New Economics Papers
on Market Microstructure
Issue of 2010‒02‒05
four papers chosen by
Thanos Verousis

  1. What Causes Herding:Information Cascade or Search Cost ? By Lin, William; Tsai, Shih-Chuan; Sun, David
  2. Asymptotic equivalence and sufficiency for volatility estimation under microstructure noise By Markus Rei\ss
  3. Customer Trading in the Foreign Exchange Market: Empirical Evidence from an Internet Trading Platform By Sandra Lechner; Ingmar Nolte
  4. The Dynamic Relationship between Price and Trading Volume: Evidence from Indian Stock Market By Brajesh Kumar

  1. By: Lin, William; Tsai, Shih-Chuan; Sun, David
    Abstract: We analyze in this study what could have caused herding in the stock market. Information cascades have often been considered as a major cause. However, we present in this study evidences inconsistent with that hypothesis. Our analysis is in support of an alternative theory based on search cost of investors. Specifically, previous works studied daily data or those with lower frequency based on a herding measure of Lakonishok, Shleifer, and Vishny (1992). We adopt instead the measure of Patterson and Sharma (2006) and argue that the search model of Vayanos and Wang (2007) characterize herding phenomenon better. Our analysis supports their hypothesis employing intraday order book data. We find that stronger order flow herding is driven by lower transactions cost. Herding tend to occur in trading of high-cap, high turnover stocks, which contradicts prediction of the information cascade hypothesis. Information cascade effect, if any, is actually stronger near market close than at open. Therefore our study suggests that herding could be related more to intrinsic search cost structure of investors rather than information related factors.
    Keywords: Herding; information cascade; search model; order book
    JEL: G12 L11 C14 D82 D83
    Date: 2009–02–03
  2. By: Markus Rei\ss
    Abstract: The basic model for high-frequency data in finance is considered, where an efficient price process is observed under microstructure noise. It is shown that this nonparametric model is in Le Cam's sense asymptotically equivalent to a Gaussian shift experiment in terms of the square root of the volatility function $\sigma$. As an application, simple rate-optimal estimators of the volatility and efficient estimators of the integrated volatility are constructed.
    Date: 2010–01
  3. By: Sandra Lechner; Ingmar Nolte
    Date: 2009
  4. By: Brajesh Kumar
    Abstract: This study investigates the nature of relationship between price and trading volume for 50 Indian stocks. Firstly the contemporaneous and asymmetric relation between price and volume are examined. Then the dynamic relation between returns and volume using VAR, Granger causality, variance decomposition (VD) and impulse response function (IRF) are examined. Mixture of Distributions Hypothesis (MDH), which tests the GARCH vs. Volume effect, is also studied between the conditional volatility and volume. [IIMA WP No. 2009-12-04].
    Keywords: Indian stocks, Trading volume, Volatility, Mixture of distributions hypothesis, GARCH, Granger,Causality, VAR, Impulse response function, Variance decomposition
    Date: 2010

This issue is ©2010 by Thanos Verousis. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
General information on the NEP project can be found at For comments please write to the director of NEP, Marco Novarese at <>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.