PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

High Frequency Statistical Arbitrage via the Optimal Thermal Causal Path
CVL Raju
(2011) Technical Report. London School of Economics.


We consider the problem of identifying similarities and causality rela- tionships in a given set of financial time series data streams. We develop further the “Optimal Thermal Causal Path”[28, 27] method, which is a non-parametric method proposed by Sornette et al. The method consid- ers 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 arbi- trage 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 sec- tors of NYSE data and extract the highly correlated pairs of time series. We show how traders could exploit arbitrage opportunities by using the method.

EPrint Type:Monograph (Technical Report)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:8631
Deposited By:Martin Anthony
Deposited On:16 February 2012