Do algorithmic traders exploit volatility?

Devika Arumugam, P. Krishna Prasanna and Rahul R. Marathe

Journal of Behavioral and Experimental Finance, 2023, vol. 37, issue C

Abstract: This study examines the impact of trading by Algorithmic Traders (ATs) and Non-Algorithmic Traders (NATs) on volatility, and conversely, the impact of volatility shocks on ATs and Non-ATs. ATs are classified as High-Frequency Traders (HFTs) and Buy-side Algorithmic Traders (BATs). Using jump robust volatility estimates, we find that excessive directional and non-directional trading by BATs and HFTs increases volatility, whereas that by NATs marginally decreases volatility. Conversely, all traders increase their non-directional trading one hour following a volatility shock. BATs carry out more directional trades during a volatility shock, whereas HFTs withdraw from such activities.

Keywords: Algorithmic trading; High-frequency trading; Volatility (search for similar items in EconPapers)
JEL-codes: G12 G14 G20 G23 G40 (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:beexfi:v:37:y:2023:i:c:s2214635022001009

DOI: 10.1016/j.jbef.2022.100778

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