CoVaR

2016 ◽  
Vol 106 (7) ◽  
pp. 1705-1741 ◽  
Author(s):  
Tobias Adrian ◽  
Markus K. Brunnermeier
Keyword(s):  
At Risk ◽  
Financial Crisis ◽  
Systemic Risk ◽  
Value At Risk ◽  
Asset Price ◽  
Financial System ◽  
Maturity Mismatch ◽  
Out Of Sample ◽  
Forward Looking

We propose a measure of systemic risk, Δ CoVaR, defined as the change in the value at risk of the financial system conditional on an institution being under distress relative to its median state. Our estimates show that characteristics such as leverage, size, maturity mismatch, and asset price booms significantly predict Δ CoVaR. We also provide out-of-sample forecasts of a countercyclical, forward-looking measure of systemic risk, and show that the 2006:IV value of this measure would have predicted more than one-third of realized Δ CoVaR during the 2007–2009 financial crisis. (JEL C58, E32, G01, G12, G17, G20, G32)

scholarly journals Measuring Systemic Risk of Banking in Indonesia: Conditional Value at Risk Model Application

2017 ◽  
Vol 6 (2) ◽  
pp. 301-318
Author(s):  
Harjum Muharam ◽  
Erwin Erwin
Keyword(s):  
At Risk ◽  
Financial Institutions ◽  
Systemic Risk ◽  
Value At Risk ◽  
Risk Model ◽  
Financial System ◽  
Conditional Value At Risk ◽  
Spearman Correlation ◽  
Weak Correlation ◽  
Model Application

Systemic risk is a risk of collapse of the financial system that would cause the financial system is not functioning properly. Measurement of systemic risk in the financial institutions, especially banks are crucial, because banks are highly vulnerable to financial crisis. In this study, to estimate the conditional value-at-risk (CoVaR) used quantile regression. Samples in this study of 9 banks have total assets of the largest in Indonesia. Testing the correlation between VaR and ΔCoVaR in this study using Spearman correlation and Kendall's Tau. There are five banks that have a significant correlation between VaR and ΔCoVaR, meanwhile four others banks in the sample did not have a significant correlation. However, the correlation coefficient is below 0.50, which indicates that there is a weak correlation between VaR and CoVaR.DOI: 10.15408/sjie.v6i2.5296

scholarly journals Systemic Risk Modeling with Lévy Copulas

2021 ◽  
Vol 14 (6) ◽  
pp. 251
Author(s):  
Yuhao Liu ◽  
Petar M. Djurić ◽  
Young Shin Kim ◽  
Svetlozar T. Rachev ◽  
James Glimm
Keyword(s):  
At Risk ◽  
Systemic Risk ◽  
Value At Risk ◽  
Joint Distribution ◽  
Risk Measure ◽  
Tail Dependence ◽  
Risk Modeling ◽  
Jones Index ◽  
Out Of Sample ◽  
Copula Approach

We investigate a systemic risk measure known as CoVaR that represents the value-at-risk (VaR) of a financial system conditional on an institution being under distress. For characterizing and estimating CoVaR, we use the copula approach and introduce the normal tempered stable (NTS) copula based on the Lévy process. We also propose a novel backtesting method for CoVaR by a joint distribution correction. We test the proposed NTS model on the daily S&P 500 index and Dow Jones index with in-sample and out-of-sample tests. The results show that the NTS copula outperforms traditional copulas in the accuracy of both tail dependence and marginal processes modeling.

scholarly journals CoCDaR and mCoCDaR: New Approach for Measurement of Systemic Risk Contributions

2020 ◽  
Vol 13 (11) ◽  
pp. 270
Author(s):  
Rui Ding ◽  
Stan Uryasev
Keyword(s):  
At Risk ◽  
Systemic Risk ◽  
Value At Risk ◽  
Financial System ◽  
Conditional Value At Risk ◽  
New Approach ◽  
Systemic Risks ◽  
Study Results ◽  
Risk Contribution

Systemic risk is the risk that the distress of one or more institutions trigger a collapse of the entire financial system. We extend CoVaR (value-at-risk conditioned on an institution) and CoCVaR (conditional value-at-risk conditioned on an institution) systemic risk contribution measures and propose a new CoCDaR (conditional drawdown-at-risk conditioned on an institution) measure based on drawdowns. This new measure accounts for consecutive negative returns of a security, while CoVaR and CoCVaR combine together negative returns from different time periods. For instance, ten 2% consecutive losses resulting in 20% drawdown will be noticed by CoCDaR, while CoVaR and CoCVaR are not sensitive to relatively small one period losses. The proposed measure provides insights for systemic risks under extreme stresses related to drawdowns. CoCDaR and its multivariate version, mCoCDaR, estimate an impact on big cumulative losses of the entire financial system caused by an individual firm’s distress. It can be used for ranking individual systemic risk contributions of financial institutions (banks). CoCDaR and mCoCDaR are computed with CVaR regression of drawdowns. Moreover, mCoCDaR can be used to estimate drawdowns of a security as a function of some other factors. For instance, we show how to perform fund drawdown style classification depending on drawdowns of indices. Case study results, data, and codes are posted on the web.

