Systemic risk signaling using scores - Andre Lucas, Bernd Schwaab, Xin Zhang, Francisco Blasques,...

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.