scholarly journals Systemic Risk Analysis Using Conditional Value at Risk (CoVaR) Model: Study of Conventional Banks in Indonesia

2020 ◽  
Vol 12 (1) ◽  
pp. 57-67
Author(s):  
Rihana Nabella ◽  
Ghozali Maski ◽  
Setyo Wahyudi
Keyword(s):  
At Risk ◽  
Risk Analysis ◽  
Systemic Risk ◽  
Value At Risk ◽  
Conditional Value At Risk ◽  
Model Study ◽  
Conventional Banks

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 Analysis through the Half-Normal Distribution

Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 2080
Author(s):  
Maria-Teresa Bosch-Badia ◽  
Joan Montllor-Serrats ◽  
Maria-Antonia Tarrazon-Rodon
Keyword(s):  
At Risk ◽  
Risk Analysis ◽  
Normal Distribution ◽  
Value At Risk ◽  
Financial Risk ◽  
Risk Model ◽  
Risk Measures ◽  
Risk Indicators ◽  
Conditional Value At Risk ◽  
Risk Matrices

We study the applicability of the half-normal distribution to the probability–severity risk analysis traditionally performed through risk matrices and continuous probability–consequence diagrams (CPCDs). To this end, we develop a model that adapts the financial risk measures Value-at-Risk (VaR) and Conditional Value at Risk (CVaR) to risky scenarios that face only negative impacts. This model leads to three risk indicators: The Hazards Index-at-Risk (HIaR), the Expected Hazards Damage (EHD), and the Conditional HIaR (CHIaR). HIaR measures the expected highest hazards impact under a certain probability, while EHD consists of the expected impact that stems from truncating the half-normal distribution at the HIaR point. CHIaR, in turn, measures the expected damage in the case it exceeds the HIaR. Therefore, the Truncated Risk Model that we develop generates a measure for hazards expectations (EHD) and another measure for hazards surprises (CHIaR). Our analysis includes deduction of the mathematical functions that relate HIaR, EHD, and CHIaR to one another as well as the expected loss estimated by risk matrices. By extending the model to the generalised half-normal distribution, we incorporate a shape parameter into the model that can be interpreted as a hazard aversion coefficient.

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 Impact of diversification on systemic risk of conventional banks listed on the Indonesia Stock Exchange

2020 ◽  
Vol 15 (4) ◽  
pp. 80-87
Author(s):  
Musa Fresno ◽  
Dewi Hanggraeni
Keyword(s):  
Systemic Risk ◽  
Value At Risk ◽  
Financial Stability ◽  
Stock Exchange ◽  
Conditional Value At Risk ◽  
Revenue Diversification ◽  
Variable Approach ◽  
Systemic Risks ◽  
Conventional Banks ◽  
The Impact

It is believed that bank diversification increases financial stability. However, several theories argue that diversification can trigger the spread of failure because of the increased interconnectivity between institutions. The aim of this study is to determine the impact of diversification on the systemic risk of banks. The sample of the study consists of 21 conventional banks listed on the Indonesia Stock Exchange from 2009 to 2018. The study uses firm-year fixed effect panel regression and an instrumental variable approach to examine how firm-specific variables determine the level of systemic risk. Diversification is measured by bank assets, funding, and revenue diversification. To measure the systemic risk, the Conditional Value-at-Risk (ΔCoVaR) methodology is applied. The results show that an increase in funding diversification leads to a decrease in ΔCoVaR, indicating that funding diversification exacerbates the level of systemic risk, whereas asset diversification and revenue diversification do not have significant effects on the level of systemic risk. The empirical findings suggest that the interconnectivity between banks should be reduced by limiting the diversification of funding in the banks to minimize their systemic risks.

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.

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