scholarly journals Backtesting Marginal Expected Shortfall and Related Systemic Risk Measures

2020 ◽  
Author(s):  
Denisa Banulescu-Radu ◽  
Christophe Hurlin ◽  
Jérémy Leymarie ◽  
Olivier Scaillet
Keyword(s):  
At Risk ◽  
Financial Institutions ◽  
Systemic Risk ◽  
Value At Risk ◽  
Risk Measures ◽  
Risk Measure ◽  
Expected Shortfall ◽  
Conditional Value At Risk ◽  
Finite Sample ◽  
Marginal Expected Shortfall

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 financial institutions that contribute the most to the overall risk in the financial system. Our procedure is based on simple tests similar to those generally used to backtest the standard market risk measures such as value-at-risk or expected shortfall. We introduce a concept of violation associated with the marginal expected shortfall (MES), and we define unconditional coverage and independence tests for these violations. We can generalize these tests to any MES-based systemic risk measures such as the systemic expected shortfall (SES), the systemic risk measure (SRISK), or the delta conditional value-at-risk ([Formula: see text]CoVaR). We study their asymptotic properties in the presence of estimation risk and investigate their finite sample performance via Monte Carlo simulations. An empirical application to a panel of U.S. financial institutions is conducted to assess the validity of MES, SRISK, and [Formula: see text]CoVaR forecasts issued from a bivariate GARCH model with a dynamic conditional correlation structure. Our results show that this model provides valid forecasts for MES and SRISK when considering a medium-term horizon. Finally, we propose an early warning system indicator for future systemic crises deduced from these backtests. Our indicator quantifies how much is the measurement error issued by a systemic risk forecast at a given point in time which can serve for the early detection of global market reversals. This paper was accepted by Kay Giesecke, finance.

A LIQUIDATION RISK ADJUSTMENT FOR VALUE AT RISK AND EXPECTED SHORTFALL

2018 ◽  
Vol 21 (03) ◽  
pp. 1850010 ◽  
Author(s):  
LAKSHITHE WAGALATH ◽  
JORGE P. ZUBELLI
Keyword(s):  
At Risk ◽  
Financial Institutions ◽  
Risk Adjustment ◽  
Value At Risk ◽  
Financial Institution ◽  
Risk Measures ◽  
Risk Measure ◽  
Expected Shortfall ◽  
Adjusted Risk ◽  
Risk Managers

This paper proposes an intuitive and flexible framework to quantify liquidation risk for financial institutions. We develop a model where the “fundamental” dynamics of assets is modified by price impacts from fund liquidations. We characterize mathematically the liquidation schedule of financial institutions and study in detail the fire sales resulting endogenously from margin constraints when a financial institution trades through an exchange. Our study enables to obtain tractable formulas for the value at risk and expected shortfall of a financial institution in the presence of fund liquidation. In particular, we find an additive decomposition for liquidation-adjusted risk measures. We show that such a measure can be expressed as a “fundamental” risk measure plus a liquidation risk adjustment that is proportional to the size of fund positions as a fraction of asset market depths. Our results can be used by risk managers in financial institutions to tackle liquidity events arising from fund liquidations better and adjust their portfolio allocations to liquidation risk more accurately.

Essays on measuring systemic risk

2019 ◽  
Author(s):  
Χριστόφορος Κωνσταντάτος
Keyword(s):  
At Risk ◽  
Systemic Risk ◽  
Value At Risk ◽  
Expected Shortfall ◽  
Conditional Value At Risk ◽  
Lehman Brothers ◽  
Marginal Expected Shortfall

