nep-mst New Economics Papers
on Market Microstructure
Issue of 2021‒07‒12
six papers chosen by
Thanos Verousis
University of Essex

  1. Estimation of Common Factors for Microstructure Noise and Efficient Price in a High-frequency Dual Factor Model By Li, Y-N.; Chen, J.; Linton, O.
  2. Insider trading in Brazil's stock market By Marzagão, Thiago
  3. Competing for Stock Market Feedback By Caio Machado; Ana Elisa Pereira
  4. Passive funds affect prices: evidence from the most ETF-dominated asset classes By Karamfil Todorov
  5. Forecasting the intra-day effective bid ask spread by combining density forecasts By Malick Fall; Waël Louhichi; Jean Viviani
  6. Bridge Proxy-SVAR: Estimating the Macroeconomic Effects of Shocks Identified at High-Frequency By Alejandro Vicondoa; Andrea Gazzani

  1. By: Li, Y-N.; Chen, J.; Linton, O.
    Abstract: We develop the Double Principal Component Analysis (DPCA) based on a dual factor structure for high-frequency intraday returns data contaminated with microstructure noise. The dual factor structure allows a factor structure for the microstructure noise in addition to the factor structure for efficient log-prices. We construct estimators of factors for both efficient log-prices and microstructure noise as well as their common components, and provide uniform consistency of these estimators when the number of assets and the sampling frequency go to infinity. In a Monte Carlo exercise, we compare our DPCA method to a PCA-VECM method. Finally, an empirical analysis of intraday returns of S&P 500 Index constituents provides evidence of co-movement of the microstructure noise that distinguishes from latent systematic risk factors.
    Keywords: Cointegration, Factor model, High-frequency data, Microstructure noise, Non-stationarity
    JEL: C10 C13 C14 C33 C38
    Date: 2021–06–30
  2. By: Marzagão, Thiago
    Abstract: How much insider trading happens in Brazil’s stock market? Previous research has used the model proposed by Easley et al. [1996] to estimate the probability of insider trading (PIN) for different stocks in Brazil. Those estimates have a number of problems: i) they are based on a factorization that biases the PIN downward, especially for high-activity stocks; ii) they fail to account for boundary solutions, which biases most PIN estimates upward (and a few of them downward); and iii) they are a decade old and therefore based on a very different market (for instance, the number of retail investors grew from 600 thousand in 2011 to 3.5 million in 2021). In this paper I address those three problems and estimate the probability of insider trading for 431 different stocks in the Brazilian stock market, for each quarter from October 2019 to March 2021.
    Date: 2021–06–18
  3. By: Caio Machado; Ana Elisa Pereira
    Abstract: We study how firms compete to attract informed trading when financial markets provide information to decision makers. Firms increase managerial risk taking to compete for market information, leading to a rat race in which firms overinvest in a (failed) attempt to increase their own stock informativeness. Efficiency gains of learning from the market may be eliminated: There is always an equilibrium where financial markets provide useful information, but are completely ignored by decision makers. Moreover, in any equilibrium firms react too little to market activity. Our results highlight that critically different outcomes arise when firms interact in integrated financial markets.
    Date: 2020
  4. By: Karamfil Todorov
    Abstract: This paper studies exchange-traded funds’ (ETFs) price impact in the most ETF-dominated asset classes: volatility (VIX) and commodities. I propose a modelindependent approach to replicate the VIX futures contract. This allows me to isolate a non-fundamental component in VIX futures prices that is strongly related to the rebalancing of ETFs. To understand the source of that component, I decompose trading demand from ETFs into three parts: leverage rebalancing, calendar rebalancing, and flow rebalancing. Leverage rebalancing has the largest effects. It amplifies price changes and exposes ETF counterparties negatively to variance.
    Keywords: ETF, leverage, commoditization, VIX, futures
    JEL: G11 G13 G23
    Date: 2021–07
  5. By: Malick Fall (CREM - Centre de recherche en économie et management - UNICAEN - Université de Caen Normandie - NU - Normandie Université - UR1 - Université de Rennes 1 - UNIV-RENNES - Université de Rennes - CNRS - Centre National de la Recherche Scientifique); Waël Louhichi (ESSCA Research Lab - ESSCA - Groupe ESSCA); Jean Viviani (CREM - Centre de recherche en économie et management - UNICAEN - Université de Caen Normandie - NU - Normandie Université - UR1 - Université de Rennes 1 - UNIV-RENNES - Université de Rennes - CNRS - Centre National de la Recherche Scientifique)
    Abstract: The bid-ask spread refers to the tightness dimension of liquidity and can be used as a proxy for transaction costs. Despite the importance of the bid-ask spread in the financial literature, few studies have investigated its forecastability. We propose a new methodology to predict the bid ask spread by combining density forecasts of two types of models: Multiplicative Errors Models and ARMA-GARCH models. Our method is employed to predict the effective intra-day bid-ask spread series of all shares pertaining to the CAC40 index. Using a one-step-ahead out-of-sample framework, we resort on the Model Confidence Set procedure of Hansen et al. (2004) to classify models and we found that the proposed model appears to beat all the benchmark specifications.
    Keywords: Effective bid-ask spread,High-Frequency,Multiplicative Errors Models,Forecasting
    Date: 2021
  6. By: Alejandro Vicondoa; Andrea Gazzani
    Abstract: This paper proposes a novel methodology, the Bridge Proxy-SVAR, which exploits high-frequency information for the identification of the Vector Autoregressive (VAR) models employed in macroeconomic analysis. The methodology is comprised of three steps: (I) identifying the structural shocks of interest in high-frequency systems; (II) aggregating the series of high-frequency shocks at a lower frequency; and (III) using the aggregated series of shocks as a proxy for the corresponding structural shock in lower frequency VARs. We show that the methodology correctly recovers the impact effect of the shocks, both formally and in Monte Carlo experiments. Thus the Bridge Proxy-SVAR can improve causal inference in macroeconomics that typically relies on VARs identified at low-frequency. In an empirical application, we identify uncertainty shocks in the U.S. by imposing weaker restrictions relative to the existing literature and find that they induce mildly recessionary effects.
    Date: 2020

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