nep-mst New Economics Papers
on Market Microstructure
Issue of 2020‒02‒17
two papers chosen by
Thanos Verousis

  1. Intraday Liquidity Flows By Michele Braun; Adam Copeland; Alexa Herlach; Radhika Mithal
  2. NAPLES;Mining the lead-lag Relationship from Non-synchronous and High-frequency Data By Katsuya Ito; Kei Nakagawa

  1. By: Michele Braun; Adam Copeland (Research and Statistics Group; National Bureau of Economic Research; Federal Reserve Bank of New York; Federal Reserve Bank; University of Minnesota); Alexa Herlach (Payments Policy Function of the New York Fed's Credit and Payments Risk Group.); Radhika Mithal (Markets Group)
    Abstract: Transactions denominated in U.S. dollars flow around the clock and around the globe, filling the pipelines that support commerce. On a typical day, more than $14 trillion of dollar-denominated payments is routed through the banking system. Critical to a well-functioning economy are the timing and smooth flow of dollars for large-value transactions and the infrastructure that enables that dollar flow. This financial market infrastructure provides essential economic services??plumbing? for the economy?and is made up of a variety of entities. In this post, we describe this financial market infrastructure, providing a simple map of its main entities and describing the flow of U.S. dollar payments among these entities. A more detailed study of intraday liquidity flows has been released by the Payments Risk Committee.
    Keywords: Payments; Liquidty; Financial Market Infrastructure
    JEL: G1
  2. By: Katsuya Ito; Kei Nakagawa
    Abstract: In time-series analysis, the term "lead-lag effect" is used to describe a delayed effect on a given time series caused by another time series. lead-lag effects are ubiquitous in practice and are specifically critical in formulating investment strategies in high-frequency trading. At present, there are three major challenges in analyzing the lead-lag effects. First, in practical applications, not all time series are observed synchronously. Second, the size of the relevant dataset and rate of change of the environment is increasingly faster, and it is becoming more difficult to complete the computation within a particular time limit. Third, some lead-lag effects are time-varying and only last for a short period, and their delay lengths are often affected by external factors. In this paper, we propose NAPLES (Negative And Positive lead-lag EStimator), a new statistical measure that resolves all these problems. Through experiments on artificial and real datasets, we demonstrate that NAPLES has a strong correlation with the actual lead-lag effects, including those triggered by significant macroeconomic announcements.
    Date: 2020–02

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