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Adaptive Money Market Interest Rate Strategy Utilizing Control Theory

Adaptive Money Market Interest Rate Strategy Utilizing Control Theory ArXiv ID: 2407.10426 “View on arXiv” Authors: Unknown Abstract Decentralized Finance (DeFi) money markets have seen explosive growth in recent years, with billions of dollars borrowed in various cryptocurrency assets. Key to the safety of money markets is the implementation of interest rates that determine the cost of borrowing, and govern counterparty exposure and return. In traditional markets, interest rates are set by risk managers, portfolio managers, the Federal Reserve, and a myriad of other sources depending on the market function. DeFi enables an algorithmic approach that typically relies on interest rates being directly dependent on market utilization. The benefit of algorithmic interest rate management is the system’s continual response to market behaviors in real time, and thus an inherent ability to mitigate risks on behalf of protocols and users. These interest rate strategies target an optimal utilization based on the protocol’s risk threshold, but historically lack the ability to compensate for excessive or diminished utilization over time. This research investigates contemporary DeFi interest rate management strategies and their limitations. Furthermore, this paper introduces a time-weighted approach to interest rate management that implements a Proportional-Integral-Derivative (PID) control system to constantly adapt to market utilization patterns, addressing observed limitations. ...

July 15, 2024 · 2 min · Research Team

Bagehot was a Shadow Banker: Shadow Banking, Central Banking, and the Future of GlobalFinance

Bagehot was a Shadow Banker: Shadow Banking, Central Banking, and the Future of GlobalFinance ArXiv ID: ssrn-2232016 “View on arXiv” Authors: Unknown Abstract At the heart of both the modern shadow banking system and the 19th century banking system described by Walter Bagehot is the wholesale money market, with the ce Keywords: shadow banking, wholesale money market, liquidity, banking history, Money Markets Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper is a conceptual, historical, and institutional analysis comparing 19th-century banking to modern shadow banking, with no advanced mathematical models or empirical backtesting presented in the provided excerpt. flowchart TD A["Research Goal: Compare 19th C Bagehot banking<br>to modern shadow banking"] --> B["Methodology: Historical & Institutional Analysis<br>of wholesale money markets"] B --> C["Data/Inputs: Bagehot's "Lombard Street"<br>+ Modern Financial Data"] C --> D["Computational Process: Cross-Era Analysis<br>Mapping mechanisms & stability roles"] D --> E{"Key Findings/Outcomes"} E --> F1["1: Wholesale money markets<br>are the structural core"] E --> F2["2: Shadow banking replicates<br>19th C. banking functions"] E --> F3["3: Central banking role remains<br>crucial for liquidity"]

March 12, 2013 · 1 min · Research Team