Η παρούσα διατριβή ερευνά διάφορα μέτρα συστημικού κινδύνου, αναγνωρίζοντας – προσδιορίζοντάς τα συστημικά τραπεζικά ιδρύματα της Ευρωπαϊκής Νομισματικής Ένωσής (Ευρωζώνης). Επίσης εξετάζει τις ακραίες κινήσεις της τιμής των μετοχών των τραπεζικών ιδρυμάτων της Ευρωζώνης. Η παρούσα αποτελείται από τρία κεφάλαια εστιάζοντας στα τραπεζικά ιδρύματα των Ηνωμένων Πολιτειών και της Ευρωζώνης. Το Κεφάλαιο 2 συγκρίνει τα συστημικά μέτρα τα επονομαζόμενα (i) Delta Conditional Value at Risk, (ii) Marginal Expected Shortfall και (iii) Systemic RISK. Τα αποτελέσματα καταδεικνύουν ότι τα τραπεζικά ιδρύματα της ζώνης του ευρώ συνεισφέρουν τον υψηλότερο κίνδυνο στο χρηματοπιστωτικό σύστημα (συμβολή στον συστημικό κίνδυνο). Επιπροσθέτως είναι και τα πιο ευάλωτα τραπεζικά ιδρύματα σε περίπτωση ύφεσης. Τα τραπεζικά ιδρύματα με τις υψηλότερες αναμενόμενες απώλειες σε περίπτωση ακραίων γεγονότων είναι κυρίως τα τραπεζικά ιδρύματα των ΗΠΑ. Το Κεφάλαιο 3 διερευνά τον συστημικό κίνδυνο που διαχέεται μεταξύ των τραπεζικών ιδρυμάτων των Ηνωμένων Πολιτειών και της Ευρωζώνης κάνοντας χρήση του μέτρου Conditional Value at Risk. Τα αποτελέσματα καταδεικνύουν ότι δύο από τα μεγαλύτερα γερμανικά τραπεζικά ιδρύματα συγκαταλέγονται στα πιο ευάλωτα τραπεζικά ιδρύματα της ζώνης του ευρώ στον συστημικό κινδύνου που προέρχονται από τα αντίστοιχα αμερικανικά τραπεζικά ιδρύματα, επίσης παρατηρείτε υψηλός βαθμός έκθεσης των αμερικανικών τραπεζικών ιδρυμάτων στα τρία μεγαλύτερα γαλλικά τραπεζικά ιδρύματα. Το Κεφάλαιο 4 ερευνά τη δομή εξάρτησης των ουρών των είκοσι τεσσάρων μεγαλύτερων τραπεζών στη ζώνη του ευρώ πριν και μετά την κατάρρευση της Lehman Brothers. Τα αποτελέσματα καταδεικνύουν ότι στη μετά κρίση περίοδο το επίπεδο της ακραίας συσχέτισης αυξάνεται σημαντικά στα τραπεζικά ιδρύματα του πυρήνα του ευρώ. Επίσης, μεταξύ των χωρών που λαμβάνουν δέσμη μέτρων διάσωσης τα μεγαλύτερα τραπεζικά ιδρύματα σε Ελλάδα και Ιρλανδία παρατηρείτε ότι μείωσαν την ακραία συσχέτιση με τα αντίστοιχα τραπεζικά ιδρύματα της ζώνης του ευρώ.

scholarly journals NONPARAMETRIC ESTIMATION OF CONDITIONAL VALUE-AT-RISK AND EXPECTED SHORTFALL BASED ON EXTREME VALUE THEORY

2016 ◽  
Vol 34 (1) ◽  
pp. 23-67 ◽  
Author(s):  
Carlos Martins-Filho ◽  
Feng Yao ◽  
Maximo Torero
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Monte Carlo Study ◽  
Value Theory ◽  
Expected Shortfall ◽  
Scale Model ◽  
Conditional Value At Risk ◽  
Strong Mixing ◽  
Finite Sample ◽  
Nonparametric Estimators

