fixed effects, the association between firm size and financing cost weakens substantially, and none of the examined firm characteristics remains statistically significant. These findings highlight the importance of accounting for firm heterogeneity and time effects when examining financing-cost patterns in China's NEV supply chain.

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In 2026, SHEIN faces three institutional pressures simultaneously in the European market: a formal investigation under the Digital Services Act (DSA), intensified environmental enforcement by EU member states, and the EU's removal of the small-package tariff exemption. These pressures are not isolated policy shocks but jointly erode the regulatory arbitrage space on which SHEIN's cross-border direct-shipping model has long depended. Based on an "narrative–signal–audience" integrated framework that combines strategic narrative theory with signaling theory, this paper finds that SHEIN's response constitutes a differentiated strategic narrative system. Specifically, a "resilience narrative" directed at investors and supply chain partners, and a "localization narrative" directed at national governments, have relatively high credibility because they are matched by observable, high-cost action signals. In contrast, a "responsibility narrative" directed at regulators and consumers remains largely discursive at present, and its effectiveness still requires further testing. The deeper commonality underlying the three pressures is that the regulatory arbitrage space that SHEIN previously relied upon is being progressively closed off.

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