Systemic risk measures on general measurable spaces

作者：Kromer E^*; Overbeck L; Zilch K

来源：Mathematical Methods of Operations Research, 2016, 84(2): 323-357.

DOI：10.1007/s00186-016-0545-1

• Systemic risk measure
• Aggregation function
• Locally convex-solid Riesz spaces
• Decomposition
• Dual representation
• Risk attribution

In view of the recent financial crisis systemic risk has become a very important research object. It is of significant importance to understand what can be done from a regulatory point of view to make the financial system more resilient to global crises. Systemic risk measures can provide more insight on this aspect. The study of systemic risk measures should support central banks and financial regulators with information that allows for better decision making and better risk management. For this reason this paper studies systemic risk measures on locally convex-solid Riesz spaces. In our work we extend the axiomatic approach to systemic risk, as introduced in Chen et al. (Manag Sci 59(6):1373-1388, 2013), in different directions. One direction is the introduction of systemic risk measures that do not have to be positively homogeneous. The other direction is that we allow for a general measurable space whereas in Chen et al. (2013) only a finite probability space is considered. This extends the scope of possible loss distributions of the components of a financial system to a great extent and introduces more flexibility for the choice of suitable systemic risk measures.

• 出版日期2016-10