Copyright © 2017 by authors and IBII. This work is licensed under the Creative Commons Attribution International License (CC BY 4.0). Journal of Management Science and Business Intelligence, 2018, 3–1 July, 2018, pages 31-39 doi: 10.5281/zenodo.1250565 http://www.ibii-us.org/Journals/JMSBI/ ISBN 2472-9264 (Online), 2472-9256 (Print) A Study on Brexit: Correlations and Tail Events Distribution of Liquidity Measures Mingyuan Kong, Amin Salighehdar and Dragos Bozdog * School of Business, Stevens Institute of Technology, Hoboken, NJ, 07030 *Email: dbozdog@stevens.edu Received on March 28, 2018; revised on May 21, 2018; published on May 21, 2018 Abstract Liquidity describes the degree to which an asset or security can be quickly bought or sold in the market without affecting the asset's price. In this study, some of the existing liquidity measures are studied and analyzed during Brexit. We examine Utilities Select Sector SPDR Fund (Exchange-Traded Fund) components in this study. The time period covers June 16, 2016 to June 30, 2016 which includes Brexit event day. We use high-frequency tick level Trade data, Quote data, and Limit Order Book data. We study the sample of Trade and Quote liquidity measures (TAQL) and Limit Order Book liquidity measures (LOBL). Our study shows that the correlations be- tween these two liquidity groups (TAQL & LOBL) have significant relationship with the returns of the underlying ETF components. Furthermore, the analysis shows that low correlation between TAQL and LOBL indicates high probability of large price change. Finally, we study the empirical distributions, which implies that Brexit generated fatter tails on liquidity measures distributions. This indicates that infrequent (low) liquidity condition occurs more frequently during Brexit. Keywords: Brexit; Liquidity Measures; Correlation; Tail Events 1 Introduction In financial markets, liquidity is the term used to describe how easy it is to convert assets to cash. This definition is equivalent to considering low transaction cost, short execution time, and small impact on asset's prices. A liquid market might be characterized as a continuous market where trad- ers can buy or sell any amount of stock immediately (Black (1971)). Theory and empirical evidence indicate that investors require higher re- turns on assets with low market liquidity to compensate them for the high transaction cost (Amihud (2006)). Liquidity plays an important role in as- set pricing and market microstructure. Brexit referendum took place on June 23, 2016, and it resulted in 51.9% of voters voting in favor of the UK leaving the EU. The effect of Brexit on economy has a significant short-term and long-term consequence, such as the decrease of GBP value and the increase of inflation rate in the UK. Brexit's effects on New Zealand, Australia and Indian Stock Markets are studied by Abraham (2016), in which economic crisis's effects are inves- tigated and there is no significant effect in the stock markets during the crisis period. Schiereck et al. (2016) analyzed Brexit referendum's effects on the stock and CDS market are analyzed and it is found that Brexit has more significant influence on the short-run drop in stock prices than Leh- man's bankruptcy. Liquidity risk and its effects on price during Brexit is studied by Mago et al. (2017). Tick level data (TAQ and LOB data) was obtained from Thomson Reu- ters Tick History Database (TRTH) to perform the analysis. All compo- nents of a specific ETF (Exchange-Traded Fund) are selected as the targets of this research. In general, an ETF tracks a certain market sector and pro- vides reduced exposure to individual constituents. We consider the Utili- ties Select Sector SPDR Fund for this study given its significant move- ment during Brexit referendum studied by Suttmeier (2016). There more than 65 liquidity measures proposed in existing literature by Salighehdar et al. (2017). Most liquidity measures have been applied to low frequency data framework. Nowadays, high-frequency data is available and high-frequency trading accounts for a large portion of the US equity market (Brogaard (2010)). In this study, we analyze TAQL measures and LOBL measures using high-frequency data. Traditionally, the liquidity measures have been clas- sified as one-dimensional or multi-dimensional based on the specific li- quidity features they are measuring. For example, Bacidore (1997) the number of transactions in a certain period of time, by which frequent trans- actions reflect high liquidity. Underlying net trade volume (buyer-side volume minus seller-side volume) has the ability to predict stock quotes (Chan et al. (2002)). Turnover and market depth focus on the total trading amount and the total volume of bid and ask volume (Brockman et al. (2000)). Bid and ask spread is frequently used as the indicator to reflect liquidity conditions (Chordia et al. (2001) and Grammig et al. (2001)). There are other liquidity measures concentrating on spread studied by (Hamao and Hasbrouck (1995), Levin et al. (1999), Fleming and