The subprime crisis of 2007 brought the worldwide financial system to the brink of breakdown. Famous rating agencies that should have sent early warning signals failed to do so and therefore proved to be poor monitors of financial markets risks.
Because of the economic hardship a breakdown of financial markets represents it becomes of highest importance that objective measures of risk are constructed and made available to the public at large as to avoid future market breakdowns.
The objective of this website is to present the results of a model developed jointly between HEC Lausanne and the NYU Stern Volatility Institute. The NYU Stern Volatility Lab is a systemic risk measurement provider for US and global financial firms. It is based at New York University Stern School of Business under the direction of NYU Stern Professor and Nobel Laureate Robert Engle. A new European model has been built by Prof. Engle as well as Profs. Eric Jondeau and Michael Rockinger both from HEC Lausanne. The model is based on publicly available data and anybody who wishes to do so can replicate our methodology which is discussed in detail in a working paper. Stated differently, we have a completely transparent model which we update once per week. The results are what they are and if some financial institution does not like the results, we cannot change them. We are willing to adapt our methodology if corrections would be needed. Again we promote openness and discussion with the market.
Disclaimer 1: The implementation of our model follows best practices, however, the model may not apply to banks with complex and opaque governance structures. It cannot be assured that the data used is correct. For these various reasons, our measures should be considered as indications. They should not be used for trading purposes.
Disclaimer 2: Financial institutions involve several categories, including banks, insurance companies, and real estate firms. Each of these categories has its own characteristics, in particular in the way financial leverage is built. For this reason, the comparison of SRISK measures across categories should be taken carefully.