nep-mst New Economics Papers
on Market Microstructure
Issue of 2009‒12‒19
seven papers chosen by
Thanos Verousis
Swansea University

  1. Quantifying High-Frequency Market Reactions to Real-Time News Sentiment Announcements By Axel Groß-Klußmann; Nikolaus Hautsch
  2. Bias-Corrected Realized Variance under Dependent Microstructure Noise By Kosuke Oya
  3. High-Frequency and Model-Free Volatility Estimators By Robert Ślepaczuk; Grzegorz Zakrzewski
  4. "Realized Volatility Risk" By David E. Allen; Michael McAleer; Marcel Scharth
  5. The information content of market liquidity: An empirical analysis of liquidity at the Oslo Stock Exchange? By Johannes A. Skjeltorp; Bernt Arne Ødegaard
  6. Disclosure requirements, the release of new information and market efficiency: new insights from agent-based models By Hermsen, Oliver; Witte, Björn-Christopher; Westerhoff, Frank
  7. Macroeconomic News, Announcements, and Stock Market Jump Intensity Dynamics By José Gonzalo Rangel

  1. By: Axel Groß-Klußmann; Nikolaus Hautsch
    Abstract: We examine intra-day market reactions to news in stock-specific sentiment disclosures. Using pre-processed data from an automated news analytics tool based on linguistic pattern recognition we extract information on the relevance as well as the direction of company-specific news. Information-implied reactions in returns, volatility as well as liquidity demand and supply are quantified by a high-frequency VAR model using 20 second intervals. Analyzing a cross-section of stocks traded at the London Stock Exchange (LSE), we find market-wide robust news-dependent responses in volatility and trading volume. However, this is only true if news items are classified as highly relevant. Liquidity supply reacts less distinctly due to a stronger influence of idiosyncratic noise. Furthermore, evidence for abnormal highfrequency returns after news in sentiments is shown.
    Keywords: firm-specific news, news sentiment, high-frequency data, volatility, liquidity, abnormal returns
    JEL: G14 C32
    Date: 2009–12
  2. By: Kosuke Oya (Graduate School of Economics, Osaka University, Toyonaka, Osaka, Japan. Japan Science and Technology Agency, CREST, Toyonaka , Osaka, Japan.)
    Abstract: The aim of this study is to develop a bias-correction method for realized variance (RV) estimation, where the equilibrium price process is contaminated with market microstructure noise, such as bid-ask bounces and price changes discreteness. Though RV constitutes the simplest estimator of daily integrated variance, it remains strongly biased and many estimators proposed in previous studies require prior knowledge about the dependence structure of microstructure noise to ensure unbiasedness and consistency. The dependence structure is unknown however and it needs to be estimated. A bias-correction method based on statistical inference from the general noise dependence structure is thus proposed. The results of Monte Carlo simulation indicate that the new approach is robust with respect to changes in the dependence of microstructure noise.
    Keywords: Realized variance; Dependent microstructure noise; Two-time scales
    JEL: C01 C13 C51
    Date: 2009–11
  3. By: Robert Ślepaczuk (Faculty of Economic Sciences, University of Warsaw); Grzegorz Zakrzewski (Deutsche Bank PBC S.A.)
    Abstract: This paper focuses on volatility of financial markets, which is one of the most important issues in finance, especially with regard to modeling high-frequency data. Risk management, asset pricing and option valuation techniques are the areas where the concept of volatility estimators (consistent, unbiased and the most efficient) is of crucial concern. Our intention was to find the best estimator of true volatility taking into account the latest investigations in finance literature. Basing on the methodology presented in Parkinson (1980), Garman and Klass (1980), Rogers and Satchell (1991), Yang and Zhang (2000), Andersen et al. (1997, 1998, 1999a, 199b), Hansen and Lunde (2005, 2006b) and Martens (2007), we computed the various model-free volatility estimators and compared them with classical volatility estimator, most often used in financial models. In order to reveal the information set hidden in high-frequency data, we utilized the concept of realized volatility and realized range. Calculating our estimator, we carefully focused on Δ (the interval used in calculation), n (the memory of the process) and q (scaling factor for scaled estimators). Our results revealed that the appropriate selection of Δ and n plays a crucial role when we try to answer the question concerning the estimator efficiency, as well as its accuracy. Having nine estimators of volatility, we found that for optimal n (measured in days) and Δ (in minutes) we obtain the most efficient estimator. Our findings confirmed that the best estimator should include information contained not only in closing prices but in the price range as well (range estimators). What is more important, we focused on the properties of the formula itself, independently of the interval used, comparing the estimator with the same Δ, n and q parameter. We observed that the formula of volatility estimator is not as important as the process of selection of the optimal parameter n or Δ. Finally, we focused on the asymmetry between market turmoil and adjustments of volatility. Next, we put stress on the implications of our results for well-known financial models which utilize classical volatility estimator as the main input variable.
