scholarly journals Analysing systemic risk contribution using a closed formula for conditional value at risk through copula

2015 ◽  
Vol 9 (1) ◽  
Author(s):  
Brice Hakwa ◽  
Manfred Jäger-Ambrożewicz ◽  
Barbara Rüdiger
Keyword(s):  
At Risk ◽  
Systemic Risk ◽  
Value At Risk ◽  
Conditional Value At Risk ◽  
Closed Formula ◽  
Risk Contribution

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.

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 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 How We Predict the Stability of Financial Sector: The Conditional Value at Risk Technique Approach

2020 ◽  
Author(s):  
David Kaluge
Keyword(s):  
At Risk ◽  
Systemic Risk ◽  
Value At Risk ◽  
Negative Impact ◽  
Stock Exchange ◽  
Banking System ◽  
Conditional Value At Risk ◽  
Negative Contribution ◽  
The Stability ◽  
The Impact

This study aims to identify the level of systemic risk of each bank and the financial linkages between banks in Indonesia. In this study, researcher uses 41 banks that have been actively traded on the Indonesia Stock Exchange in the period 2013-2018. The data of stock capitalization of banks are used as prices in a portfolio of banking system. The method used in this study is the CVaR (Conditional Value at Risk) method which was introduced by Adrian and Brunerrmeir in 2008. The equilibrium of the system is assumed reached at optimum portfolio of the system. At this situation each bank contribution to systemic risk is analyzed, as well as its impact onto it when there is a change in capitalization of a certain bank. The result shows the impact of bank onto systemic risk is not always follow its size in contribution the systemic risk. Due to covariance’s among banks are some positive and others are negative, some banks have negative contribution to systemic risk while others’ are positive. There are 4 banks that have different behavior. These banks have negative contribution to the systemic risk. These banks are BMRI, PNBN, PNBS and NAGA. The negative impact to systemic risk is dominated by BMRI as much as -0.17%, and by PNBN as much as -0.04%. There are 2 major banks that have contribution to systemic risk; BBCA (3,01% or Rp 59,1 trillion) and BBRI (0,54% Rp 10,62 trillion). However their impact on systemic risk are different. The parameters of impact on systemic for BBCA and BBRI are 14,99% and 52,94% respectively. Thus the stability of the system is more sensitive to the volatility of Bank Rakyat Indonesia (BBRI) than of Bank Central Asia (BBCA). Keywords: Systemic Risk, Financial Linkage, Value at Risk, Conditional Value at Risk, covariance banking

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 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.

Risk-Based Loan Pricing: Portfolio Optimization Approach with Marginal Risk Contribution

2020 ◽  
Vol 66 (8) ◽  
pp. 3735-3753 ◽  
Author(s):  
So Yeon Chun ◽  
Miguel A. Lejeune
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Mixed Integer ◽  
Conditional Value At Risk ◽  
Optimization Approach ◽  
Portfolio Risk ◽  
Loan Pricing ◽  
Acceptance Probability ◽  
Risk Contribution ◽  
Marginal Risk

We consider a lender (bank) that determines the optimal loan price (interest rate) to offer to prospective borrowers under uncertain borrower response and default risk. A borrower may or may not accept the loan at the price offered, and both the principal loaned and the interest income become uncertain because of the risk of default. We present a risk-based loan pricing optimization framework that explicitly takes into account the marginal risk contribution, the portfolio risk, and a borrower’s acceptance probability. Marginal risk assesses the incremental risk contribution of a prospective loan to the bank’s overall portfolio risk by capturing the dependencies between the prospective loan and the existing portfolio and is evaluated with respect to the value-at-risk and conditional value-at-risk measures. We examine the properties and computational challenges of the formulations. We design a reformulation method based on the concavifiability concept to transform the nonlinear objective functions and to derive equivalent mixed-integer nonlinear reformulations with convex continuous relaxations. We also extend the approach to multiloan pricing problems, which feature explicit loan selection decisions in addition to pricing decisions. We derive formulations with multiple loans that take the form of mixed-integer nonlinear problems with nonconvex continuous relaxations and develop a computationally efficient algorithmic method. We provide numerical evidence demonstrating the value of the proposed framework, test the computational tractability, and discuss managerial implications. This paper was accepted by Chung Piaw Teo, optimization.

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.

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