Return and Risk of Pairs Trading using a Simulation-based Bayesian Procedure for Predicting Stable Ratios of Stock Prices

In this paper we consider two cases of pairs trading strategies: a conditional statistical arbitrage method and an implicit statistical arbitrage method. We use a simulation-based Bayesian procedure for predicting stable ratios, defined in a cointegration model, of pairs of stock prices. We show the effect that using an encompassing prior under an orthogonal normalization has for the selection of pairs of cointegrated stock prices and for the estimation and prediction of the spread between cointegrated stock prices and its uncertainty. An empirical application is done using stocks that are ingredients of the Dow Jones Composite Average index. The results show that the normalization has little effect on the selection of pairs of cointegrated stocks on the basis of Bayes factors. However, the results stress the importance of the orthogonal normalization for the estimation and prediction of the spread - the deviation from the equilibrium relationship - which leads to better results in terms of profit per capital engagement and risk than using a standard linear normalization