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

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 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 Systemic Risk in the Interbank Market with Overlapping Portfolios

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Shanshan Jiang ◽  
Hong Fan
Keyword(s):  
Interest Rate ◽  
Systemic Risk ◽  
Banking System ◽  
Bipartite Network ◽  
Investment Risk ◽  
Interbank Market ◽  
Operating Rules ◽  
Proposed Model ◽  
The Stability ◽  
The Impact

The increasing frequency and scope of the financial crisis have attracted more attention in the research of the systemic risk of banking system. A new model for the interbank market with overlapping portfolios is proposed to simulate a banking system in this work. The proposed model uses a bipartite network of banks and their assets to analyze the impact of bank investment on the stability of the banking system. In addition, this model introduces investment risk and allows banks to make up for liquidity by selling devaluated assets, which reflects the operating rules of the banking system more realistically. The results show that allowing banks to sell devaluated assets to make up for liquidity can improve the stability of the banking system and the interbank market can also improve the stability of the banking system. For the investment of banks, the investment risk is an uncertain factor that affects the stability of the banking system. The proposed model further analyzes the impact of average investment interest rate, savings interest rate, deposit reserve ratio, and investment asset diversity on the stability of the banking system. The model provides a tool for policy-makers and supervision agencies to prevent the systemic risk of banking system.

scholarly journals Local Likelihood Density Estimation and Value-at-Risk

2010 ◽  
Vol 2010 ◽  
pp. 1-26 ◽  
Author(s):  
Christian Gourieroux ◽  
Joann Jasiak
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Stock Exchange ◽  
Local Approximation ◽  
Conditional Density ◽  
Conditional Value At Risk ◽  
Computation Method ◽  
Toronto Stock Exchange ◽  
Historical Simulation ◽  
Portfolio Returns

This paper presents a new nonparametric method for computing the conditional Value-at-Risk, based on a local approximation of the conditional density function in a neighborhood of a predetermined extreme value for univariate and multivariate series of portfolio returns. For illustration, the method is applied to intraday VaR estimation on portfolios of two stocks traded on the Toronto Stock Exchange. The performance of the new VaR computation method is compared to the historical simulation, variance-covariance, and J. P. Morgan methods.

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 EVALUATION OF THE RESULTS OF THE EQUITY FUNDS IN THE YEARS 2004–2015 USING VAR AND CVAR MEASURES

2018 ◽  
Vol 17 (3) ◽  
pp. 131-139
Author(s):  
Dorota Żebrowska-Suchodolska
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Risk Measures ◽  
Rank Correlation ◽  
Conditional Value At Risk ◽  
Spearman Rank Correlation ◽  
Spearman Rank ◽  
Equity Funds ◽  
The Stability ◽  
Spearman Rank Correlation Coefficient

The paper presents analysis of the risk and effectiveness of investments in equity funds using value at risk (VaR) and conditional value at risk measures, i.e. reward to value at risk (RVaR) and conditional Sharpe ratio (CS). The study was conducted for 2004–2015, divided into shorter sub-periods (two-, three-, four- and five-year). The stability of the rankings of funds was examined and its significance was verified using the Spearman rank correlation coefficient between subsequent sub-periods. The highest values of measures were observed for 2004–2005. Even then, they were not satisfactory, and the lack of stability of the results does not guarantee that they will be repeated in the future.

scholarly journals STABILITY OF THE RUSSIAN BANKING SECTOR: TRENDS AND RISKS

2021 ◽  
pp. 10-19
Author(s):  
E.V. Travkina ◽  
Keyword(s):  
Commercial Bank ◽  
Banking Sector ◽  
System Analysis ◽  
Negative Impact ◽  
Banking System ◽  
Qualitative Assessment ◽  
The Stability ◽  
Procedural Components ◽  
Statistical Survey ◽  
The Impact

In the modern conditions of functioning of the banking system, the issues that arise with the assess¬ment of the stability of a commercial bank individually and the banking sector as a whole in connection with the aggravation of the negative impact of many risk-forming factors associated with the manifestation of the pandemic are updated. In this regard, a comprehensive systematization of the existing Russian and international practice of implementing a qualitative assessment of the stability of banking organizations becomes important. The purpose of the study is to identify trends in the development of the Russian banking sector and the manifestations of banking risks that have a negative impact on its stability, as well as to identify practical opportunities to reduce the impact of these risks. The following general scientific and special methods were chosen as scientific tools for conducting this study: the method of system analysis, the method of retrospective analysis, as well as the methods of statistical survey. The information base of the study was the statistical data of the Bank of Russia. The theoretical and meth¬odological basis of the study was the works of such researchers as Fetisov G. G., Lavrushin O. I., Tarkhanov E. A., Muraviev A. K. Ovchinnikov O. P., Betz A. Yu., Peresetsky A. A. Kromonov V. S., etc. The study is based on the basic definitions of the stability of banking organizations and the regulatory framework for assessing the stability of the Russian commercial bank, as well as methods, mechanisms and procedural components for assessing the stability of the Russian banking sector. The results of the study are aimed at identifying trends and risks that affect the stability of both the Russian banking system as a whole and individual commercial banks. As practical recom¬mendations, the directions for further sustainable development of the Russian banking sector in the context of the negative impact of the pandemic on the national economy are presented.

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