Abstract
Recent literature on realized volatility suggests that the observed price process of an asset may be decomposed into two parts: the genuine (unobservable) price process and microstructure noise. In this article we present a methodology to estimate stochastic volatility by separating these components. Depending on market liquidity, the source of a move in the transaction price of an asset may be distinguished as a move in the underlying price process or as microstructure noise. To identify the weights of these components we use different (order-based and trade-based) liquidity measures to update expectations on the unobservable price process via Bayesian learning. Depending on these weights we are able to estimate stochastic volatility from noisy data.
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