Conditional Correlations and Principal Regression Analysis for Futures

TL;DR

This paper investigates how past market trends influence instantaneous correlations among futures assets, extending Principal Regression Analysis to identify sector-specific effects and improve risk estimation in a non-stationary environment.

Contribution

It introduces an extended multifactor PRA method to analyze sectorial influences on correlations, revealing distinct effects from index and bonds sectors.

Findings

Past trends in the eigen-factor affect correlations significantly.

Index sector movements reduce correlations, bonds sector movements increase them.

Two main components explain the influence of past market movements on correlations.

Abstract

We explore the effect of past market movements on the instantaneous correlations between assets within the futures market. Quantifying this effect is of interest to estimate and manage the risk associated to portfolios of futures in a non-stationary context. We apply and extend a previously reported method called the Principal Regression Analysis (PRA) to a universe of $84$ futures contracts between $2009$ and $2019$. We show that the past up (resp. down) 10 day trends of a novel predictor -- the eigen-factor -- tend to reduce (resp. increase) instantaneous correlations. We then carry out a multifactor PRA on sectorial predictors corresponding to the four futures sectors (indexes, commodities, bonds and currencies), and show that the effect of past market movements on the future variations of the instantaneous correlations can be decomposed into two significant components. The first component is due to the market movements within the index sector, while the second component is due to the market movements within the bonds sector.