High Frequency Statistical Arbitrage Via the Optimal Thermal Causal Path

V. L. Raju Chinthalapati

University of Greenwich

April 2, 2012

We consider the problem of identifying similarities and causality relationships in a given set of financial time series data streams. We develop further the Optimal Thermal Causal Path method proposed by Sornette et al, which is a non-parametric method proposed by Sornette et al. The method considers the mismatch between a given pair of time series in order to identify the expected minimum energy path lead-lag structure between the pair. Traders may find this a useful tool for directional trading, to spot arbitrage opportunities. We add a curvature energy term to the method and we propose an approximation technique to reduce the computational time. We apply the method and approximation technique on various market sectors of NYSE data and extract the highly correlated pairs of time series. We show how traders could exploit arbitrage opportunities by using the method.

Number of Pages in PDF File: 40

Keywords: Statistical Arbitrage, Time-series Classification, Optimal Thermal Causal Path, High Frequency Trading

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Date posted: April 2, 2012  

Suggested Citation

Chinthalapati, V. L. Raju, High Frequency Statistical Arbitrage Via the Optimal Thermal Causal Path (April 2, 2012). Available at SSRN: https://ssrn.com/abstract=2033172 or http://dx.doi.org/10.2139/ssrn.2033172

Contact Information

Venkata L. R. Chinthalapati (Contact Author)
University of Greenwich ( email )
United Kingdom
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