TY - JOUR
T1 - Analysis of rare events using multidimensional liquidity measures
AU - Zaika, Margarita
AU - Bozdog, Dragos
AU - Florescu, Ionut
N1 - Publisher Copyright:
© 2024 Elsevier Inc.
PY - 2024/10
Y1 - 2024/10
N2 - In this paper we develop a framework to analyze high-frequency (HF) financial transaction data focused on estimating a multidimensional intraday liquidity measure and detecting rare events. Many liquidity measures based on Trade and Quote (TAQ) and Limit Order Book (LOB) datasets are consolidated for this purpose through dimensionality reduction techniques. Several outlier methods based on extreme value theory, distance-based outlier methods, and tree-based algorithms are implemented to identify clusters of rare liquidity events. These methods provide insights into the behavior and occurrence of outliers. The methodology is optimized for HF intraday implementation. The framework is applied to transaction level data covering the beginning of COVID-19 outbreak period. We observe that after peak news activity, high-volume stocks experience extreme low-liquidity events almost immediately, while low-volume stocks have a time delayed reaction. The behavior of a select number of tickers is analyzed in detail over the outbreak period. The framework proposed can detect extreme liquidity events in real time and thus can be used to monitor market activity and provide early warnings about liquidity trends. A new intensity indicator measure is developed to assess and visualize extreme liquidity events.
AB - In this paper we develop a framework to analyze high-frequency (HF) financial transaction data focused on estimating a multidimensional intraday liquidity measure and detecting rare events. Many liquidity measures based on Trade and Quote (TAQ) and Limit Order Book (LOB) datasets are consolidated for this purpose through dimensionality reduction techniques. Several outlier methods based on extreme value theory, distance-based outlier methods, and tree-based algorithms are implemented to identify clusters of rare liquidity events. These methods provide insights into the behavior and occurrence of outliers. The methodology is optimized for HF intraday implementation. The framework is applied to transaction level data covering the beginning of COVID-19 outbreak period. We observe that after peak news activity, high-volume stocks experience extreme low-liquidity events almost immediately, while low-volume stocks have a time delayed reaction. The behavior of a select number of tickers is analyzed in detail over the outbreak period. The framework proposed can detect extreme liquidity events in real time and thus can be used to monitor market activity and provide early warnings about liquidity trends. A new intensity indicator measure is developed to assess and visualize extreme liquidity events.
KW - Event study
KW - High-frequency
KW - Liquidity measures
KW - Rare events
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U2 - 10.1016/j.irfa.2024.103455
DO - 10.1016/j.irfa.2024.103455
M3 - Article
AN - SCOPUS:85199250641
SN - 1057-5219
VL - 95
JO - International Review of Financial Analysis
JF - International Review of Financial Analysis
M1 - 103455
ER -