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

Assessing market uncertainty by means of a time-varying intermittency parameter for asset price fluctuations