Inferring latent market positioning in U.S. treasury markets using regime switching models

We investigate whether the impact of latent market expectations, models and current holdings (collectively termed market positioning ) has an impact on the reaction of U.S. Treasury bond markets to macroeconomic surprise. Our initial findings indicate a positive linear relationship between market reaction and the surprise content of the announcement. A positive linear relationship is also found between the surprise content and the market reaction post announcement. However, we demonstrate that the transitivity of these relationships fails to hold, in that no significant linear relationship could be found between pre-announcement reaction and post-announcement reaction. Thus, we hypothesise that latent market positioning is distorting the linearity between pre and post reaction, and that the market response can be regime dependent. Motivated by these findings, we introduce a threshold auto-regressive model to study the reactions to price movements in the lead-up to macroeconomic announcements. The model leverages multiple regimes which are determined by combinations of the observed macroeconomic surprise and by the latent market positioning, therefore capturing the non-linearity of the process. One critical novelty of our work is that the market positioning is latent and thus inferred from the data. We adopt a combination of particle swarm optimisation and genetic algorithms to estimate the model and to perform model selection: we discover three market positioning states to be optimal. These states are highly interpretable and the transition probabilities between states are determined based on the surprise of previous events. As an illustrative example, we use these findings to sketch a general playbook for how to trade these events, thus providing asymmetric risk reward opportunities.