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SYRTO/LABEX ReFi Closing Conference
Paris, February 2016
Joint work with: István Barra (a)
Francisco Blasques (a)
Siem Jan Koopman (a,b) Rutger Jan Lange (a) Michiel van de Leur (a) Rutger Lit (a) Federico Nucera (c) Julia Schaumburg (a) Bernd Schwaab (d,*) Arjen Siegmann (a) Xin Zhang (e,*)
a)Vrije Universiteit Amsterdam and Tinbergen Institute b)CREATES c) Luiss, Rome d) ECB, these are not the opinions of the ECB e) Riksbank, these are not the opinions of the Riksbank
André Lucas
Systemic Risk Indicators
SYstemic Risk TOmography: Signals, Measurements, Transmission Channels, and Policy Interven@ons
(Gerlach, 2009: policy note to European Parliament)
Financial surveillance before the current crisis erupted suggested that problems were forming but the indica@ons were too imprecise to permit a policy response.
Work is currently being undertaken to improve the measurement, monitoring and management of systemic risk.
That requires it to be defined, which is unproblema5c, and opera5onalized, which is not.
While promising methods to measure risk exist, the data demands are so pronounced that sta5s5cal risk monitoring will remain an imprecise science for years to come.
Where are we now ?
Types of systemic risk
• Level of systemic risk – is systemic risk currently high or low: ``objec@ve’’ policy trigger
• Dynamics of systemic risk – is systemic risk building up or not, growing misalignments, bubbles, growing linkages
• Distribu@on of systemic risk – finding biggest systemic risk contributors, targeNed monitoring
Types of measurements
• Market prices – forward looking (stock markets, yields, CDS), … – but also possibly misaligned risk cycles (Minksy)
– signals typically coincidental
ESRB Risk Dashboard (2016) Lucas, Schwaab, Zhang (2014, SYRTO/JBES): Condi@onal euro area sovereign default risk Lucas, Schwaab, Zhang (2016, SYRTO/JAppEctr): Measuring Credit Risk in a Large Banking System: Econometric Modeling and Empirics
Types of measurements
• Fundamentals versus experience, or versus prices – create a benchmark (fundamental) and see whether data are aligned with the fundamentals
– typically more leading
Creal, Schwaab, Koopman, Lucas (2014, SYRTO/REStat): Observa@on Driven Mixed-‐ Measurement Dynamic Factor Models with an Applica@on to Credit Risk Koopman, Lucas, Schwaab (2014, SYRTO/IJF): Nowcas@ng and forecas@ng global financial sector stress and credit market disloca@on
Creal, Schwaab, Koopman, Lucas (2014, SYRTO/REStat): Observa@on Driven Mixed-‐ Measurement Dynamic Factor Models with an Applica@on to Credit Risk Koopman, Lucas, Schwaab (2014, SYRTO/IJF): Nowcas@ng and forecas@ng global financial sector stress and credit market disloca@on
Schwaab, Koopman, Lucas (2016, SYRTO/JAppEctr): Global Credit Risk: World, Country and Industry Factors
ESRB Risk Dashboard (2016) Schwaab, Koopman, Lucas (2016, SYRTO/JAppEctr): Global Credit Risk: World, Country and Industry Factors Creal, Schwaab, Koopman, Lucas (2014, SYRTO/REStat): Observa@on Driven Mixed-‐ Measurement Dynamic Factor Models with an Applica@on to Credit Risk Koopman, Lucas, Schwaab (2014, SYRTO/IJF): Nowcas@ng and forecas@ng global financial sector stress and credit market disloca@on
Koopman, Lit, Lucas (2016, SYRTO): A decomposi@on of economic and financial @me series into business and financial cycles
Types of measurements
• Network structures – create summary measures of the network structure – leading or coincidental? – value-‐added to macro summaries?
Types of measurements
• Text parsing – count posi@ve and nega@ve news – news on linkages, even if indirect ?
Garmaev, Rus@ge, Lammers, Borovkova (2016, VU): Systemic Risk: A News Sen@ment based Approach
SRisk
SensR
Summary and conclusions
• Price based informa@on largely coincidental • Misalignments more promising in lead @mes, though also
more data/methodology intensive • Network data appear to add new informa@on: which?
And how useful?
• Research direc@ons – beNer understanding of the genesis of risks and imbalances;
find appropriate proxies – exploi@ng new network data (benchmarking will be hard) – exploi@ng text or other big data sources – measuring and exploi@ng misalignments
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 is not liable for any use that may be made of the information contained therein.