New Economics Papers
on Market Microstructure
Issue of 2009‒03‒28
three papers chosen by
Thanos Verousis

  1. Range-based covariance estimation using high-frequency data: The realized co-range By Bannouh, K.; Dijk, D.J.C. van; Martens, M.P.E.
  2. A State Space Approach to Estimating the Integrated Variance and Microstructure Noise Component By Daisuke Nagakura; Toshiaki Watanabe
  3. Funding Liquidity Risk: Definition and Measurement. By Mathias Drehmann; Kleopatra Nikolaou

  1. By: Bannouh, K.; Dijk, D.J.C. van; Martens, M.P.E. (Erasmus Econometric Institute)
    Abstract: We introduce the realized co-range, utilizing intraday high-low price ranges to estimate asset return covariances. Using simulations we find that for plausible levels of bid-ask bounce and infrequent and non-synchronous trading the realized co-range improves upon the realized covariance, which uses cross-products of intraday returns. One advantage of the co-range is that in an ideal world it is five times more efficient than the realized covariance when sampling at the same frequency. The second advantage is that the upward bias due to bid-ask bounce and the downward bias due to infrequent and non-synchronous trading partially offset each other. In a volatility timing strategy for S\&P500, bond and gold futures we find that the co-range estimates are less noisy as exemplified by lower transaction costs and also higher Sharpe ratios when using more weight on recent data for predicting covariances.
    Keywords: realized covariance;realized co-range;high-frequency date;market microstructure noise;bias-correction
    Date: 2008–01–15
  2. By: Daisuke Nagakura (Institute for Monetary and Economic Studies, Bank of Japan (E-mail:; Toshiaki Watanabe (Professor, Institute of Economic Research, Hitotsubashi University, and Institute for Monetary and Economic Studies, Bank of Japan (E-mail:,
    Abstract: We call the realized variance (RV) calculated with observed prices contaminated by microstructure noises (MNs) the noise-contaminated RV (NCRV) and refer to the component in the NCRV associated with the MNs as the MN component. This paper develops a method for estimating the integrated variance (IV) and MN component simultaneously, extending the state space method proposed by Barndorff-Nielsen and Shephard (2002). Our extension is based on the result obtained in Meddahi (2003), namely, when the true log-price process follows a general class of continuous-time stochastic volatility (SV) models, the IV follows an ARMA process. 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 fewer identifiable parameters. We illustrate how to estimate these parameters. The proposed method is applied to yen/dollar exchange rate data. We find that the magnitude of the MN component is, on average, about 21%-48 % of the NCRV, depending on the sampling frequency.
    Keywords: Realized Variance, Integrated Variance, Microstructure Noise
    JEL: C0 G0
    Date: 2009–03
  3. By: Mathias Drehmann (Bank for International Settlements, Centralbahnplatz 2, CH-4002 Basel, Switzerland.); Kleopatra Nikolaou (European Central Bank, Kaiserstrasse 29, D-60311 Frankfurt am Main, Germany.)
    Abstract: In this paper we propose definitions of funding liquidity and funding liquidity risk and present a simple, yet intuitive, measure of funding liquidity risk based on data from open market operations. Our empirical analysis uses a unique data set of 135 main refinancing operation auctions conducted at the ECB between June 2005 and December 2007. We find that our proxies for funding liquidity risk are typically stable and low, with occasional spikes, especially during the recent turmoil. We are also able to document downward spirals between funding liquidity risk and market liquidity. JEL Classification: E58, G21.
    Keywords: funding liquidity, liquidity risk, bidding data, money market auctions, interbank markets.
    Date: 2009–03

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