nep-mst New Economics Papers
on Market Microstructure
Issue of 2016‒09‒18
three papers chosen by
Thanos Verousis

  1. Entropy and efficiency of the ETF market By Lucio Maria Calcagnile; Fulvio Corsi; Stefano Marmi
  2. Testing for heteroscedasticity in jumpy and noisy high-frequency data: A resampling approach By Kim Christensen; Ulrich Hounyo; Mark Podolskij
  3. Price impact without order book: A study of the OTC credit index market By Zoltan Eisler; Jean-Philippe Bouchaud

  1. By: Lucio Maria Calcagnile; Fulvio Corsi; Stefano Marmi
    Abstract: We investigate the relative information efficiency of financial markets by measuring the entropy of the time series of high frequency data. Our tool to measure efficiency is the Shannon entropy, applied to 2-symbol and 3-symbol discretisations of the data. Analysing 1-minute and 5-minute price time series of 55 Exchange Traded Funds traded at the New York Stock Exchange, we develop a methodology to isolate true inefficiencies from other sources of regularities, such as the intraday pattern, the volatility clustering and the microstructure effects. The first two are modelled as multiplicative factors, while the microstructure is modelled as an ARMA noise process. Following an analytical and empirical combined approach, we find a strong relationship between low entropy and high relative tick size and that volatility is responsible for the largest amount of regularity, averaging 62% of the total regularity against 18% of the intraday pattern regularity and 20% of the microstructure.
    Date: 2016–09
  2. By: Kim Christensen (Aarhus University and CREATES); Ulrich Hounyo (Aarhus University and CREATES); Mark Podolskij (Aarhus University and CREATES)
    Abstract: In this paper, we propose a new way to measure and test the presence of time-varying volatility in a discretely sampled jump-diffusion process that is contaminated by microstructure noise. We use the concept of pre-averaged truncated bipower variation to construct our t-statistic, which diverges in the presence of a heteroscedastic volatility term (and has a standard normal distribution otherwise). The test is inspected in a general Monte Carlo simulation setting, where we note that in finite samples the asymptotic theory is severely distorted by infinite-activity price jumps. To improve inference, we suggest a bootstrap approach to test the null of homoscedasticity. We prove the first-order validity of this procedure, while in simulations the bootstrap leads to almost correctly sized tests. As an illustration, we apply the bootstrapped version of our t-statistic to a large cross-section of equity high-frequency data. We document the importance of jump-robustness, when measuring heteroscedasticity in practice. We also find that a large fraction of variation in intraday volatility is accounted for by seasonality. This suggests that, once we control for jumps and deate asset returns by a non-parametric estimate of the conventional U-shaped diurnality profile, the variance of the rescaled return series is often close to constant within the day.
    Keywords: Bipower variation, bootstrapping, heteroscedasticity, high-frequency data, microstructure noise, pre-averaging, time-varying volatility
    JEL: C10 C80
    Date: 2016–08–30
  3. By: Zoltan Eisler; Jean-Philippe Bouchaud
    Abstract: We present a study of price impact in the over-the-counter credit index market, where no limit order book is used. Contracts are traded via dealers, that compete for the orders of clients. Despite this distinct microstructure, we successfully apply the propagator technique to estimate the price impact of individual transactions. Because orders are typically split less than in multilateral markets, impact is observed to be mainly permanent, in line with theoretical expectations. A simple method is presented to correct for errors in our classification of trades between buying and selling. We find a very significant, temporary increase in order flow correlations during late 2015 and early 2016, which we attribute to increased order splitting or herding among investors. We also find indications that orders advertised to less dealers may have lower price impact. Quantitative results are compatible with earlier findings in other more classical markets, further supporting the argument that price impact is a universal phenomenon, to a large degree independent of market microstructure.
    Date: 2016–09

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