Price impact and bursts in liquidity provision

Gençay, Ramazan; Mahmoodzadeh, Soheil; Rojcek, Jakub; Tseng, Michael

doi:10.1080/14697688.2017.1420209

Cited by 6 publications

(3 citation statements)

References 30 publications

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“…Thanks to the availability of high frequency data, one important line of research in the order-driven market in recent years is to understand price impact of orders since it is a fundamental mechanism of price formation (Cont et al, 2014;Gencay et al, 2018;Wilinski et al, 2015). Dufour and Engle (2000), Easley et al (1996), Engle and Patton (2004), Hasbrouck (1991) and Jang and Venkatesh (1991) explore how characteristics of trades such as frequency, size, order flows and bid-ask spread contribute to price formation.…”

Section: Literature Reviewmentioning

confidence: 99%

Volatility Modeling and Prediction: The Role of Price Impact

et al. 2018

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In this paper, we are interested in exploring the role of price impact, derived from the order book, in modeling and predicting stock volatility. This is motivated by the market microstructure literature that examines the mechanics of price formation and its relevance to market quality. Using a comprehensive dataset of intraday bids, asks, and three levels of market depths for 148 stocks in the Shanghai Stock Exchange from 2005 to 2016, we find substantial intraday impact from incoming bid and ask limit and market orders on stock prices. More importantly, the permanent price impact at the daily level is a significant determinant of stock volatility dynamics as suggested by the panel VAR estimation. Furthermore, when we augment traditional volatility models with the time series of daily price impact, the augmented models produce significantly more accurate volatility predictions at the one-day ahead forecasting horizon. These volatility predictions also offer economic gains to a mean-variance utility investor in a portfolio setting.

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Section: Literature Reviewmentioning

confidence: 99%

Volatility Modeling and Prediction: The Role of Price Impact

et al. 2018

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“…2 Kacperczyk and Pagnotta (2019) and Garriott and Riordan (2019) find that directly identified informed traders empirically use limit orders frequently. Gencay, Mahmoodzadeh, Rojcek, and Tseng (2016) investigate brief episodes of extreme quotation behavior in the U.S. equity market (bursts in liquidity provision that happen several hundreds of time a day for actively traded stocks) and find that limit orders during these bursts significantly impact prices.…”

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confidence: 99%

Information, Liquidity, and Dynamic Limit Order Markets

2017

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“…Next, we test whether an increase in informed trading activity affects the bid and ask sides of the market differently. Recent studies underline the different attributes of bid‐ and ask‐side liquidity (Brennan et al, 2012; Cenesizoglu & Grass, 2018; Roşu, 2009) and the presence of asymmetric adverse selection on the bid and ask sides of the order book (Gencay et al, 2018). To this end, we compute the bid‐ and ask‐side measures for

{SLOPE}

and

{DEPTH}

for each cryptocurrency and for all intervals.…”

Section: Resultsmentioning

confidence: 99%

Adverse selection in cryptocurrency markets

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et al. 2023

J of Financial Research

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In this article we investigate the influence that information asymmetry may have on future volatility, liquidity, market toxicity, and returns within cryptocurrency markets. We use the adverse‐selection component of the effective spread as a proxy for overall information asymmetry. Using order and trade data from the Bitfinex exchange, we first document statistically significant adverse‐selection costs for major cryptocurrencies. Also, our results suggest that adverse‐selection costs, on average, correspond to 10% of the estimated effective spread, indicating an economically significant impact of adverse‐selection risk on transaction costs in cryptocurrency markets. Finally, we document that adverse‐selection costs are important predictors of intraday volatility, liquidity, market toxicity, and returns.

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Price impact and bursts in liquidity provision

Cited by 6 publications

References 30 publications

Volatility Modeling and Prediction: The Role of Price Impact

Volatility Modeling and Prediction: The Role of Price Impact

Information, Liquidity, and Dynamic Limit Order Markets

Adverse selection in cryptocurrency markets

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