nep-mst New Economics Papers
on Market Microstructure
Issue of 2022‒02‒07
four papers chosen by
Thanos Verousis

  1. They Still Haven't Told You By Bruce Knuteson
  2. Limiting Spectral Distribution of High-dimensional Hayashi-Yoshida Estimator of Integrated Covariance Matrix By Arnab Chakrabarti; Rituparna Sen
  3. Financial Intermediaries and the Macroeconomy: Evidence from a High-Frequency Identification By Pablo Ottonello; Wenting Song
  4. Identifying High-Frequency Shockswith Bayesian Mixed-Frequency VARs By Alessia Paccagnini; Fabio Parla

  1. By: Bruce Knuteson
    Abstract: The world's stock markets display a decades-long pattern of overnight and intraday returns seemingly consistent with only one explanation: one or more large, long-lived quant firms tending to expand its portfolio early in the day (when its trading moves prices more) and contract its portfolio later in the day (when its trading moves prices less), losing money on its daily round-trip trades to create mark-to-market gains on its large existing book. In the fourteen years since this extraordinary pattern of overnight and intraday returns was first noted in the literature, no plausible alternative explanation has been advanced. The main question remaining is therefore which of the few firms capable of profitably trading in this manner are guilty of having done so. If any of this is news to you, it is because the people you trust to alert you to such problems still haven't told you.
    Date: 2022–01
  2. By: Arnab Chakrabarti; Rituparna Sen
    Abstract: In this paper, the estimation of the Integrated Covariance matrix from high-frequency data, for high dimensional stock price process, is considered. The Hayashi-Yoshida covolatility estimator is an improvement over Realized covolatility for asynchronous data and works well in low dimensions. However it becomes inconsistent and unreliable in the high dimensional situation. We study the bulk spectrum of this matrix and establish its connection to the spectrum of the true covariance matrix in the limiting case where the dimension goes to infinity. The results are illustrated with simulation studies in finite, but high, dimensional cases. An application to real data with tick-by-tick data on 50 stocks is presented.
    Date: 2022–01
  3. By: Pablo Ottonello; Wenting Song
    Abstract: We provide empirical evidence of the causal effects of changes in financial intermediaries' net worth in the aggregate economy. Our strategy identifies financial shocks as high-frequency changes in the market value of intermediaries' net worth in a narrow window around their earnings announcements, based on U.S. tick-by-tick data. Using these shocks, we estimate that news of a 1-percent decline in intermediaries' net worth leads to a 0.2-0.4 percent decrease in the market value of nonfinancial firms. These effects are more pronounced for firms with high default risk and low liquidity and when the aggregate net worth of intermediaries is low.
    JEL: E30 E5 G01 G2
    Date: 2022–01
  4. By: Alessia Paccagnini (University College Dublin and CAMA); Fabio Parla (Bank of Lithuania)
    Abstract: We contribute to research on mixed-frequency regressions by introducing an innovative Bayesian approach. We impose a Normal-inverse Wishart prior by adding a set of auxiliary dummies in estimating a Mixed-Frequency VAR. We identify a high frequency shock in a Monte Carlo experiment and in an illustrative example with uncertainty shock for the U.S. economy. As the main findings, we document a “temporal aggregation bias” when we adopt a common low-frequency model instead of estimating a mixed-frequency framework. The bias is amplified in case of a large mismatching between the highfrequency shock and low-frequency business cycle variables.
    Keywords: Bayesian mixed-frequency VAR, MIDAS, Monte Carlo, uncertainty shocks, macro-financial linkages
    JEL: C32 E44 E52
    Date: 2021–12–29

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