Real-Time Detection of Volatility in Liquidity Provision
Abstract
Previous research has found that high-frequency traders will vary the bid or offer price rapidly overperiods of milliseconds. This is a benefit to fast traders who can time thier trades with microsecondprecision, however it is a cost to the average market participant due to increased trade execution priceuncertainty. In this analysis we attempt to construct real-time methods for determining whether theliquidity of a security is being altered by high-frequency traders. We find a four-state Markov switchingmodel identifies a state consistent with high-frequency traders affecting liquidity. Moreover, we find thisstate is positicely corrrelated with the prediction error from a deep neural network. This state can beused as a signal to delay market participant orders until the price volatility subsides. This delay wouldonly last tens of milliseconds, and so would not be noticable by the average market participant.
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Copyright (c) 2020 Matt Brigida
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