Trading strategies and dynamic interactions under long-term volatility

This dissertation investigates the behavior of trading strategies and the dynamics of global financial interactions under conditions of long-term market volatility. Volatility, as a central feature of financial markets, significantly shapes investor behavior, feedback mechanisms, and inter-market dependencies. Through a combination of empirical modeling and high-frequency data analysis, this research explores how traders respond to uncertainty and how financial systems across borders co-evolve in volatile regimes. Chapter 1 analyzes autocorrelation and state-dependence in trading decisions, particularly under market distress. Using a Markov-switching framework, the study captures how trading behavior shifts between tranquil and distressed states. The results show that autocorrelation in trading intensifies under distress, suggesting momentum effects and delayed information processing. The findings also reveal that trading behavior is not only path-dependent but also influenced by perceived risk regimes, highlighting the role of market conditions in shaping investor responses. Chapter 2 focuses on feedback trading strategies across different time intervals (day vs. night) and within a globally interconnected environment. Building on existing models of positive and negative feedback trading, this study introduces a global feedback trading model to examine how overnight information from international markets affects trading patterns during local trading hours. Empirical results show asymmetric feedback behaviors, with night-time trading often driven by global risk transmission, while daytime trading reflects localized reactions. The chapter highlights the importance of time segmentation and the spillover of sentiment and signals across markets and time zones. Chapter 3 extends the analysis to a global macro-financial perspective, investigating the dynamic interdependencies among major asset markets using VARX and GVAR models. The research uncovers significant long-term interconnections among equity, bond, and commodity markets, with cross-market volatility serving as a key transmission channel. Generalized impulse response functions (GIRFs) show that shocks originating in one market, such as a sudden volatility spike in U.S. equities, can propagate rapidly through interconnected global systems. This systemic sensitivity underscores the relevance of understanding global feedback loops and asset co-movements for strategic asset allocation and risk management. The concluding chapter synthesizes insights across the three studies, demonstrating that volatility is not only a catalyst for individual trading behavior but also a structural force shaping global financial interactions. The dissertation contributes to the literature by: Modeling the conditional nature of trading behavior in volatile states, Revealing temporal asymmetries in feedback trading behavior across global markets, and Mapping the global transmission of volatility through interdependent asset classes. Together, the findings offer new insights into how traders and markets adapt to prolonged volatility and provide empirical guidance for regulators, asset managers, and policymakers seeking to understand market behavior in increasingly complex and interconnected financial ecosystems.