Financial Network Systemic Risk Contributions - Hautsch, Schaumburg, Schienle - 14-16 December 2013 - CFE, 7th International Conference on Computational and Financial Econometrics

1. Financial Network
Systemic Risk
Contributions
SYstemic Risk TOmography:
Signals, Measurements, Transmission Channels, and
Policy Interventions
Nikolaus Hautsch - University of Vienna
Julia Schaumburg - VU University Amsterdam, Tinbergen Institute
Melanie Schienle - Leibniz Universität Hannover
!
CFE - 7th International Conference on Computational and Financial Econometrics
London, December 14-16, 2013

2. Financial Network Systemic Risk Contributions
Nikolaus Hautsch
University of Vienna
Julia Schaumburg
VU University Amsterdam, Tinbergen Institute
Melanie Schienle
Leibniz Universit¨at Hannover

3. Introduction 2
Systemic Risk
Systemic risk: Breakdown risk of the financial system induced by
the interdependence of its constituents.
In crisis times, banks face
liquidity shortage, undercapitalisation;
leading to
fire-sales, hoarding;
⇒ further enhanced by pro-cyclicality of capital
requirements.
Consequences of interdependence:
Spillovers of risks
Co-movements of losses
=⇒ Systemic risk network
Financial Network Systemic Risk Contributions

4. Introduction 3
Network-based systemic risk assessment
Before the financial crisis 2007–2009, systemic risk was neglected by
regulation authorities.
Hellwig (2009), p. 134:
“Regulatory reform must [...] address the risks generated by [...]
interdependence and by the lack of transparency about systemic
risk exposure.”
Need for a transparent measure for systemic risk that takes
interdependence (risk spillovers) into account.
Financial Network Systemic Risk Contributions

5. Introduction 4
Here: VaR-Based Systemic Risk Contributions
Objective: Individual banks’ contributions to system tail risk: ”stress
test”-type analysis given publicly available data
Time-varying Value at Risk (VaR) conditional on observations of V
Pr(Xt ≤ qp,t(Xt)) = Pr(−Xt ≥ VaRp,t) = p,
where qp,t(Xt) = qp(Xt|V = Vt) is the pth cond. return quantile.
Estimation of a time-varying reduced form relation in quantiles
VaRs
q,t = gt(VaRi
p,t) = g(VaRi
p,t, Bt)
given control variables Bt and VaRi
p,t = VaRi
p(Wt).
Time-varying systemic risk contribution
∂VaRs
q,t
∂VaRi
p,t
=
∂gt(.)
∂VaRi
p,t
=: β
s|i
t
Financial Network Systemic Risk Contributions

6. Introduction 5
Our approach
Step 0 Selection of relevant tail risk drivers for each firm i:
LASSO for quantiles
other companies’ tail risk
macro environment
individual characteristics
Step 1 Estimation of VaRi : post-LASSO quantile regression
Step 2 Measuring each i’s time-varying contribution to
system risk: VaRs as function of VaRi
control variables: selected companies’ VaRj s
(from Step 0), macro environment
test of significance of VaRi for VaRs
Financial Network Systemic Risk Contributions

7. Introduction 6
Contribution
Identification of tail risk cross-linkages between financial
companies (Systemic risk network)
Estimation of and inference for two-stage quantile regression
model
Identification of systemically relevant companies and
quantification of systemic risk contributions
Time-varying systemic risk rankings
Financial Network Systemic Risk Contributions

8. Network 7
U.S. network of risk spillovers
Financial Network Systemic Risk Contributions

9. Systemic risk betas 8
Systemic risk ranking during the crisis (June 08)
Rank Name β
s|i
2008 · 102
β
s|i
2008 VaR
i
2008
1 BANK OF AMERICA 2.86∗
0.186 0.154
2 AMERICAN EXPRESS 2.78∗
0.278 0.100
3 WELLS FARGO & CO 2.51∗
0.186 0.135
4 MARSHALL & ILSLEY 2.31∗
0.516 0.045
5 JP MORGAN CHASE & CO. 2.22 0.265 0.084
6 PROGRESSIVE OHIO 1.97∗
0.380 0.052
7 LEGG MASON 1.96∗
0.137 0.143
8 REGIONS FINANCIAL 1.86∗
0.107 0.173
9 MARSH & MCLENNAN 1.76∗
0.471 0.037
10 STATE STREET 1.44∗
0.171 0.084
11 NY.CMTY.BANC. 1.12 0.090 0.125
12 PNC FINANCIAL SVS. GP 1.09∗
0.153 0.071
13 CHUBB 1.07∗
0.176 0.061
14 TORCHMARK 1.00∗
0.177 0.057
15 CHARLES SCHWAB 0.91∗
0.149 0.060
16 CITIGROUP 0.90∗
0.072 0.124
17 MORGAN STANLEY 0.61∗
0.074 0.083
18 ZIONS BANCORP. 0.58∗
0.058 0.100
19 UNUM GROUP 0.34∗
0.033 0.104
20 UNION PACIFIC 0.27∗
0.047 0.056
21 HARTFORD FINL.SVS.GP. 0.24∗
0.012 0.201
22 FRANKLIN RESOURCES 0.17∗
0.026 0.064
23 T ROWE PRICE GP. 0.01∗
0.001 0.102
Financial Network Systemic Risk Contributions

10. Case study 9
Pre-crisis case study
Excluding the financial crisis time period, can we infer companies’
developments from our method?
Repeat entire estimation and testing procedure using only
data until June 2007
Focus on companies that were affected by the crisis: AIG,
Freddie Mac, Lehman Brothers, Merrill Lynch
⇒ Results are reasonable with respect to systemic riskiness and
tail risk dependencies.
Financial Network Systemic Risk Contributions

11. Conclusion 10
Conclusions
Transparent risk measure accounting for tail risk dependencies.
Approach provides complementing qualitative (tail risk
network) and quantitative (systemic risk ranking) information
on the U.S. financial sector.
Although direct backtesting is not possible, plausibility checks
suggest that the method works.
Measure may be extended in several ways.
Financial Network Systemic Risk Contributions

12. This project is funded by
the European Union under the
7th Framework Programme (FP7-SSH/2007-2013)
Grant Agreement n°320270
!
!
!
!
!
!
!
www.syrtoproject.eu
This document reflects only the author’s views. The European Union is not liable for any use that may be made of the information contained therein.