Abstract:
We show that volatility spillovers arc large enough to matter to investors. We demonstrate that standard
deviations of returns to mean-variance portfolios of European equities fall by 1-1.5% at daily, weekly, and
monthly rebalancing horizons when volatility spillovers are included in covariance forecasts. We estimate
the conditional second moment matrix of (synchronized) daily index returns for the London, Frankfurt and
Paris stock markets via two asymmetric dynamic conditional correlation models (A-DCC): the unrestricted
model includes volatility spillovers and the restricted model docs not. We combine covariance forecasts
from the restricted and unrestricted models with a wide range of assumed returns relatives via a polar coordinates
method, and compute out-of-sample realized portfolio returns and variances for testing. Diebold
Mariano teste; confinn that most risk reductions are statistically significant. Stochastic dominance test"
indicate that portfolios accounting for volatility spillover would be preferred by risk adverse agents.
