This thesis presents a new strategy that unites qualitative and quantitative mass data in form
of text news and tick-by-tick asset prices to forecast the risk of upcoming volatility shocks.
Holger Kömm embeds the proposed strategy in a monitoring system using first a sequence of
competing estimators to compute the unobservable volatility second a new two-state Markov
switching mixture model for autoregressive and zero-inflated time-series to identify structural
breaks in a latent data generation process and third a selection of competing pattern
recognition algorithms to classify the potential information embedded in unexpected but public
observable text data in shock and nonshock information. The monitor is trained tested and
evaluated on a two year survey on the prime standard assets listed in the indices DAX MDAX
SDAX and TecDAX.
