New Economics Papers
on Market Microstructure
Issue of 2013‒09‒24
two papers chosen by
Thanos Verousis

  1. Bootstrapping pre-averaged realized volatility under market microstructure noise By Ulrich Hounyo; Sílvia Goncalves; Nour Meddahi
  2. Modeling of Stock Returns and Trading Volume By Taisei Kaizoji

  1. By: Ulrich Hounyo (Oxford-Man Institute of Quantitative Finance and CREATES); Sílvia Goncalves (Département de sciences économiques, CIREQ and CIRANO, Université de Montréal); Nour Meddahi (Toulouse School of Economics)
    Abstract: The main contribution of this paper is to propose a bootstrap method for inference on integrated volatility based on the pre-averaging approach of Jacod et al. (2009), where the pre-averaging is done over all possible overlapping blocks of consecutive observations. The overlapping nature of the pre-averaged returns implies that these are kn-dependent with kn growing slowly with the sample size n. This motivates the application of a blockwise bootstrap method. We show that the "blocks of blocks" bootstrap method suggested by Politis and Romano (1992) (and further studied by Bühlmann and Künsch (1995)) is valid only when volatility is constant. The failure of the blocks of blocks bootstrap is due to the heterogeneity of the squared pre-averaged returns when volatility is stochastic. To preserve both the dependence and the heterogeneity of squared pre-averaged returns, we propose a novel procedure that combines the wild bootstrap with the blocks of blocks bootstrap. We provide a proof of the first order asymptotic validity of this method for percentile intervals. Our Monte Carlo simulations show that the wild blocks of blocks bootstrap improves the finite sample properties of the existing first order asymptotic theory. We use empirical work to illustrate its use in practice.
    Keywords: High frequency data, realized volatility, pre-averaging, market microstructure noise, wild bootstrap, block bootstrap
    JEL: C15 C22 C58
    Date: 2013–08–29
  2. By: Taisei Kaizoji
    Abstract: In this study, we investigate the statistical properties of the returns and the trading volume. We show a typical example of power-law distributions of the return and of the trading volume. Next, we propose an interacting agent model of stock markets inspired from statistical mechanics [24] to explore the empirical findings. We show that as the interaction among the interacting traders strengthens both the returns and the trading volume present power-law behavior.
    Date: 2013–09

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