Volatility forecasting is crucial for option pricing and financial risk management. Existing research can be categorized into two main types: parametric and non-parametric models. Research has concluded that non-parametric models enhance prediction accuracy by integrating high-frequency data or implied volatility while the integration requires more precise data and consumes more time. On the other hand, parametric modules such as Generalized Autoregressive Conditional Heteroskedasticity (GARCH) and its derived models are adept at capturing volatility clustering, but heavily rely on historical data and tend to lag in their response to new information. This review proposes an optimized Exponentially Weighted Moving Average GARCH (EWMA-GARCH) model to neutralize the insensitivity in existing studies, such as GARCH models’ ineffective integration of IV and incomplete model comparisons. This study provides a theoretical framework by incorporating IV into the GARCH residual term using an EWMA-weighted form with an exponential decay mechanism to assign higher weights to recent IV, while maintaining the core structure of GARCH to capture volatility clustering. Future prospects include segmenting IV by time scales, incorporating external risk factors, customizing parameters for different markets, and enhancing extreme risk prediction through quantile regression. Thus, it integrates forward-looking IV into GARCH models and offers practical risk management guidance.

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This study organizes firm level banking and Financial technology (FinTech) data and reports preliminary results using DuPont decomposition and regression. The sample covers PayPal and U.S. banks from 2015 to 2023, covering pre and post pandemic periods. In order to trace profitability channels, Return on Equity(ROE) is decomposed into profit margin, asset turnover, and equity multiplier. A linear model then links bank ROE to FinTech activity, operational drivers, and controls for size and inflation.Results show margin erosion and slower asset use in the later period: mean profit margin falls from 0.26 to 0.19, asset turnover from 0.072 to 0.065, while the equity multiplier rises from 11.5 to 12.2. Consequently, mean ROE declines from 21.5 percent to 15.1 percent. Regression estimates indicate that higher FinTech funding is associated with lower bank ROE, β = −0.43, p < 0.01, which supports a substitution effect. Profit margin and asset turnover remain positive and significant drivers of ROE, while leverage is weakly positive. The adjusted R² is 0.63, suggesting a good fit for preliminary analysis.These findings suggest that FinTech growth compresses bank profitability, that is why digital transformation, and selective partnerships may be necessary to sustain stable returns over time.

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With the vigorous development of digital finance and the widespread popularity of social media, platforms such as Xueqiu and East Money Stock Bar have become core channels for investors to obtain information and exchange views, profoundly changing the information flow pattern of financial markets and the decision-making environment for investors. Based on the perspective of behavioral finance, this paper focuses on the impact of social media on investors' investment behaviors and conducts an empirical analysis. By collecting 86,420 pieces of discussion data from Xueqiu and East Money Stock Bar between January and June 2024 using Python crawlers, combined with the trading records and characteristic questionnaires of 120 investors, this study employs methods including descriptive statistics, correlation analysis, regression analysis, and mediating effect test to explore the influence of social media discussion popularity and emotional tendency on investors' trading frequency, position change rate, and investment return rate, clarify the action paths, and analyze the behavioral differences among investors with different investment experience. The results show that social media discussion popularity significantly increases investors' trading frequency and position change rate, emotional polarity indirectly affects investment return rate through behavioral biases, and investment experience plays a moderating role in the impact of discussion popularity. The conclusions of this paper provide practical implications for investors' rational decision-making, platform governance optimization, and regulatory authorities' risk prevention and control, as well as empirical support for research on the interaction between social media and financial markets.

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This paper chronicles the evolution of USD-denominated stablecoins through two distinct eras. It begins by deconstructing the fundamental mechanics of stablecoins, explaining their 1:1 reserve backing as a solution to the price volatility of early cryptocurrencies. The "First Era" (2017-2022) is detailed as a period of cautious engagement by the U.S. banking sector within a landscape of regulatory ambiguity. Subsequently, the paper analyzes the regulatory turning point that ushered in the "Second Era," providing a comprehensive overview of the proposed U.S. GENIUS Act and its strategic design. The core argument of this paper is that the U.S. is strategically leveraging this new regulatory clarity to cultivate the stablecoin ecosystem as a tool for reinforcing its global dollar hegemony. We support this thesis with evidence from capital markets, political economy, and the strategic design of the legal architecture. The conclusion posits that the rise of the regulated digital dollar marks the dawn of a new, strategically managed era in global finance.

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