Flexible and robust mixture models for the identification of structural shocks in financial time series

• Applicant: Professor Dr. Markus Haas
• Subject Area: Statistics and Econometrics
• Term: from 2015 to 2019
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 273769120

Identification of the contemporaneous structural effects in vector autoregressive models is an important issue in the analysis of multivariate time series since it is the structural effects which incarnate the economic content of a model. An important example is the analysis of transmission and (possibly) contagion effects of shocks in financial markets. We can measure the correlations between assets and observe that often these correlations are substantially different in bull and bear market periods. However, risk managers and policy makers typically need more information. Namely, they need to know how strong and in which directions shocks in specific markets are transmitted to other markets, and whether the pattern of shock transmission is the same in boom and crisis periods. This information cannot be directly read off the correlations structure.This research project aims at extending and newly developing methods which can help to identify structural shock particularly in financial data. It is known that typical distributional properties of financial time series can be exploited to reach this goal. These are, in particular, the conditional heteroskedasticity as well as the pronounced leptokurtosis of most financial data, i.e., the fact that the empirical distribution typically has thicker tails and higher peaks than the Gaussian distribution. These features may already be sufficient to identify the structural effects.The models to be developed and investigated in this research project are members of the class of regime-switching models. These models are rather popular in empirical finance due to their good fit and economic interpretability of the extracted regimes. Currently existing approaches to identification via regime-switching effects are adopted and extended in order to achieve an optimal fit to the pertinent properties of the data under study. Thus models are constructed incorporating thick-tailed innovations, independent components, and Markov-switching GARCH effects, where the latter class of models has been proven to deliver a particularly close fit to financial returns measured at higher frequencies such as daily or weekly. Subsequently, the usefulness of the models is illustrated by applying them to a set of relevant problems in financial economics, such as transmission of shocks, price formation in foreign exchange markets, and the effects of speculation in commodity markets.

• DFG Programme: Research Grants