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

  1. Price and trading response to public information By Magdalena Malinowska
  2. Why do firms pay for liquidity provision in limit order markets? By Skjeltorp, Johannes A; Odegaard, Bernt Arne
  3. Information Content of Order Flow and Cross-market Portfolio Rebalancing: Evidence for the Chinese Stock, Treasury and Corporate Bond Markets By Eric Girardin; Dijun Tan; Woon K. Wong
  4. Vast Volatility Matrix Estimation using High Frequency Data for Portfolio Selection By Jianqing Fan; Yingying Li; Ke Yu
  5. InsiderTrading, Option Exercises and Private Benefits of Control By Cziraki, P.; Goeij, P. C. de; Renneboog, L.D.R.
  6. Smooth Transition Patterns in the Realized Stock Bond Correlation By Nektarios Aslanidis; Charlotte Christiansen

  1. By: Magdalena Malinowska (European Central Bank, Directorate General Economics, Capital Markets and Financial Structure Division, Kaiserstrasse 29, 60311 Frankfurt am Main, Germany.)
    Abstract: In their seminal paper French and Roll (1986) postulate that public information affects prices before anyone can trade on it. In contrast, several models assuming heterogeneous investors show that public news releases are directly followed by high trading volume. Empirical evidence on this question is still mixed, primarily due to the lack of sufficiently precise data. This paper examines the process of price adjustment to public news in an electronic limit order market, based on very precise information from the largest European bond futures market. The results show that the price response to public news is gradual and accompanied by trading. Good (bad) news releases are followed by a sequence of positive (negative) returns and a large buying (selling) activity in the first seconds after the news release. JEL Classification: E44, G14.
    Keywords: information processing, market microstructure, macroeconomic announcements, price adjustment.
    Date: 2010–04
  2. By: Skjeltorp, Johannes A (Norges Bank); Odegaard, Bernt Arne (University of Stavanger)
    Abstract: In recent years, a number of electronic limit order have reintroduced market makers for some securities (Designated Market Makers). This trend has mainly been initiated by financial intermediaries and listed firms themselves, without any regulatory pressure. In this paper we ask why firms are willing to pay to improve the secondary market liquidity of its shares. We show that a contributing factor in this decision is the likelihood that the firm will interact with the capital markets in the near future, either because they have capital needs, or that they are planning to repurchase shares. We also find some evidence of agency costs, managers desiring good liquidity when they plan insider trades.
    Keywords: Market microstructure; Corporate Finance; Designated Market Makers; Insider Trading
    JEL: G10 G20
    Date: 2010–04–28
  3. By: Eric Girardin (GREQAM ,Universitˆm de la Mˆmditerranˆme-Aix Marseille II ,Hong Kong Institute for Monetary Research); Dijun Tan (GREQAM ,Universitˆm de la Mˆmditerranˆme-Aix Marseille II ,University of Electronic Science and Technology of China); Woon K. Wong (University of the West of England)
    Abstract: This paper examines the within-market and cross-market information content of order flow for stocks, corporate bonds and Treasury bonds in China. With daily-aggregated tick-by-tick data over three years on the Shanghai Security Exchange, we find negative cross-asset effects of order flow on returns, both between stocks and bonds and between corporate and Treasury bonds. Our results provide evidence that not only cross-market portfolio rebalancing under general market conditions, but also flight-to-quality, which occurs particularly under extreme market conditions, are responsible for the cross-market effects of order flow. In particular, while Treasury bonds play a dominant role in stock-bond portfolio rebalancing, corporate bonds can replace Treasury bonds as a safe ¡§haven¡¨ during extreme stock market conditions or during a fall in Treasury bond market returns.
    Keywords: Order Flow, Portfolio Rebalancing, Flight-to-quality, Chinese Financial Markets
    JEL: G10 G11 G12 G14
    Date: 2010–01
  4. By: Jianqing Fan; Yingying Li; Ke Yu
    Abstract: Portfolio allocation with gross-exposure constraint is an effective method to increase the efficiency and stability of selected portfolios among a vast pool of assets, as demonstrated in Fan et al (2008). The required high-dimensional volatility matrix can be estimated by using high frequency financial data. This enables us to better adapt to the local volatilities and local correlations among vast number of assets and to increase significantly the sample size for estimating the volatility matrix. This paper studies the volatility matrix estimation using high-dimensional high-frequency data from the perspective of portfolio selection. Specifically, we propose the use of "pairwise-refresh time" and "all-refresh time" methods proposed by Barndorff-Nielsen et al (2008) for estimation of vast covariance matrix and compare their merits in the portfolio selection. We also establish the concentration inequalities of the estimates, which guarantee desirable properties of the estimated volatility matrix in vast asset allocation with gross exposure constraints. Extensive numerical studies are made via carefully designed simulations. Comparing with the methods based on low frequency daily data, our methods can capture the most recent trend of the time varying volatility and correlation, hence provide more accurate guidance for the portfolio allocation in the next time period. The advantage of using high-frequency data is significant in our simulation and empirical studies, which consist of 50 simulated assets and 30 constituent stocks of Dow Jones Industrial Average index.
    Date: 2010–04
  5. By: Cziraki, P.; Goeij, P. C. de; Renneboog, L.D.R. (Tilburg University, Center for Economic Research)
    Abstract: We investigate patterns of abnormal stock performance around insider trades and option exercises on the Dutch market. Listed firms in the Netherlands have a long tradition of employing many anti-shareholder mechanisms limiting shareholders rights. Our results imply that insider transactions are more profitable at firms where shareholder rights are not restricted by antishareholder mechanisms. This finding goes against the monitoring hypothesis which states that more shareholder orientation and stronger blockholders would reduce the gains from insider trading. We show robust support for the substitution hypothesis as insiders of firms which effectively curtail shareholder rights enjoy valuable private benefits of control in lieu of engaging in insider trading to exploit their position.
    Keywords: insider trading;management stock options;timing by insiders;corporate governance;antishareholder mechanisms;anti-takeover mechanisms.
    JEL: G14 G34 M52
    Date: 2010
  6. By: Nektarios Aslanidis (Department of Economics, FCEE, University Rovira Virgili); Charlotte Christiansen (School of Economics and Management, Aarhus University and CREATES)
    Abstract: This paper re-examines the joint distribution of equity and bond returns using high frequency data. In particular, we analyze the weekly realized stock bond correlation calculated from 5-minute returns of the futures prices of the S&P 500 and the 10-year Treasury Note. A potentially gradual transition in the realized correlation is accommodated by regime switching smooth transition regressions. The regimes are defined by the VIX/VXO volatility index and the model includes additional economic and financial explanatory variables. The empirical results show that the smooth transition model has a better fit than a linear model at forecasting in sample, whereas the linear model is more accurate for out-of-sample forecasting. It is also shown that it is important to account for differences between positive and negative realized stock bond correlations.
    Keywords: realized correlation, smooth transition regressions, stock bond correlation, VIX index
    JEL: C22 G11
    Date: 2010–04–26

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