Identification of FX Arbitrage Opoortunities with a Non-Linear Mulitve Ariate Kalman Filter

摘要

We present a method of identifying arbitrage opportunities across multiple foreign exchange markets. Arbitrage opportunities vioolate exchange rate equilibrium relationships and can be viewed as multivariate additive outliers. Robust identification/filtering of arbitrage opportunities in the data is accomplished by Kalman filtering. The state space model used to describe the FX markets is general enough to handle both linear and non-linear models. The recusive Kalaman equations are adapted to filter tick data, cope with the erratic arrival of observations and produce estimates of all the exchange rates o nevery second. Having exchange rate estimates every second increase the speed and efficiency of arbitrage identification. We demonstrate the methodology with a robust neural network filter applied to currency tick data for three markets: