Fat tails, serial dependence, and implied volatility index connections

This paper accounts for fat tails and serial dependence for implied volatility index network connections among equity and commodity markets using Bayesian vector heterogeneous autoregressions. I analyse the information content of such connections over short-, medium- and long-horizons for predicting underlying asset returns and whether conventional asset pricing risk factors explain the variation of portfolios that sort on directional connections. Including network connections within the information set yields significant gains when forecasting underlying asset returns. Sorting underlying assets on directional connections shows that investors can hedge against temporary changes to investment opportunities at horizons of less than one month.