Big-Data driven 1-to-1 Simulations of Financial Systems for the Elimination of Systemic Risk
Stefan Thurner - Medical University of Vienna, Austria
Session chair: Peter Sloot

Abstract : Controlling complex systems is a challenge that is as old as humanity. Unlike physical systems, which often can be described with a few parameters, complex systems usually depend on many details, often millions. For the first time, with the advent of a new data generation, we are in the position to measure all such details in real time. This opens completely new abilities for modeling, understanding and evemtually managing complex systems. We will present a 1:1 model of the financial market of an entire nation and show the knowledge of all transactions can help to eliminate the financial systemic risk of the country. We show that the systemic risk level of every agent in the system can be measured by simple network measures. With actual central bank data for Austria and Mexico we are able to compute the expected systemic losses of an economy, a number that allows us to estimate the cost of a financial crises. We can further show that it is even possible to compute the systemic risk of every single financial transaction. We suggest an intelligent financial transaction tax that taxes the systemic risk contribution of all transactions. With the 1:1 agent based model we demonstrate that this Systemic Risk Tax practically eliminates the network-component of systemic risk in a system.

http://www.complex-systems.meduniwien.ac.at/people/sthurner/