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چکیده
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In the complex economic environment, modeling and analyzing the relationships between key factors enhance our understanding of their interactions, enabling effective forecasting and informed decision-making. This study examines the dynamic causality among Bitcoin, oil, gold, and economic uncertainty, considering their significant roles in the global economy. Although many studies have investigated the causal and time-varying relationships among these assets, the conditional interactions in their dynamic relationships are often overlooked. Recognizing the non-stationary and non-linear characteristics of economic time series, we propose a model that probes time-varying conditional causality among the studied variables. This model examines relationships through a dual-perspective lens. Initially, the time-varying information transfer patterns among all relevant parameters are assessed across different horizons. Subsequently, our model distinctively derives and examines the conditional causal networks based on momentary trend slope. Our analysis reveals that the time-varying relationship patterns evolve by considering the conditional effects of variables on each other. The conditional causal networks among these assets vary across different time horizons, indicating complex interdependencies. Specifically, oil and gold significantly influence economic uncertainty across various scenarios, acting as potential shocks to the economic system. Furthermore, Bitcoin’s role as a safe haven depends on circumstances, particularly the nature and duration of critical periods. This research provides substantial evidence regarding the nature, extent, and conditions of interaction and risk transfer among the examined variables, offering fresh insights for stakeholders, policymakers, and researchers involved in financial analysis.
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