Conditional Correlations and Principal Regression Analysis for Futures

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.

Suggested Citation: Suggested Citation

Karami, Armine and Benichou, Raphael and Benzaquen, Michael and Benzaquen, Michael and Bouchaud, Jean-Philippe, Conditional Correlations and Principal Regression Analysis for Futures (December 27, 2019). Available at SSRN: https://ssrn.com/abstract=3510384 or http://dx.doi.org/10.2139/ssrn.3510384