Publication: Volatility and covariation of financial assets: a high-frequency analysis
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2009-12
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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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Volatility estimation, High-frequency data, Market microstructure theory, Covariation of assets, Matrix process, Kalman filter