The Impact of Stock Market Volatility (VIX Index) on the Options Trading Activity of the S&P 500: A Case Study of the US Derivatives Market (CBOE) during the Period from May 2020 to May 2025

Abstract

This study investigates the relationship between market volatility, as measured by the Volatility Index (VIX), and the monthly trading volume of S&P 500 options contracts in the U.S. derivatives market, specifically at the Chicago Board Options Exchange (CBOE). The primary aim is to examine whether the VIX index has a statistically significant role in explaining option trading activity during the period from May 2020 to May 2025. This topic holds both theoretical and practical importance amid ongoing global economic fluctuations, as understanding market dynamics and their connection to volatility indicators is essential for financial decision-making and risk management. The study adopts a quantitative analytical approach to assess the numerical relationship between variables using appropriate statistical tools. Data were collected from reliable primary sources, including the Federal Reserve Bank of St. Louis (FRED) for VIX data and the official monthly statistics of the CBOE for option trading volumes. Analytical tools employed include descriptive statistics, Pearson correlation analysis, and simple linear regression modeling, utilizing statistical software such as Excel and Python to ensure accuracy. The findings indicate a weak and statistically insignificant relationship between the VIX and option trading volumes, with a correlation coefficient of -0.097, a p-value of 0.459, and an R-squared of only 0.009. These results suggest that VIX alone is not a sufficient predictor of investor behavior or trading activity in the options market. The analysis further implies the need for more comprehensive models that incorporate additional economic and behavioral variables such as interest rates, market liquidity, and investor sentiment. Moreover, it highlights the importance of reevaluating volatility-trading relationships during crisis periods and adopting more advanced analytical frameworks like multiple regression, GARCH, or ARIMA models to capture non-linear or lagged market behaviors.