We propose nonparametric estimators for conditional value-at-risk (CVaR) and conditional expected shortfall (CES) associated with conditional distributions of a series of returns on a financial asset. The return series and the conditioning covariates, which may include lagged returns and other exogenous variables, are assumed to be strong mixing and follow a nonparametric conditional location-scale model. First stage nonparametric estimators for location and scale are combined with a generalized Pareto approximation for distribution tails proposed by Pickands (1975, Annals of Statistics 3, 119–131) to give final estimators for CVaR and CES. We provide consistency and asymptotic normality of the proposed estimators under suitable normalization. We also present the results of a Monte Carlo study that sheds light on their finite sample performance. Empirical viability of the model and estimators is investigated through a backtesting exercise using returns on future contracts for five agricultural commodities.

scholarly journals Estimation of the Portfolio Risk from Conditional Value at Risk Using Monte Carlo Simulation

2021 ◽  
Vol 17 (3) ◽  
pp. 370-380
Author(s):  
Ervin Indarwati ◽  
Rosita Kusumawati
Keyword(s):  
At Risk ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Value At Risk ◽  
Risk Measures ◽  
Risk Measure ◽  
Risk Estimation ◽  
Conditional Value At Risk ◽  
Portfolio Risk ◽  
Portfolio Returns

Portfolio risk shows the large deviations in portfolio returns from expected portfolio returns. Value at Risk (VaR) is one method for determining the maximum risk of loss of a portfolio or an asset based on a certain probability and time. There are three methods to estimate VaR, namely variance-covariance, historical, and Monte Carlo simulations. One disadvantage of VaR is that it is incoherent because it does not have sub-additive properties. Conditional Value at Risk (CVaR) is a coherent or related risk measure and has a sub-additive nature which indicates that the loss on the portfolio is smaller or equal to the amount of loss of each asset. CVaR can provide loss information above the maximum loss. Estimating portfolio risk from the CVaR value using Monte Carlo simulation and its application to PT. Bank Negara Indonesia (Persero) Tbk (BBNI.JK) and PT. Bank Tabungan Negara (Persero) Tbk (BBTN.JK) will be discussed in this study.  The  daily  closing  price  of  each  BBNI  and BBTN share from 6 January 2019 to 30 December 2019 is used to measure the CVaR of the two banks' stock portfolios with this Monte Carlo simulation. The steps taken are determining the return value of assets, testing the normality of return of assets, looking for risk measures of returning assets that form a normally distributed portfolio, simulate the return of assets with monte carlo, calculate portfolio weights, looking for returns portfolio, calculate the quartile of portfolio return as a VaR value, and calculate the average loss above the VaR value as a CVaR value. The results of portfolio risk estimation of the value of CVaR using Monte Carlo simulation on PT. Bank Negara Indonesia (Persero) Tbk and PT. Bank Tabungan Negara (Persero) Tbk at a confidence level of 90%, 95%, and 99% is 5.82%, 6.39%, and 7.1% with a standard error of 0.58%, 0.59%, and 0.59%. If the initial funds that will be invested in this portfolio are illustrated at Rp 100,000,000, it can be interpreted that the maximum possible risk that investors will receive in the future will not exceed Rp 5,820,000, Rp 6,390,000 and Rp 7,100,000 at the significant level 90%, 95%, and 99%

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 An evaluation and comparison of Value at Risk and Expected Shortfall

2018 ◽  
Vol 15 (4) ◽  
pp. 17-34 ◽  
Author(s):  
Tom Burdorf ◽  
Gary van Vuuren
Keyword(s):  
At Risk ◽  
Monte Carlo ◽  
Emerging Economies ◽  
Value At Risk ◽  
Risk Measures ◽  
Risk Measure ◽  
Expected Shortfall ◽  
Significance Level ◽  
Tail Conditional Expectation ◽  
The Uk

