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Volatility and covariation of financial assets: a high-frequency analysis

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  • Alvaro Cartea

    ()

  • Dimitrios Karyampas

    ()

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 efficient 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 microstructure noise affects portfolio decisions and calculations of the equity beta in a CAPM setting.

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Bibliographic Info

Paper provided by Universidad Carlos III, Departamento de Economía de la Empresa in its series Business Economics Working Papers with number wb097609.

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Date of creation: Dec 2009
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Handle: RePEc:cte:wbrepe:wb097609

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Keywords: Volatility estimation; High-frequency data; Market microstructure theory; Covariation of assets; Matrix process; Kalman filter;

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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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