Dynamic Interactions Between Stock Prices, Trading Volume, and Market Volatility in the Biotechnology Sector : A Sliding-window Granger-causality and PCA approach

Abstract

This thesis examines the dynamic relationships among stock prices, trading volumes, and market volatility in the biotechnology sector. This study employs Granger-causality analysis, sliding-window methodologies, and Principal Component Analysis to investigate the dynamic evolution of causal linkages and the impact of sector-wide influences on firm-level behavior. The analysis utilizes daily data from ten large-cap biotechnology firms and the CBOE Volatility Index (VIX) for the period 2015–2024. The findings indicate that the VIX considerably influences biotechnology stock returns, verifying that volatility shocks substantially impact sector performance. Bidirectional causal linkages between certain biotechnology stocks and the VIX indicate feedback mechanisms between sectoral returns and overall market volatility. The asymmetrical relationship between price and volume suggests that price fluctuations often precede changes in trading activity, partially supporting the information-driven mechanism proposed by the Mixture of Distributions Hypothesis (MDH). The time-varying Granger causality paradigm demonstrates that causal strength amplifies during volatile market phases and diminishes during stable ones. The PCA results reveal a significant common component driving stock fluctuations, emphasizing the sector’s low-dimensional framework. The integration of PCA with dynamic Granger-causality techniques yields significant insights into volatility transmission and information flow within the biotechnology sector, presenting implications for risk management and portfolio diversification.