The Fedz: A New Approach to Studying Bank Runs in Field Conditions

In the following chapter, we delve into the extensive background of bank-run studies, primarily focusing on their development in theoretical environments. While these theoretical frameworks have provided significant insights, they often lack the practical applicability needed to fully understand and prevent bank runs in real-world scenarios.

The Paradox of Bank-Run Prevention

Despite the robust theoretical advancements, bank runs still occur in practice. This paradox highlights the gap between theoretically sound mechanisms and their real-world effectiveness. As David Andolfatto (2017) states, "Anyone who wants to use Diamond and Dybvig (1983) to explain historical episodes of bank runs must provide a consistent theory of why the banks operating during those episodes did not take advantage of contracts capable of preventing runs (as the one we propose here)." Factors such as information asymmetry, behavioral biases, and systemic risks can undermine theoretical solutions.

The Fedz: A New Methodology for Field Experiment Study of Bank Run Mitigation

The Fedz ecosystem offers a groundbreaking environment for conducting field experiments on bank run mitigation. By leveraging blockchain technology and decentralized finance (DeFi) principles, The Fedz provides a real-world yet controlled setting to test and refine bank run prevention mechanisms.

Strategies for Managing Bank Withdrawals

The Fedz environment supports various models of decision-making to study their effects on bank stability and depositor behavior.

• Sequential Service (First Come, First Serve - FCFS):

  In line with the Diamond-Dybvig (DD) model, the environment can simulate a traditional banking system where withdrawals are processed sequentially in the order they are received. This setup replicates the sequential service constraint, where early withdrawers are prioritized. Researchers can examine how quickly bank runs can escalate under this system and explore preventive measures to mitigate them.

• Intermediary Planner:

  Inspired by the Green-Lin (GL) model, this setup introduces an intermediary planner who coordinates withdrawals and communicates with depositors to manage liquidity distribution effectively. The planner can implement mechanisms such as pro-rata sharing of available liquidity, which can reduce the incentive for early withdrawals and potentially prevent panic-induced runs. Researchers can study the effectiveness of these planned interventions and their ability to maintain stability in the banking system.

  • Future Ideas:

    The Fedz environment is flexible and open to testing new concepts proposed by the community and researchers. This adaptability allows for continuous innovation and testing of novel bank run prevention mechanisms, such as decentralized insurance pools or real-time liquidity tracking systems, ensuring the environment remains at the forefront of financial stability research.