We modeled long memory with just one lag!

Bauwens, Luc [UCL] Chevillon, Guillaume Laurent, Sébastien

We build on two contributions that have found conditions for large dimensional networks or systems to generate long memory in their individual components, and provide a methodology for modeling and forecasting series displaying long range dependence. We model long memory properties within a vector autoregressive system of order 1 and consider Bayesian estimation or ridge regression. For these, we derive a theory-driven parametric setting that informs a prior distribution or a shrinkage target. Our proposal significantly outperforms univariate time series long memory models when forecasting a daily volatility measure for 250 U.S. company stocks, as well as seasonally adjusted monthly streamflow series recorded at 97 locations of the Columbia river basin.