High Frequency Statistical Arbitrage via the Optimal Thermal Causal Path
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.