As a risk measure, Value at Risk (VaR) is neither sub-additive nor coherent. These drawbacks have coerced regulatory authorities to introduce and mandate Expected Shortfall (ES) as a mainstream regulatory risk management metric. VaR is, however, still needed to estimate the tail conditional expectation (the ES): the average of losses that are greater than the VaR at a significance level These two risk measures behave quite differently during growth and recession periods in developed and emerging economies. Using equity portfolios assembled from securities of the banking and retail sectors in the UK and South Africa, historical, variance-covariance and Monte Carlo approaches are used to determine VaR (and hence ES). The results are back-tested and compared, and normality assumptions are tested. Key findings are that the results of the variance covariance and the Monte Carlo approach are more consistent in all environments in comparison to the historical outcomes regardless of the equity portfolio regarded. The industries and periods analysed influenced the accuracy of the risk measures; the different economies did not.

Systemic Risk-Driven Portfolio Selection

2022 ◽  
Author(s):  
Agostino Capponi ◽  
Alexey Rubtsov
Keyword(s):  
At Risk ◽  
Portfolio Selection ◽  
Systemic Risk ◽  
Value At Risk ◽  
Global Financial Crisis ◽  
Risk Measures ◽  
Conditional Value At Risk ◽  
The Face ◽  
Financial Index ◽  
The Global Financial Crisis

How can we construct portfolios that perform well in the face of systemic events? The global financial crisis of 2007–2008 and the coronavirus disease 2019 pandemic have highlighted the importance of accounting for extreme form of risks. In “Systemic Risk-Driven Portfolio Selection,” Capponi and Rubtsov investigate the design of portfolios that trade off tail risk and expected growth of the investment. The authors show how two well-known risk measures, the value-at-risk and the conditional value-at-risk, can be used to construct portfolios that perform well in the face of systemic events. The paper uses U.S. stock data from the S&P500 Financials Index and Canadian stock data from the S&P/TSX Capped Financial Index, and it demonstrates that portfolios accounting for systemic risk attain higher risk-adjusted expected returns, compared with well-known benchmark portfolio criteria, during times of market downturn.

scholarly journals Pomiar ryzyka rynkowego miarą wartoś​ci zagrożonej. Metoda kombinowania prognoz ​

2018 ◽  
pp. 183-198
Author(s):  
Piotr Mazur
Keyword(s):  
At Risk ◽  
Financial Institutions ◽  
Value At Risk ◽  
Risk Measure ◽  
Market Risk ◽  
Risk Measurement ◽  
Conditional Value At Risk ◽  
Parametric Methods ◽  
Combining Forecasts

The article discusses the measurement of market risk by Value at Risk method. Value at Risk measure is an important element of risk measurement mainly for financial institutions but can also be used by other companies. The Value at Risk is presented together with its alternative Conditional Value at Risk. The main methods of VaR estimation were divided into nonparametric, parametric and semi-parametric methods. The next part of the article presents a method of combining forecasts, which can be used in the context of forecasting Value at Risk.

scholarly journals THE DETERMINANTS OF SYSTEMIC RISK: EVIDENCE FROM INDONESIAN COMMERCIAL BANKS

2020 ◽  
Vol 23 (1) ◽  
pp. 101-120
Author(s):  
Mutiara Aini ◽  
Deddy Priatmodjo Koesrindartoto
Keyword(s):  
At Risk ◽  
Systemic Risk ◽  
Commercial Banks ◽  
Value At Risk ◽  
Expected Shortfall ◽  
Quarterly Data ◽  
Marginal Expected Shortfall ◽  
The Government

This paper examines the determinants of systemic risk across Indonesian commercialbanks using quarterly data from 2001Q4 to 2017Q4. Employing four measures ofsystemic risk, namely value-at-risk (VaR), historical marginal expected shortfall(MESH), marginal expected shortfall from GARCH-DCC (MESdcc), and long-runmarginal expected shortfall (LRMES), we find that bank size is positively related tosystemic risk, whereas banks and economic loan activity are negatively related tosystemic risk. These findings suggest that the government needs to regulate loanactivities and to monitor big banks as they have significant impacts on bank systemicrisk.

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