Statistical Arbitrage and High-Frequency Data with an Application to Eurostoxx 50 Equities

31 Pages Posted: 1 Jun 2013 Last revised: 3 Jun 2013

See all articles by Jozef Rudy

Jozef Rudy

Harvest Alpha Capital

Christian Dunis

John Moores University - Business School

Gianluigi Giorgioni

University of Liverpool - Management School (ULMS)

Jason Laws

University of Liverpool - Accounting and Finance Division

Date Written: September 10, 2010

Abstract

The motivation for this paper is to apply a statistical arbitrage technique of pairs trading to high-frequency equity data and compare its profit potential to the standard sampling frequency of daily closing prices. We use a simple trading strategy to evaluate the profit potential of the data series and compare information ratios yielded by each of the different data sampling frequencies. The frequencies observed range from a 5-minute interval, to prices recorded at the close of each trading day.

The analysis of the data series reveals that the extent to which daily data are cointegrated provides a good indicator of the profitability of the pair in the high-frequency domain. For each series, the in-sample information ratio is a good indicator of the future profitability as well.

Conclusive observations show that arbitrage profitability is in fact present when applying a novel diversified pair trading strategy to high-frequency data. In particular, even once very conservative transaction costs are taken into account, the trading portfolio suggested achieves very attractive information ratios (e.g. above 3 for an average pair sampled at the high-frequency interval and above 1 for a daily sampling frequency).

Keywords: High-frequency data, statistical arbitrage, pairs trading, cointegration, time adaptive models

JEL Classification: C00, C10, C50, G00, G11

Suggested Citation

Rudy, Jozef and Dunis, Christian and Giorgioni, Gianluigi and Laws, Jason, Statistical Arbitrage and High-Frequency Data with an Application to Eurostoxx 50 Equities (September 10, 2010). Available at SSRN: https://ssrn.com/abstract=2272605 or http://dx.doi.org/10.2139/ssrn.2272605

Jozef Rudy (Contact Author)

Harvest Alpha Capital ( email )

Bratislava
Slovakia

Christian Dunis

John Moores University - Business School ( email )

John Foster Building
98 Mount Pleasant
Liverpool, L3 5UZ
United Kingdom

Gianluigi Giorgioni

University of Liverpool - Management School (ULMS) ( email )

Chatham Street
Liverpool, L69 7ZH
United Kingdom

Jason Laws

University of Liverpool - Accounting and Finance Division ( email )

United Kingdom

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