Modeling Asset Prices for Algorithmic and High Frequency Trading

32 Pages Posted: 9 Dec 2010 Last revised: 28 Feb 2014

Álvaro Cartea

University of Oxford; University of Oxford - Oxford-Man Institute of Quantitative Finance

Sebastian Jaimungal

University of Toronto - Department of Statistics

Date Written: December 8, 2010

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.

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

JEL Classification: G10, G11, G14, C41

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: https://ssrn.com/abstract=1722202 or http://dx.doi.org/10.2139/ssrn.1722202

Álvaro Cartea

University of Oxford ( email )

Mansfield Road
Oxford, Oxfordshire OX1 4AU
United Kingdom

University of Oxford - Oxford-Man Institute of Quantitative Finance ( email )

Eagle House
Walton Well Road
Oxford, Oxfordshire OX2 6ED
United Kingdom

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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