The present study explores the forecasting performance of two distinct methods: Historical Simulation (HS) and Monte Carlo (MC). The aforementioned approaches find application in the estimation of VaR of the CBOE Volatility Index (VIX), a benchmark of paramount importance in the assessment of market risk. As financial institutions increasingly rely on VaR models to quantify volatility risk, the choice between computationally efficient but potentially oversimplified HS approaches and MC methods, though more sophisticated, is a key operational decision. This study employs a rolling-window framework with 10-year calibration periods to analyse a three-decade period of VIX data (1990-2023). This methodology is utilised in order to draw comparisons between standard HS, crisis-adjusted HS, and MC simulation incorporating Ornstein-Uhlenbeck processes. The findings reveal that the MC approach attained a statistically significant 12.7% reduction (p <0.01) in 95% VaR forecast errors when compared against HS during normal volatility periods (VIX <25). Furthermore, the MC approach exhibited superior crisis performance, with breach rates deviating 8.2% from theoretical expectations, in contrast to the HS approach, which deviated 31.4%. However, it is important to note that this was achieved at a substantial computational cost of 117 times the processing time (9.4 seconds vs. 0.08 seconds per estimation). The findings of the study provide a decision framework grounded in empirical evidence. It is asserted that the implementation of weighted HS is to be recommended for scenarios involving high-frequency monitoring, and that MC is to be employed for stress testing scenarios. The robustness of the decision framework has been demonstrated to be reliable on multiple occasions, as evidenced by its application during significant market events. These include the 2008 financial crisis and the 2020 pandemic volatility spike. The present text provides practitioners with guidance for the implementation of volatility risk management systems, which has been empirically validated.

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This research investigates how national digital strategies affect corporate ESG performance. Utilizing data from Chinese A-share listed companies spanning 2011-2023, we empirically examine the impact of the National Big Data Comprehensive Pilot Zones policy on corporate ESG and its underlying mechanisms using a multi-period difference-in-differences (DID) approach. The findings reveal that: (1) This policy significantly enhances firms' overall ESG performance; (2) The policy effect emerges after a 2-3 year lag, aligning with the "technology absorption → organizational adaptation → governance optimization" transmission path; (3) The policy impact exhibits heterogeneity: responses are more pronounced among firms in the eastern region and non-state-owned enterprises (non-SOEs), while the effect is insignificant for firms in the central and western regions and state-owned enterprises (SOEs). This indicates that decision-making differences stemming from regional resource endowments and corporate ownership structure are key influencing factors. This study provides evidence for understanding the micro-mechanisms through which digital policies drive corporate sustainable development, offering policy implications for optimizing pilot zone construction, promoting coordinated regional development, and precisely guiding enterprises to enhance ESG practices.

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With the rapid development of technology, the scale of enterprise accounting information continues to grow, and problems such as difficult invoice collection and cumbersome document processing are becoming increasingly prominent, Artificial Intelligence -Robotic Process Automation (AI-RPA) technology, with its advantages of simulating manual operations and automating repetitive tasks, has become an important means of accounting informatization transformation. This article is based on the case study method, combined with Weaver's Qianli Ling platform and its customer practice, to analyze the application effect of this technology in financial tax reporting and invoice issuance. In the tax declaration process, the platform can automatically log in to the business finance system through robots, obtain tax information, and complete automatic declaration of multiple tax categories in the electronic tax bureau and tax declaration client; In the invoice management process, the full automation of invoice query, information verification, automatic invoicing, result backfilling, and automatic printing can be achieved. Research has shown that AI-RPA effectively reduces labor costs, minimizes operational errors, and improves enterprise operational efficiency, providing practical reference and reference value for intelligent financial and tax applications in enterprise digital transformation.

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The rapid advancement of artificial intelligence has fundamentally reshaped corporate governance paradigms, with algorithmic ethics emerging as a critical determinant of Environmental, Social, and Governance (ESG) performance. This study investigates how algorithmic ethical failures impact ESG governance performance and explores optimization pathways through a comparative analysis of ride-hailing platforms and food delivery platforms. Despite operating in distinct service sectors, these two platform types exhibit isomorphic algorithmic governance structures characterized by black-box decision-making, power asymmetry, and responsibility drift. Employing stakeholder theory and institutional isomorphism as analytical frameworks, this paper examines the transmission mechanisms through which algorithmic ethical controversies translate into ESG rating downgrades, regulatory interventions, and reputational damage. The findings reveal that algorithmic ethical failures directly impact the Social dimension through labor rights violations and safety incidents, while simultaneously exposing Governance dimension deficiencies in transparency and accountability mechanisms. However, recent developments in China demonstrate that ESG pressures can drive substantive governance optimization. This study proposes a dual-track governance model comprising baseline protection mechanisms and value-oriented algorithm reconstruction, contributing to both theoretical understanding of digital platform governance and practical pathways for responsible algorithm development.

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