A simple isochore model evidencing regulation risk

2018 ◽  
Vol 12 (2) ◽  
pp. 233-248 ◽  
Author(s):  
J. Lévy Véhel
Keyword(s):  
At Risk ◽  
Systemic Risk ◽  
Value At Risk ◽  
Solvency Ii ◽  
Basel Ii ◽  
Minimal Volume ◽  
Model Based

AbstractIn this note, we provide a simple example of regulation risk. The idea is that, in certain situations, the very prudential rules (or, rather, some of them) imposed by the regulator in the framework of the Basel II/III Accords or Solvency II directive are themselves the source of a systemic risk. The instance of regulation risk that we bring to light in this work can be summarised as follows: wrongly assuming that prices evolve in a continuous fashion when they may in fact display large negative jumps, and trying to minimise Value at Risk (VaR) under a constraint of minimal volume of activity leads in effect to behaviours that will maximise VaR. Although much stylised, our analysis highlights some pitfalls of model-based regulation.

scholarly journals Bank Value at Risk (VAR) disclosures. A missed leading indicator to the Financial Crisis of 2008?

2020 ◽  
Vol 9 (3) ◽  
pp. 1
Author(s):  
Kiran Parthasarathy
Keyword(s):  
At Risk ◽  
Financial Crisis ◽  
Repeated Measures ◽  
Value At Risk ◽  
Annual Reports ◽  
Us Economy ◽  
Leading Indicator ◽  
Financial Crisis Of 2008 ◽  
Single Number ◽  
Risk Disclosures

The financial crisis of 2008 led to devastating consequences such as bankruptcies and recession in the US economy. Many big banks were at the forefront owing to their risk exposures and open positions. Prior research documents that bank financial statements did not provide adequate lead indicators on the looming crisis in reducing information asymmetry. However, there is no prior research focused on the sufficiency of risk disclosures around this time period. This paper seeks to address this gap using Bank Value at Risk (VAR), a single number publicly disclosed in the annual reports of banks. Bank VAR attempts to quantify the worst possible loss the bank expects to have on its trading portfolios under normal market conditions. Using hand-collected data from the annual reports of the top twelve US banks, this study documents that the change in VAR was steady and positive until the point of the crisis and then decreased in the years thereafter. A repeated-measures analysis of variance model is used to study whether two indicators of VAR (year-to-year change in VAR and log-transformed ratio of VAR to the total trading revenue) differ from pre-crisis to the post-crisis levels. Both VAR indicators reveal an increasing trend pre-crisis and are significantly higher pre-crisis compared to post-crisis. This opens the possibility that the trend of VAR might have information content as a potential leading indicator of the crisis. The finding sheds light on efficacy of risk analysis in ­­bank trading portfolios and could have implications for governance.

scholarly journals MENGUKUR RISIKO SISTEMIK DAN KETERKAITAN FINANSIAL PERBANKAN DI INDONESIA

2014 ◽  
Vol 16 (2) ◽  
pp. 103-125 ◽  
Author(s):  
Sri Ayomi ◽  
Bambang Hermanto
Keyword(s):  
At Risk ◽  
Systemic Risk ◽  
Value At Risk ◽  
Jel Classification ◽  
Conditional Value At Risk ◽  
Probability Of Default ◽  
Insolvency Risk ◽  
Merton Model ◽  
Risk Probability

This paper measures the insolvency risk of bank in Indonesia. We apply Merton model to identify the probability of defaul tover 30 banks during the period of 2002-2013. This paper also identify role of financial linkage a cross banks on transmitting from one bank to another; which enable us to assess if the risk is systemic or not. The results showed the larger total asset of the bank, the larger they contribute to systemic risk. Keywords : Conditional Value at Risk; Probability of Default; systemic risk and financial linkages;Value at Risk. JEL Classification: D81, G21, G33

scholarly journals Risk-based constraints with correlated uncertainties for the optimal operation of an energy community

2021 ◽  
Author(s):  
Mihály Dolányi ◽  
Kenneth Bruninx ◽  
Jean-François Toubeau ◽  
Erik Delarue
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Measurement Data ◽  
Real Life ◽  
Optimal Operation ◽  
Conditional Value At Risk ◽  
Forecast Errors ◽  
Worst Case ◽  
Life Measurement ◽  
Out Of Sample

<div>This paper formulates an energy community's centralized optimal bidding and scheduling problem as a time-series scenario-driven stochastic optimization model, building on real-life measurement data. In the presented model, a surrogate battery storage system with uncertain state-of-charge (SoC) bounds approximates the portfolio's aggregated flexibility. </div><div>First, it is emphasized in a stylized analysis that risk-based energy constraints are highly beneficial (compared to chance-constraints) in coordinating distributed assets with unknown costs of constraint violation, as they limit both violation magnitude and probability. The presented research extends state-of-the-art models by implementing a worst-case conditional value at risk (WCVaR) based constraint for the storage SoC bounds. Then, an extensive numerical comparison is conducted to analyze the trade-off between out-of-sample violations and expected objective values, revealing that the proposed WCVaR based constraint shields significantly better against extreme out-of-sample outcomes than the conditional value at risk based equivalent.</div><div>To bypass the non-trivial task of capturing the underlying time and asset-dependent uncertain processes, real-life measurement data is directly leveraged for both imbalance market uncertainty and load forecast errors. For this purpose, a shape-based clustering method is implemented to capture the input scenarios' temporal characteristics.</div>

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