    Keywords: financial market volatility, high-frequency financial data, realized volatility and correlation, volatility forecasting, microstructure bias, the opening jump effect, the bid-ask bounce, autocovariance bias, daily patterns of volatility, emerging markets
    JEL: G14 G15 C61 C22
    Date: 2009
  4. By: David E. Allen (School of Accounting, Finance and Economics, Edith Cowan University); Michael McAleer (Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam and Tinbergen Institute); Marcel Scharth (VU University Amsterdam and Tinbergen Institute)
    Abstract: In this paper we document that realized variation measures constructed from high-frequency returns reveal a large degree of volatility risk in stock and index returns, where we characterize volatility risk by the extent to which forecasting errors in realized volatility are substantive. Even though returns standardized by ex post quadratic variation measures are nearly gaussian, this unpredictability brings considerably more uncertainty to the empirically relevant ex ante distribution of returns. Carefully modeling this volatility risk is fundamental. We propose a dually asymmetric realized volatility (DARV) model, which incorporates the important fact that realized volatility series are systematically more volatile in high volatility periods. Returns in this framework display time varying volatility, skewness and kurtosis. We provide a detailed account of the empirical advantages of the model using data on the S&P 500 index and eight other indexes and stocks.
    Date: 2009–12
  5. By: Johannes A. Skjeltorp (Norges Bank); Bernt Arne Ødegaard (University of Stavanger and Norges Bank)
    Abstract: We investigate the information content of aggregate stock market liquidity and ask whether it may be a useful realtime indicator, both for financial stress, and real economic activity in Norway. We describe the development in a set of liquidity proxies at the Oslo Stock Exchange (OSE) for the period 1980-2008, with particular focus on crisis period 2007 through 2008, showing how market liquidity and trading activity changed for the whole market as well as for individual industry sectors. We also evaluate the predictive power of market liquidity for economic growth both in-sample and out-of-sample.
    Keywords: Liquidity, Business Cycles, Financial crisis, Economic Activity
    JEL: G10 G20
    Date: 2009–11–25
  6. By: Hermsen, Oliver; Witte, Björn-Christopher; Westerhoff, Frank
    Abstract: We explore how disclosure requirements that regulate the release of new information may affect the dynamics of financial markets. Our analysis is based on three agentbased financial market models that are able to produce realistic financial market dynamics. We discover that the average deviation between market prices and fundamental values increases if new information is released with a delay, while the average price volatility is virtually unaffected by such regulations. Interestingly, the tails of the distribution of returns become fatter if fundamental data is released less continuously, indicating an increase in financial market risk. --
    Keywords: Agent-based financial market models,market efficiency,release of new information,disclosure requirements,regulation of financial markets,Monte Carlo analysis
    JEL: G14 G18
    Date: 2009
  7. By: José Gonzalo Rangel
    Abstract: This paper examines the effect of macroeconomic releases on stock market volatility through a Poisson-Gaussian-GARCH process with time varying jump intensity, which is allowed to respond to such information. It is found that the day of the announcement, per se, has little impact on jump intensities. Employment releases are an exception. However, when macroeconomic surprises are considered, inflation shocks show persistent effects while monetary policy and employment shocks show only short-lived effects. Also, the jump intensity responds asymmetrically to macroeconomic shocks. Evidence that macroeconomic variables are relevant to explain jump dynamics and improve volatility forecasts on event days is provided.
    Keywords: Conditional jump intensity, conditional volatility, macroeconomic announcements.
    JEL: C22 G14
    Date: 2009–12

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