nep-mst New Economics Papers
on Market Microstructure
Issue of 2016‒04‒30
five papers chosen by
Thanos Verousis

  1. Bid-Ask Spreads in OTC Markets By Carol Osler; Geir Bjonnes; Neophytos Kathitziotis
  2. Inference on Self-Exciting Jumps in Prices and Volatility using High Frequency Measures By Worapree Maneesoonthorn; Catherine S. Forbes; Gael M. Martin
  3. Jumps and Information Asymmetry in the US Treasury Market By Dumitru, Ana-Maria; Urga, Giovanni
  4. Using Social Media to Identify Market Inefficiencies: Evidence from Twitter and Betfair By Alasdair Brown; Dooruj Rambaccussing; James Reade; Giambattista Rossi
  5. Application of Demand Analysis Framework to Understand the Price and Volume Movements of Exchange Traded Funds (ETFs) By Fei, Chengcheng; Gao, Chen; Hardin, Erin M.; Dharmasena, Senarath

  1. By: Carol Osler (Brandeis University); Geir Bjonnes (Norwegian Business School); Neophytos Kathitziotis (University of Hamburg)
    Abstract: According to well-accepted theory, the three primary components of bid-ask spreads reflect operating costs, inventory costs, and adverse selection. We challenge the idea that the traditional trinity applies in all markets, arguing that OTC spreads include a price discrimination component rather than an adverse-selection component. Because OTC trades are not anonymous, OTC dealers will price discriminate according to their clients' information, market sophistication, and trading volume. Adverse selection could influence the information dimension of price discrimination or it could be irrelevant. We support this view with an empirical analysis of transactions data from the world's largest OTC market that include venue and customer IDs. The estimated price discrimination component ranges from two-thirds to six times the combined operating and inventory cost components for different cutomer groups. Adverse selection is irrelevant for most customer groups, and its contribution to spreads paid by the other two customer groups, hedge funds and customer banks, is small in absolute terms but large relative to their average markup. We indentify two structural determinants of the relevance of adverse selection: the presence of an active interdealer market and a customer's engagement in HFT.
    Date: 2016–03
  2. By: Worapree Maneesoonthorn; Catherine S. Forbes; Gael M. Martin
    Abstract: Dynamic jumps in the price and volatility of an asset are modelled using a joint Hawkes process in conjunction with a bivariate jump diffusion. A state space representation is used to link observed returns, plus nonparametric measures of integrated volatility and price jumps, to the specified model components; with Bayesian inference conducted using a Markov chain Monte Carlo algorithm. An evaluation of marginal likelihoods for the proposed model relative to a large number of alternative models, including some that have featured in the literature, is provided. An extensive empirical investigation is undertaken using data on the S&P500 market index over the 1996 to 2014 period, with substantial support for dynamic jump intensities – including in terms of predictive accuracy – documented.
    Keywords: Dynamic price and volatility jumps, stochastic volatility, Hawkes process, nonlinear state space model, Bayesian Markov chain Monte Carlo, global financial crises
    JEL: C11 C58 G01
    Date: 2016
  3. By: Dumitru, Ana-Maria; Urga, Giovanni
    Abstract: This paper analyses the informational role of the trading activity when jumps occur in the US Treasury market. As jumps mark the arrival of new information to the market, we explore the contribution of jumps in reducing the informational asymmetry. We identify jumps using a combination of jump detection techniques. For all maturities, the trading activity is more informative in the proximity of jumps. For the 2- and 5-year maturities, there is a lower level of information asymmetry before the jump, followed by a high level during the jump window and up to 20 minutes after the jump occurs. Thus, the incorporation of new information in prices is not instantaneous but several transactions are needed for the market to completely acknowledge the new information. Finally, we propose the use of the estimated integrated volatility as an exogenous predictor of jump occurrence in addition to announcement surprises.
    Keywords: Jumps,High Frequency Data,Jump Tests,US Treasury Market,Macroeconomic News,Information Asymmetry
    JEL: G12 G14 C01 C51
    Date: 2016
  4. By: Alasdair Brown (School of Economics, University of East Anglia); Dooruj Rambaccussing (Economic Studies, University of Dundee); James Reade (Department of Economics, University of Reading); Giambattista Rossi (Department of Management, Birkbeck, University of London)
    Abstract: Information extracted from social media has been used by academics, and increasingly by practitioners, to predict stock returns. But to what extent does social media output predict asset fundamentals, and not simply short-term returns? In this paper we analyse 13.8m posts on Twitter, and high-frequency betting data from Betfair, concerning English Premier League soccer matches in 2013/14. Crucially, asset fundamentals are revealed at the end of play. We find that the tweets of certain journalists, and the tone of all tweets, contain fundamental information not revealed in betting prices. In particular, tweets aid in the interpretation of news during matches.
    Keywords: social media, prediction markets, fundamentals, sentiment, mispricing
    JEL: G14 G17
    Date: 2016–04–10
  5. By: Fei, Chengcheng; Gao, Chen; Hardin, Erin M.; Dharmasena, Senarath
    Abstract: applying demand analysis framework towards financial market data
    Keywords: Exchange Traded Funds, Energy ETFs, demand systems, Agribusiness, Agricultural Finance, Demand and Price Analysis, Financial Economics, Resource /Energy Economics and Policy, Risk and Uncertainty, G11, G12, C39,
    Date: 2016

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