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on Market Microstructure |
| By: | John Heilbron; Stathis Tompaidis |
| Abstract: | During market stress, various demands on U.S. G-SIBs by central counterparties may further deplete the banks’ already stressed liquid and capital resources. (Working Paper no. 25-03). |
| Date: | 2025–07–01 |
| URL: | https://d.repec.org/n?u=RePEc:ofr:wpaper:25-03 |
| By: | Gara Afonso; Marco Cipriani; JC Martinez; Matthew Plosser |
| Abstract: | Banks use central bank reserves for a multitude of purposes including making payments, managing intraday liquidity outflows, and meeting regulatory and internal liquidity requirements. Data on aggregate reserves for the U.S. banking system are readily accessible, but information on the holdings of individual banks is confidential. This makes it difficult to investigate important questions like: “Which types of banks hold reserves?” “How concentrated are they?” and “Does the distribution change over time or in response to significant events?” In this post, we summarize how non-confidential data can be used to answer these questions by providing publicly available proxies for bank-level reserves. |
| Keywords: | reserves; Publicly Available Dataset |
| JEL: | E52 G21 |
| Date: | 2025–06–23 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fednls:101134 |
| By: | Inga Ivanova; Grzegorz Rzadkowski |
| Abstract: | In this paper we analyze how market prices change in response to information processing among the market participants and how non-linear information dynamics drive market price movement. We analyze historical data of the SP 500 market for the period 1950 -2025 using the logistic Continuous Wavelet Transformation method. This approach allows us to identify various patterns in market dynamics. These patterns are conceptualized using a new theory of reflexive communication of information in a market consisting of heterogeneous agents who assign meaning to information from different perspectives. This allows us to describe market dynamics and make forecasts of its development using the most general mechanisms of information circulation within the content-free approach. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.09625 |
| By: | Francesca Molinari; Yiqi Liu |
| Abstract: | Algorithms are increasingly used to aid with high-stakes decision making. Yet, their predictive ability frequently exhibits systematic variation across population subgroups. To assess the trade-off between fairness and accuracy using finite data, we propose a debiased machine learning estimator for the fairness-accuracy frontier introduced by Liang, Lu, Mu, and Okumura (2024). We derive its asymptotic distribution and propose inference methods to test key hypotheses in the fairness literature, such as (i) whether excluding group identity from use in training the algorithm is optimal and (ii) whether there are less discriminatory alternatives to a given algorithm. In addition, we construct an estimator for the distance between a given algorithm and the fairest point on the frontier, and characterize its asymptotic distribution. Using Monte Carlo simulations, we evaluate the finite-sample performance of our inference methods. We apply our framework to re-evaluate algorithms used in hospital care management and show that our approach yields alternative algorithms that lie on the fairness-accuracy frontier, offering improvements along both dimensions. |
| Date: | 2025–07–01 |
| URL: | https://d.repec.org/n?u=RePEc:azt:cemmap:13/25 |