Extreme value approach to CoVaR estimation
@inproceedings{Nolde2019ExtremeVA,
title={Extreme value approach to CoVaR estimation},
author={Natalia Nolde and Chen Zhou and Mengling Zhou},
year={2019}
}The global financial crisis of 2007-2009 highlighted the crucial role systemic risk plays in ensuring stability of financial markets. Accurate assessment of systemic risk would enable regulators to introduce suitable policies to mitigate the risk as well as allow individual institutions to monitor their vulnerability to market movements. One popular measure of systemic risk is the conditional value-at-risk (CoVaR), proposed in Adrian and Brunnermeier (2011). We develop a methodology to estimate…
Figures and Tables from this paper
table 2.1 
figure 3.1 
table 3.1 
figure 3.10 
figure 3.11 
figure 3.12 
figure 3.13 
figure 3.14 
figure 3.15 
figure 3.16 
figure 3.17 
figure 3.18 
figure 3.19 
figure 3.2 
table 3.2 
figure 3.20 
figure 3.21 
figure 3.3 
figure 3.4 
figure 3.5 
figure 3.6 
figure 3.7 
figure 3.8 
figure 3.9 
figure 4.1 
table 4.1 
figure 4.2 
table 4.2 
figure 4.3 
table 4.3 
figure 4.4 
figure 4.5 
figure 4.6 
figure 4.7 
figure B.1 
figure B.2 
figure B.3 
figure B.4 
figure B.5 
figure B.6
References
SHOWING 1-10 OF 64 REFERENCES
Conditional Extremes in Asymmetric Financial Markets
- EconomicsJournal of Business & Economic Statistics
- 2018
ABSTRACT The global financial crisis of 2007–2009 revealed the great extent to which systemic risk can jeopardize the stability of the entire financial system. An effective methodology to quantify…
Systemic Risk Measurement: Multivariate GARCH Estimation of CoVaR
- Economics
- 2012
We modify Adrian and Brunnermeier’s (2011) CoVaR, the VaR of the financial system conditional on an institution being in financial distress. We change the definition of financial distress from an…
Backtesting Marginal Expected Shortfall and Related Systemic Risk Measures
- EconomicsSSRN Electronic Journal
- 2019
This paper proposes an original approach for backtesting systemic risk measures. This backtesting approach makes it possible to assess the systemic risk measure forecasts used to identify the…
Estimation of tail-related risk measures for heteroscedastic financial time series: an extreme value approach
- Economics
- 2000
Measuring Systemic Risk
- Economics
- 2001
The ability of components of SES to predict emerging systemic risk during the financial crisis of 2007-2009 is demonstrated, in particular, the outcome of stress tests performed by regulators; the decline in equity valuations of large financial firms in the crisis; and the widening of their credit default swap spreads.
Value-at-Risk Prediction: A Comparison of Alternative Strategies
- Economics
- 2005
Given the growing need for managing financial risk, risk prediction plays an increasing role in banking and finance. In this study we compare the out-of-sample performance of existing methods and…
Backtesting Systemic Risk Forecasts using Multi-Objective Elicitability
- Economics
- 2021
Backtesting risk measure forecasts requires identifiability (for model validation) and elicitability (for model comparison). The systemic risk measures CoVaR (conditional value-atrisk), CoES…
Elicitability and backtesting: Perspectives for banking regulation
- Economics
- 2016
Conditional forecasts of risk measures play an important role in internal risk management of financial institutions as well as in regulatory capital calculations. In order to assess forecasting…
On dependence consistency of CoVaR and some other systemic risk measures
- Computer Science
- 2012
This paper compares two alternative notions of Conditional Value-at-Risk (CoVaR) available in the current literature and proves general results that relate the dependence consistency of CoVaR using conditioning on X ≥ VaR α ( X ) to well established results on concordance ordering of multivariate distributions or their copulas.
Techniques for Verifying the Accuracy of Risk Measurement Models
- Economics
- 1995
It does not appear possible for a bank or its supervisor to reliably verify the accuracy of an institution's internal model loss exposure estimates using standard statistical techniques, and the results have implications both for banks that wish to assess the accuracies of their internal risk measurement models as well as for supervisors who must verify the accurate estimate.