  Maximum likelihood estimation applied to high-frequency data allows us to
quantify intermittency in the fluctu- ations of asset prices. From time records
as short as one month these methods permit extraction of a meaningful
intermittency parameter {\lambda} characterising the degree of volatility
clustering of asset prices. We can therefore study the time evolution of
volatility clustering and test the statistical significance of this
variability. By analysing data from the Oslo Stock Exchange, and comparing the
results with the investment grade spread, we find that the estimates of
{\lambda} are lower at times of high market uncertainty.
