New Economics Papers
on Market Microstructure
Issue of 2008‒05‒17
four papers chosen by
Thanos Verousis

  1. Empirical Market Microstructure: An Analysis Of The Brl/Us$ Exchange Rate Market Using High-Frequency Data By Laurini, Márcio P. & Furlani, Luiz G. C. & Portugual, Marcelo S.
  2. Decimalization, Realized Volatility, and Market Microstructure Noise By Vuorenmaa, Tommi A.
  3. Measuring and Analyzing the Liquidity of the Italian Treasury Security Wholesale Secondary Market By Coluzzi, Chiara; Ginebri, Sergio; Turco, Manuel
  4. Comparison of Volatility Measures: a Risk Management Perspective By Christian T. Brownlees; Giampiero Gallo

  1. By: Laurini, Márcio P. & Furlani, Luiz G. C. & Portugual, Marcelo S.
    Date: 2008–10
  2. By: Vuorenmaa, Tommi A.
    Abstract: This paper studies empirically the effect of decimalization on volatility and market microstructure noise. We apply several non-parametric estimators in order to accurately measure volatility and market microstructure noise variance before and after the final stage of decimalization which, on the NYSE, took place in January, 2001. We find that decimalization decreased observed volatility by decreasing noise variance and, consequently, increased the significance of the true signal especially in the trade price data for the high-activity stocks. In general, however, most of the found increase in the signal-to-noise ratio is explainable by confounding and random effects. We also find that although allowing for dependent noise can matter pointwisely, it does not appear to be critical in our case where the estimates are averaged over time and across stocks. For that same reason rare random jumps are not critical either. It is more important to choose a proper data type and prefilter the data carefully.
    Keywords: Decimalization; Market microstructure noise; Realized volatility; Realized variance; Tick size; Ultra-high-frequency data
    JEL: C14 C19
    Date: 2008–04
  3. By: Coluzzi, Chiara; Ginebri, Sergio; Turco, Manuel
    Abstract: Although its importance, only recently the issue of liquidity in Treasury markets has received greater attention. We survey the literature about market liquidity and liquidity measures, and we put forward new measures. The aim is to provide a description of the liquidity of the Italian wholesale secondary market, which we describe thoroughly. We apply a large set of measures on a unique dataset, which gives us a complete view of the market. Even though the market provides an amount of liquidity that fits the market needs, the quality of the order book is low, and despite the presence of a large number of market makers, the degree of competition among them is not very high. Moreover, no clear and general relationship emerges between trading and order book measures. Indeed, even though trading activity is higher for on-the-run securities with respect to the off-the-run securities, there is not a sharp difference in terms of liquidity of the order book between them. In this case market regulation plays an important role. Finally, we investigate how long it takes for a new issue to become the benchmark for its segment. Our evidence shows that some modifications of the issuance policy in order to have a larger outstanding since the first auction could help securities in gaining earlier their benchmark status, especially in case of 10-year BTPs.
    Keywords: Liquidity, liquidity measures, Government securities, market microstructure, benchmark status.
    JEL: D49 G12 H63
    Date: 2008–05–11
  4. By: Christian T. Brownlees (Università degli Studi di Firenze, Dipartimento di Statistica); Giampiero Gallo (Università degli Studi di Firenze, Dipartimento di Statistica "G. Parenti")
    Abstract: In this paper we address the issue of forecasting Value–at–Risk (VaR) using different volatility measures: realized volatility, bipower realized volatility, two scales realized volatility, realized kernel as well as the daily range. We propose a dynamic model with a flexible trend specification bonded with a penalized maximum likelihood estimation strategy: the P-Spline Multiplicative Error Model. Exploiting UHFD volatility measures, VaR predictive ability is considerably improved upon relative to a baseline GARCH but not so relative to the range; there are relevant gains from modeling volatility trends and using realized kernels that are robust to dependent microstructure noise.
    Keywords: Volatility Measures, VaR Forecasting, GARCH, MEM, P-Spline.
    JEL: C22 C51 C52 C53
    Date: 2008–02

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