EMPIRICAL MEASUREMENT OF FINANCIAL UNCERTAINTY

Author: José Gerardo De La Vega Meneses

ABSTRACT

This project explores the behavior of the CBOE Volatility Index (VIX) through data analysis in Python, providing investors with a structured way to interpret market uncertainty. The VIX, commonly called the “Fear Index,” captures expected volatility in the S&P 500 and serves as a key indicator of market sentiment.

Using Python tools such as YFinance and Pandas, historical data is extracted, organized, and examined. The study applies visual and statistical techniques—including moving averages, Bollinger Bands, and candlestick representations—to identify recurring patterns and shifts in volatility conditions. Additional evaluation of daily returns and trend segmentation helps distinguish between stable and turbulent market phases.

By translating raw financial data into meaningful insights, this approach supports better risk assessment and strategic planning. Python enables efficient processing of large datasets, making it easier to navigate and interpret the complexity of financial markets characterized by constant change.

Keywords: VIX, Python, analysis.

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