Working Paper: CEPR ID: DP17992
Authors: Daniel Dimitrov; Sweder van Wijnbergen
Abstract: We propose a credit portfolio approach for evaluating systemic risk and attributing it across institutions. We construct a model that can be estimated from high-frequency CDS data. This captures risks from publicly traded banks, privately held institutions, and cooperative banks, extending approaches that rely on information from the public equity market only. We account for correlated losses between the institutions, overcoming a modeling weakness in earlier studies. We also offer a modeling extension to account for fat tails and skewness of asset returns. The model is applied to a universe of banks where we find discrepancies between the capital adequacy of the largest contributors to systemic risk relative to less systemically important banks on a European scale.
Keywords: systemic risk; CDS rates; implied market measures; financial institutions; fat tails
JEL Codes: G01; G20; G18; G38
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| systemic risk measurement (E44) | policy decisions regarding capital buffers (G28) |
| extreme losses in the banking sector (F65) | correlated losses (G33) |
| common economic factors (E25) | extreme losses in the banking sector (F65) |
| correlated losses (G33) | systemic risk (E44) |
| default probabilities of individual banks (G21) | joint probabilities of distress across the banking sector (E44) |
| default of one institution (G33) | likelihood of distress in others (E71) |
| market conditions (P42) | joint default probabilities (G33) |
| systemic risk measurements (E44) | interconnections among institutions (D02) |