Abstract
Drift and volatility are two mainsprings of asset price dynamics. While volatilities have been studied extensively in the literature, drifts are commonly believed to be impossible to estimate and largely ignored in the literature. This paper shows how to detect drift using realized autocovariance implemented on high-frequency data. We use a theoretical treatment in which the classical model for the efficient price, an Itō semimartingale possibly contaminated by microstructure noise, is enriched with drift and volatility explosions. Our theory advocates a novel decomposition for realized variance into a drift and a volatility component, which leads to significant improvements in volatility forecasting.
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