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 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.

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.

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 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 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

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