  In order to price contingent claims one needs to first understand the
dynamics of these indices. Here we provide a first econometric analysis of the
CRIX family within a time-series framework. The key steps of our analysis
include model selection, estimation and testing. Linear dependence is removed
by an ARIMA model, the diagnostic checking resulted in an ARIMA(2,0,2) model
for the available sample period from Aug 1st, 2014 to April 6th, 2016. The
model residuals showed the well known phenomenon of volatility clustering.
Therefore a further refinement lead us to an ARIMA(2,0,2)-t-GARCH(1,1) process.
This specification conveniently takes care of fat-tail properties that are
typical for financial markets. The multivariate GARCH models are implemented on
the CRIX index family to explore the interaction.
