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Systemic risk signaling using scores - Andre Lucas, Bernd Schwaab, Xin Zhang, Francisco Blasques, Siem Jan Koopman, Julia Schaumburg. SYRTO Code Workshop Workshop on Systemic Risk Policy Issues for SYRTO (Bundesbank-ECB-ESRB) Head Office of Deustche Bundesbank, Guest House Frankfurt am Main - July, 2 2014
Citation preview
Systemic risk signaling
using scores
SYstemic Risk TOmography:
Signals, Measurements, Transmission Channels, and Policy Interventions
André Lucas, Bernd Schwaab, Xin Zhang VU University Amsterdam / ECB / Riksbank
Francisco Blasques, Siem Jan Koopman, Andre Lucas, Julia Schaumburg VU University Amsterdam SYRTO Code Workshop Workshop on Systemic Risk Policy Issues for SYRTO July, 2 2014 - Frankfurt (Bundesbank-ECB-ESRB)
Three papers:
� Lucas, Schwaab, Zhang: "Conditional euro area sovereign default risk," Journal of Business and Economic Statistics, 32:2, 271-284.
� Lucas, Schwaab, Zhang: "Measuring Credit Risk in a Large Banking System: Econometric Modeling and Empirics,", TI Discussion Paper 13-063/IV/DSF56, version June 2014.
� Blasques, Koopman, Lucas, Schaumburg: “Spillover dynamics for systemic risk measurement using spatial financial time series models," version June 2014.
The data
Sovereign CDS data
Bank equity return data and EDF data
Objectives
Objectives: systemic risk measurement
� Model the time-variation in 2nd order moments
(allowing for other distributional features …
� … to answer questions about joint and conditional
risk (model needed!)
P[ Deutsche stressed, BNP stressed ]
P[ Deutsche stressed | BNP stressed ]
� … and the perceived effectiveness of policies
� Model first order spill-overs and clustering features
and their time variation …
� … and the perceived effectiveness of policies
The models
Models: Generalized Autoregressive Score (GAS)
� Key features:
� Observation driven (likelihood known in closed form)
� General framework for any parametric distribution with
time varying parameters
� Nests many familiar models (GARCH, ACD, MEM, etc)
� Generates many new interesting models
� See: GASMODEL.COM
� Examples: � ��~� 0, �� , ��� � � �� � ����
� � ���
� ��~� 0, �� , � , ��� � � �� � ��������
�������/��
��� � ���
Models: novelties
� Model 1+2:
� Time varying volatilities, time varying correlations/copula
� Skewed and fat tailed conditional distribution for CDS
changes (GH)
� Output: joint and conditional probabilities of stress; daily
calibration of marginal probabilities; relation dynamic
correlations to observables
� Model 3:
� Dynamic spatial dependence model:�� ����� � �� � � , �~��0, !"#$�Σ&�, ��
� Allowance for fat tails
� Spatial weights based on financial cross exposures
� Output: dynamics of spatial weights
The findings
Sovereign findings (1)
Sovereign findings (2)
Sovereign findings (3)
Banking system findings (1): block
equicorrelations
Banking system findings (2): joint risk
falls, but NOT average conditional
risk(>7 defaults)
Sovereign spatial dependence
The summary
Summarizing haiku:
Systemic risks fly high
and low, but caught by scores show
bond buys partly pay.
This project has received funding from the European Union’s
Seventh Framework Programme for research, technological
development and demonstration under grant agreement no° 320270
www.syrtoproject.eu
This document reflects only the author’s views.
The European Union and Sveriges Riksbank are not liable for any use that may be made of the information contained therein.