New Economics Papers
on Market Microstructure
Issue of 2009‒04‒05
four papers chosen by
Thanos Verousis

  1. Learning from post-trade identity disclosure in electronic trading By Menkhoff, Lukas; Schmeling, Maik
  2. Quadratic Variation by Markov Chains By Peter Reinhard Hansen; Guillaume Horel
  3. Modelling intra-daily volatility by functional data analysis: an empirical application to the spanish stock market By Kenedy Alva; Juan Romo; Esther Ruiz
  4. Model Misspecification, Learning and the Exchange Rate Disconnect Puzzle By V. LEWIS; A. MARKIEWICZ

  1. By: Menkhoff, Lukas; Schmeling, Maik
    Abstract: This paper shows how traders learn from post-trade identity disclosure in a currency limit order market. We establish that identity disclosure reveals information and show how traders react by reversing their order flow in line with the better informed. Informed traders primarily incorporate their own private as well as publicly available information into prices, whereas uninformed mainly magnify the effect of the informed. Within this framework, traders treat own and others? market orders as more informative than limit orders. We show that counterparty information drives out public information and that its value decays over time.
    Keywords: Identity disclosure, order flow, informed trading, foreign exchange
    JEL: G12 G15 D82 F31
    Date: 2009–03
  2. By: Peter Reinhard Hansen (Stanford University and CREATES); Guillaume Horel (Merrill Lynch, New York)
    Abstract: We introduce a novel estimator of the quadratic variation that is based on the the- ory of Markov chains. The estimator is motivated by some general results concerning filtering contaminated semimartingales. Specifically, we show that filtering can in prin- ciple remove the effects of market microstructure noise in a general framework where little is assumed about the noise. For the practical implementation, we adopt the dis- crete Markov chain model that is well suited for the analysis of financial high-frequency prices. The Markov chain framework facilitates simple expressions and elegant analyti- cal results. The proposed estimator is consistent with a Gaussian limit distribution and we study its properties in simulations and an empirical application.
    Keywords: Markov chain, Filtering Contaminated Semimartingale, Quadratic Variation, Integrated Variance, Realized Variance, High Frequency Data
    JEL: C10 C22 C80
    Date: 2009–03–24
  3. By: Kenedy Alva; Juan Romo; Esther Ruiz
    Abstract: We propose recent functional data analysis techniques to study the intra-daily volatility. In particular, the volatility extraction is based on functional principal components and the volatility prediction on functional AR(1) models. The estimation of the corresponding parameters is carried out using the functional equivalent to OLS. We apply these ideas to the empirical analysis of the IBEX35 returns observed each _ve minutes. We also analyze the performance of the proposed functional AR(1) model to predict the volatility along a given day given the information in previous days for the intra-daily volatility for the firms in the IBEX35 Madrid stocks index
    Keywords: Market microstructure, Ultra-high frequency data, Functional data analysis,Functional AR(1) model
    Date: 2009–03
    Abstract: Rational expectations models fail to explain the disconnect between the ex-change rate and macroeconomic fundamentals. In line with survey evidence on the behaviour of foreign exchange traders, we introduce model misspecification and learning into a standard monetary model. Agents use simple forecasting rules based on a restricted information set. They learn about the parameters and performance of different models and can switch between forecasting rules. We compute the implied US-UK post-Bretton Woods exchange rate under learning. While the excess volatility of the exchange rate return can be reproduced with low values of the learning gain, the implied correlations with the fundamentals are higher than in the data
    Keywords: exchange rate, disconnect, misspecification, learning
    JEL: F31 E37 E44
    Date: 2009–03

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