New Economics Papers
on Market Microstructure
Issue of 2011‒10‒01
five papers chosen by
Thanos Verousis

  1. The Relationship between Volatility and Trading Volume in the Chinese Stock Market: A Volatility Decomposition Perspective By Tianyi Wang; Zhuo Huang
  2. A semi-Markov model with memory for price changes By Guglielmo D'Amico; Filippo Petroni
  3. Asymmetric Information and the Foreign-Exchange Trades of Global Custody Banks By Carol Osler; Thang Nguyen; Tanseli Savaser
  4. The Capital Asset Pricing Model as a corollary of the Black-Scholes model By Vladimir Vovk
  5. Forecasting volatility: does continuous time do better than discrete time? By Carles Bretó; Helena Veiga

  1. By: Tianyi Wang (China Center for Economic Research, National School of Development, Peking University); Zhuo Huang (China Center for Economic Research, National School of Development, Peking University)
    Abstract: We use heterogeneous autoregressive (HAR) model with high-frequency data of Hu-Shen 300 index to investigate the volatility-volume relationship via the volatility decomposition approach. Although we find that the continuous component of daily volatility is positively correlated with trading volume, the jump component reveals a significant and robust negative relation with volume. This result suggests that the jump component contains some "public information" while the continuous components are more likely driven by "private information". Discussion of the intertemporal relationship supports the information-driven trading hypothesis. Lagged realized skewness only significantly affects the continuous component.
    Keywords: High Frequency, Price Jump, Trading Volume
    JEL: G10 G12 G14
    Date: 2011
  2. By: Guglielmo D'Amico; Filippo Petroni
    Abstract: We study the high frequency price dynamics of traded stocks by a model of returns using a semi-Markov approach. More precisely we assume that the intraday returns are described by a discrete time homogeneous semi-Markov which depends also on a memory index. The index is introduced to take into account periods of high and low volatility in the market. First of all we derive the equations governing the process and then theoretical results have been compared with empirical findings from real data. In particular we analyzed high frequency data from the Italian stock market from first of January 2007 until end of December 2010.
    Date: 2011–09
  3. By: Carol Osler (Brandeis International Business School); Thang Nguyen (Brandeis International Business School); Tanseli Savaser (Williams College)
    Abstract: This paper provides the first rigorous empirical analysis of markups on custodial foreign exchange trades. It finds that they substantially exceed relevant benchmarks such as interbank half-spreads. We trace this to an information asymmetry -- custodial bank dealers know more about their prices and bid-ask spreads than their client funds. We also examine the asset managers’ continued heavy reliance on this high-cost approach to trading when alternatives are available with lower markups. We provide evidence that this choice does not reflect ignorance of the cost differential. Analysis relies on the complete foreign exchange trading record of a mid-sized global custody bank during calendar year 2006.
    Date: 2011–06
  4. By: Vladimir Vovk
    Abstract: We consider a financial market in which two securities are traded: a stock and an index. Their prices are assumed to satisfy the Black-Scholes model. Besides assuming that the index is a tradable security, we also assume that it is efficient, in the following sense: we do not expect a prespecified self-financing trading strategy whose wealth is almost surely nonnegative at all times to outperform the index greatly. We show that, for a long investment horizon, the appreciation rate of the stock has to be close to the interest rate (assumed constant) plus the covariance between the volatility vectors of the stock and the index. This contains both a version of the Capital Asset Pricing Model and our earlier result that the equity premium is close to the squared volatility of the index.
    Date: 2011–09
  5. By: Carles Bretó; Helena Veiga
    Abstract: In this paper we compare the forecast performance of continuous and discrete-time volatility models. In discrete time, we consider more than ten GARCH-type models and an asymmetric autoregressive stochastic volatility model. In continuous-time, a stochastic volatility model with mean reversion, volatility feedback and leverage. We estimate each model by maximum likelihood and evaluate their ability to forecast the two scales realized volatility, a nonparametric estimate of volatility based on highfrequency data that minimizes the biases present in realized volatility caused by microstructure errors. We find that volatility forecasts based on continuous-time models may outperform those of GARCH-type discrete-time models so that, besides other merits of continuous-time models, they may be used as a tool for generating reasonable volatility forecasts. However, within the stochastic volatility family, we do not find such evidence. We show that volatility feedback may have serious drawbacks in terms of forecasting and that an asymmetric disturbance distribution (possibly with heavy tails) might improve forecasting.
    Keywords: Asymmetry, Continuous and discrete-time stochastic volatility models, GARCH-type models, Maximum likelihood via iterated filtering, Particle filter, Volatility forecasting
    Date: 2011–07

This issue is ©2011 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.
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