Real-Time Detection of Volatility in Liquidity Provision

TL;DR

This paper develops real-time methods using a four-state Markov model to detect rapid liquidity changes in securities, helping market participants avoid increased price uncertainty caused by high-frequency trading.

Contribution

It introduces a novel four-state Markov switching model for real-time detection of rapid liquidity variations, enabling delay signals to mitigate trading risks.

Findings

Identifies a rapid liquidity variation state with the Markov model.

Signals can delay orders for tens of milliseconds.

Delays occur over 0-10% of trading day, reducing price uncertainty.

Abstract

Previous research has found that high-frequency traders will vary the bid or offer price rapidly over periods of milliseconds. This is a benefit to fast traders who can time their trades with microsecond precision, however it is a cost to the average market participant due to increased trade execution price uncertainty. In this analysis we attempt to construct real-time methods for determining whether the liquidity of a security is being altered rapidly. We find a four-state Markov switching model identifies a state where liquidity is being rapidly varied about a mean value. This state can be used to generate a signal to delay market participant orders until the price volatility subsides. Over our sample, the signal would delay orders, in aggregate, over 0 to 10% of the trading day. Each individual delay would only last tens of milliseconds, and so would not be noticeable by the average market participant.