nep-mst New Economics Papers
on Market Microstructure
Issue of 2016‒03‒17
six papers chosen by
Thanos Verousis

  1. Limit Order Book and its modelling in terms of Gibbs Grand-Canonical Ensemble By Alberto Bicci
  2. The role of volume in order book dynamics: a multivariate Hawkes process analysis By Marcello Rambaldi; Emmanuel Bacry; Fabrizio Lillo
  3. Order Book, Financial Markets and Self-Organized Criticality By Alessio Emanuele Biondo; Alessandro Pluchino; Andrea Rapisarda
  4. Performing anonymity: Investors, brokers, and the malleability of material identity information in financial markets By Aaron Z. Pitluck
  5. Volatility Discovery By Gustavo Fruet Dias; Cristina M. Scherrer; Fotis Papailias
  6. Futures trading and the excess comovement of commodity prices By Yannick Le Pen; Benoît Sévi

  1. By: Alberto Bicci
    Abstract: In the domain of the so called Econophysics some attempts already have been made for applying the theory of Thermodynamics and Statistical Mechanics to economics and financial markets. In this paper a similar approach is made from a different perspective, trying to model the limit order book and price formation process of a given stock by the Grand-Canonical Gibbs Ensemble for the bid and ask processes. As a consequence we can define in a meaningful way expressions for the temperatures of the ensembles of bid orders and of ask orders, which are a function of maximum bid, minimum ask and closure prices of the stock as well as of the exchanged volume of shares. It is demonstrated that the difference between the ask and bid orders temperatures can be related to the VAO (Volume Accumulation Oscillator) indicator, empirically defined in Technical Analysis of stock markets. Furthermore the distributions for bid and ask orders derived by the theory can be subject to well defined validations against real data, giving a falsifiable character to the model.
    Date: 2016–01
  2. By: Marcello Rambaldi; Emmanuel Bacry; Fabrizio Lillo
    Abstract: We show that multivariate Hawkes processes coupled with the nonparametric estimation procedure first proposed in Bacry and Muzy (2015) can be successfully used to study complex interactions between the time of arrival of orders and their size, observed in a limit order book market. We apply this methodology to high-frequency order book data of futures traded at EUREX. Specifically, we demonstrate how this approach is amenable not only to analyze interplay between different order types (market orders, limit orders, cancellations) but also to include other relevant quantities, such as the order size, into the analysis, showing also that simple models assuming the independence between volume and time are not suitable to describe the data.
    Date: 2016–02
  3. By: Alessio Emanuele Biondo; Alessandro Pluchino; Andrea Rapisarda
    Abstract: We present a simple order book mechanism that regulates an artificial financial market with self-organized criticality dynamics and fat tails of returns distribution. The model shows the role played by individual imitation in determining trading decisions, while fruitfully replicates typical aggregate market behavior as the "self-fulfilling prophecy". We also address the role of random traders as a possible decentralized solution to dampen market fluctuations.
    Date: 2016–02
  4. By: Aaron Z. Pitluck (Illinois State University; University of Chicago)
    Abstract: Purpose Although markets are intensely social, stock markets are peculiar in that they are normatively anonymous spaces. Anonymity is a difficult-to-achieve social accomplishment in which material identity information is successfully stripped from participants. The academic literature is conflicted regarding the degree to which equity markets are anonymous and how this influences traders’ behavior. Methodology Based on focused, tape-recorded ethnographic interviews, the article investigates the work practices of professional investors and brokers to describe the conditions under which brokers veil or reveal investors’ identities to their competitors, and thereby shed light on how anonymity is socially produced (or eroded) in global stock markets. Findings The social structure of brokered financial markets places brokers in the awkward situation of sitting in an information-poor structural location for so-called “fundamental information†while being paid to share information with professional investors who sit in an information-rich structural location. A resolution to this material and social dilemma is that brokers can erode the market’s anonymity by gifting identity information (“order flow†) —the previous, prospective, or pending trades of their clients’ competitors—thereby providing traders a competitive advantage. They share identity information in three types of performances: transparent relationships, masked relationships, and the transformation of illicit material identity information into licit and shareable “fundamental†information. Each performance partly erodes transaction-level and market-level anonymity while simultaneously partially supporting anonymity. Originality/Value Even well-regulated markets are semi-anonymous spaces due to the systematic exposure of investors’ identities to competitors by their shared brokers on a daily basis. This finding provides an additional explanation for how professional investors can imitate one another (“herd†) as well as why subpopulations of investors often trade so similarly to one another. Practical implications Laws and regulations requiring brokers’ confidentiality of their clients’ trades are easily and systematically eluded. Policy makers and regulators may opt to respond by increasing surveillance and mechanization of brokers’ work so as to promote a normatively anonymous market. Alternatively, they may opt to question the value of promoting and policing anonymity in financial markets by revising insider trading regulations.
    Date: 2016
  5. By: Gustavo Fruet Dias (Aarhus University and CREATES); Cristina M. Scherrer (Aarhus University and CREATES); Fotis Papailias (Queen's University Management School)
    Abstract: There is a large literature that investigates how homogenous securities traded on different markets incorporate new information (price discovery analysis). We extend this concept to the stochastic volatility process and investigate how markets contribute to the efficient stochastic volatility which is attached to the common efficient price (volatility discovery analysis). We use daily measures of realized variance as estimates of the latent market integrated variance and adopt the fractionally cointegrated vector autoregressive (FCVAR) framework. We extract the common fractionally stochastic trend associated with the efficient stochastic volatility, which is common to all markets. We evaluate volatility discovery by the adjustment coefficients of the FCVAR. We work with 30 of the most actively traded stocks in the U.S., which span from January 2007 to December 2013. We document that the volatility discovery does not necessarily take place at the same venue as the price discovery. These results hint that market quality and efficiency should be analysed by broader measures which take into consideration the stochastic volatility process.
    Keywords: volatility persistency, realized variance, fractionally cointegrated vector autoregressive (FCVAR), price discovery, high-frequency data
    JEL: G15 G12 G32 C32
    Date: 2016–02–24
  6. By: Yannick Le Pen; Benoît Sévi
    Date: 2016–02–18

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