Posted: Aug 09, 2023
The global financial landscape has undergone profound transformations in recent decades, characterized by increasing complexity, interconnectedness, and the emergence of novel financial instruments. Central banks, as the primary guardians of financial stability, face unprecedented challenges in navigating economic crises that propagate through intricate networks of financial institutions and markets. Traditional approaches to central banking, rooted in conventional monetary theory and linear economic models, have demonstrated limitations in addressing the non-linear, emergent behaviors that characterize modern financial crises. This research introduces an innovative computational framework that re-conceptualizes central banking operations through the lens of complex adaptive systems and artificial intelligence, offering new insights into crisis management strategies. Financial stability represents a fundamental prerequisite for sustainable economic growth and social welfare. During crisis periods, the conventional toolkit of central banks—including interest rate adjustments, liquidity provision, and regulatory measures—often proves insufficient to contain systemic risk propagation. The 2008 global financial crisis and subsequent economic disruptions have highlighted the need for more sophisticated, adaptive approaches to financial stability management. This study addresses this gap by developing a multi-methodological framework that integrates computational modeling, network analysis, and machine learning techniques to enhance our understanding of central bank effectiveness during economic turmoil.
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