Information Shares in Stationary Time Series and Global Volatility Discovery
58 Pages Posted: 16 Feb 2016 Last revised: 25 Jul 2017
Date Written: July 24, 2017
Standard price discovery measures, particularly information shares, rely on the concept of co-integration for non-stationary time series. For the definition of information shares, the existence of a permanent impact of innovations is crucial, as these shares measure the relative contribution of different markets to this permanent impact. For stationary time series such as interest rates, CDS prices, or volatilities, permanent impacts do not occur and thus the concept fails. In this paper, we extend the concept of information shares to the case of stationary time series. We suggest a price discovery metric based on the well-known variance decomposition for stationary time series, aiming to be equivalent to Hasbrouck's (1995) information share. This new price discovery measure converges to the standard information share in the limiting case of non-stationarity. As an application, we use the new measure to gain insight into the behavior of major implied volatility indices in Japan, Germany, and the US. We find that the US market has lost its dominant role for global volatility discovery with the emergence of the European debt crisis. In recent periods, the German volatility index exhibits the largest information shares. The Japanese market had a higher importance before the global financial crisis, which has almost vanished within recent periods.
Keywords: information share, stationarity, co-integration, variance decomposition, volatility spillover
JEL Classification: G14, C32, C10, C51
Suggested Citation: Suggested Citation