The Nasdaq-100, a benchmark index dominated by high-growth technology firms, exhibits distinct volatility patterns that reflect inherent financial risks. This study investigates the historical volatility trends and employs predictive models—GARCH, Random Forest, and Neural Network Auto-Regressive (NNAR)—to forecast future volatility. Historical volatility is quantified as the rolling standard deviation of log returns over a 30-day window, leveraging data from 2010 to 2024. While GARCH remains a robust traditional model for capturing heteroscedasticity, machine learning methods like Random Forest and NNAR offer insights into non-linear patterns and feature interactions. The result indicates that the volatility of Nasdaq-100 is going to be increase in the short term, according to the result prediction of Random Forest, as its lowest RMSE, which means the probability of its precise prediction is higher that others. It highlight the trade-offs in predictive accuracy and computational efficiency among these models, providing a comprehensive approach for investors and analysts in risk management and strategic decision-making. This study underscores the importance of combining traditional and modern techniques for robust volatility forecasting.

This paper examines the key drivers of bond market dynamics in the current economic environment, with a focus on the relationship between inflation, real yields, foreign investment trends, and gold. Given the persistently high interest rates, shifting inflation expectations, and global capital flows, understanding the factors influencing bond yields is critical for assessing market risks and opportunities.