@article{Brigida_2020, title={Real-Time Detection of Volatility in Liquidity Provision}, volume={9}, url={https://ojs.aut.ac.nz/applied-finance-letters/article/view/268}, DOI={10.24135/afl.v9i.268}, abstractNote={<p><span style="left: 184.55px; top: 321.079px; font-size: 14.944px; font-family: sans-serif; transform: scaleX(0.896598);">Previous research has found that high-frequency traders will vary the bid or offer price rapidly over</span><span style="left: 161.512px; top: 339.344px; font-size: 14.944px; font-family: sans-serif; transform: scaleX(0.925607);">periods of milliseconds. This is a benefit to fast traders who can time thier trades with microsecond</span><span style="left: 161.512px; top: 357.609px; font-size: 14.944px; font-family: sans-serif; transform: scaleX(0.890132);">precision, however it is a cost to the average market participant due to increased trade execution price</span><span style="left: 161.512px; top: 375.874px; font-size: 14.944px; font-family: sans-serif; transform: scaleX(0.91707);">uncertainty. In this analysis we attempt to construct real-time methods for determining whether the</span><span style="left: 161.512px; top: 394.137px; font-size: 14.944px; font-family: sans-serif; transform: scaleX(0.894707);">liquidity of a security is being altered by high-frequency traders. We find a four-state Markov switching</span><span style="left: 161.512px; top: 412.402px; font-size: 14.944px; font-family: sans-serif; transform: scaleX(0.882245);">model identifies a state consistent with high-frequency traders affecting liquidity. Moreover, we find this</span><span style="left: 161.512px; top: 430.667px; font-size: 14.944px; font-family: sans-serif; transform: scaleX(0.913478);">state is positicely corrrelated with the prediction error from a deep neural network. This state can be</span><span style="left: 161.512px; top: 448.932px; font-size: 14.944px; font-family: sans-serif; transform: scaleX(0.898167);">used as a signal to delay market participant orders until the price volatility subsides. This delay would</span><span style="left: 161.512px; top: 467.197px; font-size: 14.944px; font-family: sans-serif; transform: scaleX(0.883383);">only last tens of milliseconds, and so would not be noticable by the average market participant.</span></p&gt;}, journal={Applied Finance Letters}, author={Brigida, Matt}, year={2020}, month={Dec.}, pages={143 - 156} }