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

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  • Cartea, Álvaro
  • Karyampas, Dimitrios

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.

Suggested Citation

  • Cartea, Álvaro & Karyampas, Dimitrios, 2009. "Volatility and covariation of financial assets: a high-frequency analysis," DEE - Working Papers. Business Economics. WB wb097609, Universidad Carlos III de Madrid. Departamento de Economía de la Empresa.
  • Handle: RePEc:cte:wbrepe:wb097609
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Anatoly A. Peresetsky & Ruslan I. Yakubov, 2017. "Autocorrelation in an unobservable global trend: does it help to forecast market returns?," International Journal of Computational Economics and Econometrics, Inderscience Enterprises Ltd, vol. 7(1/2), pages 152-169.
    2. 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.
    3. repec:eee:ecosta:v:5:y:2018:i:c:p:67-82 is not listed on IDEAS
    4. repec:eee:econom:v:201:y:2017:i:1:p:19-42 is not listed on IDEAS
    5. Durdyev, Ruslan & Peresetsky, Anatoly, 2014. "Autocorrelation in the global stochastic trend," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 35(3), pages 39-58.
    6. Grigoryeva, Lyudmila & Ortega, Juan-Pablo & Peresetsky, Anatoly, 2018. "Volatility forecasting using global stochastic financial trends extracted from non-synchronous data," Econometrics and Statistics, Elsevier, vol. 5(C), pages 67-82.

    More about this item

    Keywords

    Volatility estimation;

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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