financialriskmeter
low risk of crisis
Green: low risk of crisis in the financial market.
The incidence of a crisis is less likely than usual.
Current risk level since 24/11/2017.
The Financial Risk Meter (FRM) helps you to identify different systemic risk level in the financial market over time. It is an index of the system volatility level which indicates that if FRM is high, then the systemic risk is high.
severe risk
Red: severe risk of a crisis in the financial market. Our risk measure suggests that a financial crisis is imminent or happening right now. This risk level is given for lambda values higher then the 80%-quantile.
high risk
Orange: high risk of crisis in the financial market. A crisis might occur very soon. This risk level is given for lambda values between the 60%-quantile and 80%-quantile.
elevated risk
Yellow: elevated risk of crisis in the financial market. The incidence of a crisis is somewhat higher than usual. This risk level is given for lambda values between the 40%-quantile and 60%-quantile.
general risk
Blue: general risk of crisis in the financial market. There is no specific risk of a crisis. This risk level is given for lambda values between the 20%-quantile and 40%-quantile.
✗ low risk
Green: low risk of crisis in the financial market.
The incidence of a crisis is less likely than usual.
This risk level is given for lambda values lower then the 20%-quantile.
Current risk level since 24/11/2017: 15.1%-quantile
Evolution of risk over time. In 2007 the subprime mortgage crisis started. In 2008 the global financial crisis swept the world, the European sovereign debt crisis broke out in the same year. After 2013 the global economy is showing signs of the slow recovery from the recession. High or low levels of systemic risk play different roles in each period. You may get a clue or some evidence of these financial events by following the timeline and its corresponding volatility level in this interactive chart, on which you can observe how the systemic risk evolved over time by exploring the historical data of 200 US financial firms.
Interactive moving time window: select desired frame in lower graph.
We propose a linear lasso measure to estimate systemic interconnectedness across financial institutions based on tail-driven spill-over effects in an ultra-high dimensional framework. Methodologically, we employ a variable selection technique in a time series setting for a linear quantile regression framework. We can thus include more financial institutions into the analysis, to measure their interdependencies in tails.
Then FRM is induced from this model which is the averaged tuning parameter lambda from lasso technique. The estimation method of it is cross validation. In application we apply 100 US publicly traded financial institutions and 6 macro state variables to estimate this index. Previously we have used 200 financial institutions, after comparison we find out that using 100 firms is more efficient way.
Composite Quantile Regression for the Single-Index Model (2013)
SFB 649 Discussion Paper 2013-010
Yan Fan, Wolfgang Karl Härdle, Weining Wang and Lixing Zhu
TENET: Tail-Event driven NETwork risk (2014)
SFB 649 Discussion Paper 2014-066
Wolfgang Karl Härdle, Weining Wang, Lining Yu
Financial Network Systemic Risk Contributions (2014)
Review of Finance
Nikolaus Hautsch, Julia Schaumburg and
Melanie Schienle
Forecasting systemic impact in financial networks (2014)
International Journal of Forecasting, Vol. 30
Nikolaus Hautsch, Julia Schaumburg and
Melanie Schienle
Quantile Lasso Regression for Single Index Model (2014)
Master Thesis, Ladislaus von Bortkiewicz Chair of Statistics
Lining Yu
Systemic Risk Spillovers in the European Banking and Sovereign Network (2014)
CFS Working Paper, No. 467
Frank Betz, Nikolaus Hautsch, Tuomas A. Peltonen and
Melanie Schienle
(responsible until January 2016):
Automation of data collection,
optimization and parallelization of code,
data visualization, Google trends
Technical implementation and visualization
Theoretical core driver,
scientific advice
Theoretical framework,
Quantile Lasso Regression algorithms,
scientific advice
Theoretical core driver,
scientific advice
Professor Ladislaus von Bortkiewicz Lehrstuhl für Statistik, Humboldt-Universität zu Berlin
Theoretical core driver,
scientific advice
Theoretical core driver,
scientific advice
Theoretical core driver,
scientific advice
Theoretical core driver,
scientific advice
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