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Quantifying Systemic Risk by Solutions of the Mean-Variance Risk Model
Jan Jurczyk1, Alexander Eckrot1, Ingo Morgenstern1
1Department of Physics, University of Regensburg, Regensburg, Germany.
Plos One
|June 29, 2016
Summary
Monitoring average investor behavior using the mean-variance model can serve as an early warning system for financial market turmoil. This approach helps quantify systemic risk by analyzing changes in investor strategies during economic downturns.
Area of Science:
- Financial Economics
- Market Dynamics
- Risk Management
Background:
- The 2008 financial crisis, triggered by Lehman Brothers' bankruptcy, highlighted the need for better systemic risk detection.
- Cross-correlations between assets and indices have been linked to market systemic risks post-crisis.
Purpose of the Study:
- To develop an early warning system for financial market turmoil.
- To quantify periods of increased systemic risk by monitoring investor behavior.
Main Methods:
- Analysis of time-dependent cross-correlations among 37 diverse US indices.
- Approximation of overall investor strategy using ground-states of the mean-variance model under real-world constraints.
Main Results:
- Changes in average investor behavior, approximated by the mean-variance model, can quantify times of increased market risk.
- Monitoring these behavioral shifts provides an early warning signal for potential financial instability.
Conclusions:
- Average investor behavior is a quantifiable indicator of systemic risk.
- The mean-variance model, applied to real-world constraints, offers a method for early detection of financial market stress.
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