This article proposes a new empirical methodology for computing a cross-market volatility index - coined CMIX - based on the Factor-Dynamic Conditional Correlation (DCC) model, implemented on volatility surprises. This approach solves problems in treating high-dimensional data and estimating time-varying conditional correlations. We provide an application to multi-asset market data composed of equities, bonds, foreign exchange rates and commodities during 1983-2013. This new methodology may be attractive to asset managers, because it provides a simple way to hedge multi-asset portfolios with derivatives contracts written on the CMIX.
