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Volatility and Covariation of Financial Assets: A High-Frequency Analysis

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  • Álvaro Cartea
  • Dimitrios Karyampas

    (Department of Economics, Mathematics & Statistics, Birkbeck)

Abstract

Using high frequency data for the price dynamics of equities we measure the impact that market microstructure noise has on estimates of the: (i) volatility of returns; and (ii) variance-covariance matrix of n. assets. We propose a Kalman-filter-based methodology that allows us to deconstruct price series into the true effcient price and the microstructure noise. This approach allows us to employ volatility estimators that achieve very low Root Mean Squared Errors (RMSEs) compared to other estimators that have been proposed to deal with market microstructure noise at high frequencies. Furthermore, this price series decomposition allows us to estimate the variance covariance matrix of n assets in a more efficient way than the methods so far proposed in the literature. We illustrate our results by calculating how microstructre noise affects portfolio decisions and calculations of the equity beta in a CAPM setting.

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File URL: http://www.ems.bbk.ac.uk/research/wp/PDF/BWPEF0913.pdf
File Function: First version, 2009
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Bibliographic Info

Paper provided by Birkbeck, Department of Economics, Mathematics & Statistics in its series Birkbeck Working Papers in Economics and Finance with number 0913.

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Date of creation: Oct 2009
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Handle: RePEc:bbk:bbkefp:0913

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Cited by:
  1. Korhonen, Iikka & Peresetsky , Anatoly, 2013. "Extracting global stochastic trend from non-synchronous data," BOFIT Discussion Papers 15/2013, Bank of Finland, Institute for Economies in Transition.

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