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High frequency statistical arbitrage via the optimal thermal causal path

High frequency statistical arbitrage via the optimal thermal causal path

Chinthalapati, V L Raju (2011) High frequency statistical arbitrage via the optimal thermal causal path. [Working Paper] (doi:10.2139/ssrn.2033172)

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Abstract

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, 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.

Item Type: Working Paper
Uncontrolled Keywords: Statistical Arbitrage, Time-series Classification, Optimal Thermal Causal Path.
Faculty / Department / Research Group: Faculty of Business > Department of Accounting & Finance
Last Modified: 14 Oct 2016 09:32
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
URI: http://gala.gre.ac.uk/id/eprint/13359

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