New Economics Papers
on Market Microstructure
Issue of 2010‒03‒13
three papers chosen by
Thanos Verousis

  1. A State Space Approach to Estimating the Integrated Variance under the Existence of Market Microstructure Noise By Daisuke Nagakura; Toshiaki Watanabe
  2. Order flow dynamics around extreme price changes on an emerging stock market By Guo-Hua Mu; Wei-Xing Zhou; Wei Chen; Janos Kertesz
  3. Order selection tests with multiply-imputed data. By Consentino, Fabrizio; Claeskens, Gerda

  1. By: Daisuke Nagakura; Toshiaki Watanabe
    Abstract: Abstract We call the realized variance (RV) calculated with observed prices contaminated by (market) microstructure noises (MNs) the noise-contaminated RV (NCRV), referring to the bias component in the NCRV associated with the MNs as the MN component. This paper develops a state space method for estimating the integrated variance (IV) and MN component. We represent the NCRV by a state space form and show that the state space form parameters are not identifiable, however, they can be expressed as functions of identifiable parameters. We illustrate how to estimate these parameters. The proposed method also serves as a convenient way for estimating a general class of continuous-time stochastic volatility (SV) models under the existence of MN. We apply the proposed method to yen/dollar exchange rate data, where we find that most of the variation in NCRV is of the MN component.
    Keywords: Realized Variance, Integrated Variance, Microstructure Noise, State Space, Identification, Exchange Rate
    Date: 2010–02
  2. By: Guo-Hua Mu (ECUST); Wei-Xing Zhou (ECUST); Wei Chen (SZSE); Janos Kertesz (BME)
    Abstract: We study the dynamics of order flows around large intraday price changes using ultra-high-frequency data from the Shenzhen Stock Exchange. We find a significant reversal of price for both intraday price decreases and increases with a permanent price impact. The volatility, the volume of different types of orders, the bid-ask spread, and the volume imbalance increase before the extreme events and decay slowly as a power law, which forms a well-established peak. The volume of buy market orders increases faster and the corresponding peak appears earlier than for sell market orders around positive events, while the volume peak of sell market orders leads buy market orders in the magnitude and time around negative events. When orders are divided into four groups according to their aggressiveness, we find that the behaviors of order volume and order number are similar, except for buy limit orders and canceled orders that the peak of order number postpones two minutes later after the peak of order volume, implying that investors placing large orders are more informed and play a central role in large price fluctuations. We also study the relative rates of different types of orders and find differences in the dynamics of relative rates between buy orders and sell orders and between individual investors and institutional investors. There is evidence showing that institutions behave very differently from individuals and that they have more aggressive strategies. Combing these findings, we conclude that institutional investors are more informed and play a more influential role in driving large price fluctuations.
    Date: 2010–02
  3. By: Consentino, Fabrizio; Claeskens, Gerda
    Abstract: We develop nonparametric tests for the null hypothesis that a function has a prescribed form, to apply to data sets with missing observations. Omnibus nonparametric tests do not need to specify a particular alternative parametric form, and have power against a large range of alternatives, the order selection tests that we study are one example. We extend such order selection tests to be applicable in the context of missing data. In particular, we consider likelihood-based order selection tests for multiply- imputed data. A simulation study and data analysis illustrate the performance of the tests. A model selection method in the style of Akaike's information criterion for multiply imputed datasets results along the same lines.
    Keywords: Akaike information criterion; Hypothesis test; Multiple imputation; lack-of-fit test; Missing data; Omnibus test; Order selection;
    Date: 2009–03

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