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http://ssrn.com/abstract=1722202
 
 

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Modeling Asset Prices for Algorithmic and High Frequency Trading


Álvaro Cartea


University College London

Sebastian Jaimungal


University of Toronto - Department of Statistics

December 8, 2010

Applied Mathematical Finance, Vol. 20, No. 6, 2013

Abstract:     
Algorithmic Trading (AT) and High Frequency (HF) trading, which are responsible for over 70\% of US stocks trading volume, have greatly changed the microstructure dynamics of tick-by-tick stock data. In this paper we employ a hidden Markov model to examine how the intra-day dynamics of the stock market have changed, and how to use this information to develop trading strategies at high frequencies. In particular, we show how to employ our model to submit limit-orders to profit from the bid-ask spread and we also provide evidence of how HF traders may profit from liquidity incentives (liquidity rebates). We use data from February 2001 and February 2008 to show that while in 2001 the intra-day states with shortest average durations (waiting time between trades) were also the ones with very few trades, in 2008 the vast majority of trades took place in the states with shortest average durations. Moreover, in 2008 the states with shortest durations have the smallest price impact as measured by the volatility of price innovations.

Number of Pages in PDF File: 32

Keywords: High Frequency Traders, Algorithmic Trading, Durations, Hidden Markov Model

JEL Classification: G10, G11, G14, C41

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Date posted: December 9, 2010 ; Last revised: February 28, 2014

Suggested Citation

Cartea, Álvaro and Jaimungal, Sebastian, Modeling Asset Prices for Algorithmic and High Frequency Trading (December 8, 2010). Applied Mathematical Finance, Vol. 20, No. 6, 2013. Available at SSRN: http://ssrn.com/abstract=1722202 or http://dx.doi.org/10.2139/ssrn.1722202

Contact Information

Álvaro Cartea
University College London ( email )
Gower Street
London, WC1E 6BT
United Kingdom
HOME PAGE: http://www.cartea.net
Sebastian Jaimungal (Contact Author)
University of Toronto - Department of Statistics ( email )
100 St. George St.
Toronto, Ontario M5S 3G3
Canada
HOME PAGE: http://www.utstat.utoronto.ca/sjaimung
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