Microstructures of the US money markets

This project works out the microstructures of the US money markets. In doing so, I will employ a special data set on money market funds which allows the detailed analysis of frictions stemming from money market funds (MMFs) and how these frictions are a major contributing factor to the fragility of money markets and hence banks' liquidity management operations.
So far, existing literature has been focusing on the underlying factors necessitating liquidity management operations. A key contribution is that of Binachi & Bigio (2021) showing a theoretical model of random withdrawal shocks. De Fiore, Hoerova and Uhlig (2019) embed this mechanism in a sophisticated environment, showing that such withdrawal shocks combined with the need for collateralized lending drives banks into holding inefficiently large liquidity buffers.
A less developed strand of the literature is focusing on the action of liquidity management. This is largely owed to the fact that the necessary data on money markets is scarce. This strand tries to identify money market microstructures and their impact on the stability of banks' liquidity management, as well as the transmission of monetary policy.
In reaction to the 2008 Great Financial Crisis, Krishnamurthy, Nagel & Orlov (2014) identified freezes in the tri-party repo markets, dominated by MMFs. Duffie & Krishnamurthy (2016) develop an index of rate dispersion in money markets, showing the existence of money market frictions and their impact on the transmission efficiency of monetary policy. Cipriani, Gortmaker & La Spada (2019) have shown a significant lag in how MMFs' net yields are reacting to monetary policy changes. Fleming & Garbade (2007), however, have shown that dealers in the specials market for US treasuries fail to exploit arbitrage situations. And, finally, Lugo (2021) identifies MMFs as preferred-habitat investors.
Together these partial findings motivate the development of a reduced-form theory in which MMFs exhibit the following two properties: First, despite the short-lived nature of their asset holdings, MMFs tend to behave sluggishly in the money markets. I will model this behavior through assuming habit formation in their utility maximization objective. This is an established modeling choice in many macroeconomic models and facilitates a tractable way of modeling the sluggish MMF behavior. The second important property is to treat the securities showing up on MMF balance sheets as a composite. This composite allows me to have elasticities of substitution between different security classes. These elasticities capture the preferred-habitat behavior of MMFs which is born out of various reasons. MMFs have seen a number of regulations limiting for example their total risk exposure and different fund categories focus on different market segments.
Finally, these properties are embedded in a standard mean-variance setup well known in the financial literature.
The above theoretical setup will lay the foundation for the empirical model. This model will aim at estimating the key parameters accounting for the real-world behavior of MMFs. The results will help to identify to which extent MMFs are responsible for money market segmentation and further in identifying impediments to the transmission efficiency of monetary policy. Understanding the underlying dynamics of money market fragmentation is of utmost importance to the central bank, as the 2008 Great Financial Crisis was largely driven by money market dynamics which always propagate into the liquidity management of banks, and hence determine the stability of the entire banking sector. In September 2019, two coinciding regular calendar events brought the money markets in a very short-lived but extreme episode to the brink of a collapse. In March 2020, the Federal Reserve had to intervene in the money markets through purchasing assets worth several hundred billions of dollars. This project will help developing more targeted approaches.