Modeling the impact of data breaches on stock volatility using financial time series and event-based risk models

Data breaches have emerged as critical financial events with the potential to significantly impact investor confidence, market stability, and stock price volatility. As cyberattacks become more frequent and damaging, there is a growing demand for robust analytical frameworks to quantify their financial implications. This study presents a comprehensive approach to modeling the impact of publicly disclosed data breaches on stock volatility using financial time series analysis and event-based risk modeling. The research applies Generalized Autoregressive Conditional Heteroskedasticity (GARCH), Exponential GARCH (EGARCH), and Vector Autoregression (VAR) models to assess post-breach volatility patterns, spillover effects, and event lags across different industries, including technology, finance, and retail. The analysis begins with an exploration of historical stock performance around breach disclosure windows, identifying volatility clustering and asymmetric effects consistent with investor panic and uncertainty. Using event study methodology, abnormal returns and volatility shocks are captured and measured to evaluate both short-term and persistent impacts. GARCH and EGARCH models are used to quantify volatility persistence and asymmetric responses to negative news, while VAR models assess the spillover of breach-related shocks across correlated securities and sectors. Findings reveal that breach disclosures typically result in short-term spikes in volatility and negative abnormal returns, with more severe impacts observed in sectors that handle sensitive customer data. Furthermore, the market response exhibits lag effects, suggesting delayed price adjustments as new information unfolds post-breach. This study provides actionable insights for institutional investors, financial risk managers, and regulators seeking to better understand and mitigate cybersecurity-induced market risk.