Uncertainty Shocks and Systemic-Risk Indicators

2021 ◽  
pp. 102573
Author(s):  
Nikolay Hristov ◽  
Markus Roth
Keyword(s):  
Systemic Risk ◽  
Risk Indicators ◽  
Uncertainty Shocks

A Survey of Systemic Risk Indicators

2018 ◽  
Author(s):  
Antonio Di Cesare ◽  
Anna Rogantini Picco
Keyword(s):  
Systemic Risk ◽  
Risk Indicators

scholarly journals Systemic Risk Indicators Based on Nonlinear PolyModel

2018 ◽  
Vol 12 (1) ◽  
pp. 2 ◽  
Author(s):  
Xingxing Ye ◽  
Raphael Douady
Keyword(s):  
Financial Market ◽  
Systemic Risk ◽  
Financial Crises ◽  
Economic Benefit ◽  
Risk Indicators ◽  
Large Set ◽  
Theoretical Simulation ◽  
Nonlinear Factor Analysis ◽  
Nonlinear Approach ◽  
Covered Call

The global financial market has become extremely interconnected as it demonstrates strong nonlinear contagion in times of crisis. As a result, it is necessary to measure financial systemic risk in a comprehensive and nonlinear approach. By establishing a large set of risk factors as the main bones of the financial market network and applying nonlinear factor analysis in the form of so-called PolyModel, this paper proposes two systemic risk indicators that can prognosticate the advent and trace the development of financial crises. Through financial network analysis, theoretical simulation, empirical data analysis and final validation, we argue that the indicators suggested in this paper are proved to be very effective in forecasting and tracing the financial crises from 1998 to 2017. The economic benefit of the indicator is evidenced by the enhancement of a protective put/covered call strategy on major stock markets.

scholarly journals The Leaders, the Laggers, and the “Vulnerables”

Risks ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 26
Author(s):  
Veni Arakelian ◽  
Shatha Qamhieh Hashem
Keyword(s):  
Financial Crisis ◽  
Bayesian Network ◽  
Financial Institutions ◽  
Systemic Risk ◽  
Expected Shortfall ◽  
Risk Indicators ◽  
Network Structures ◽  
Lag Effect ◽  
Marginal Expected Shortfall ◽  
The Individual

We examine the lead-lag effect between the large and the small capitalization financial institutions by constructing two global weekly rebalanced indices. We focus on the 10% of stocks that “survived” all the rebalancings by remaining constituents of the indices. We sort them according to their systemic importance using the marginal expected shortfall (MES), which measures the individual institutions’ vulnerability over the market, the network based MES, which captures the vulnerability of the risks generated by institutions’ interrelations, and the Bayesian network based MES, which takes into account different network structures among institutions’ interrelations. We also check if the lead-lag effect holds in terms of systemic risk implying systemic risk transmission from the large to the small capitalization, concluding a mixed behavior compared to the index returns. Additionally, we find that all the systemic risk indicators increase their magnitude during the financial crisis.

Systemic Risk and Trading Strategy Based on Correlation-Based Networks in Stock Markets

2020 ◽  
Vol 19 (03) ◽  
pp. 2050028
Author(s):  
Jia-Wei Yu ◽  
Qin-Qin Huang ◽  
Yong-Han Guo ◽  
Zhi-Qiang Jiang ◽  
Wen-Jie Xie
Keyword(s):  
Financial Markets ◽  
Systemic Risk ◽  
Debt Crisis ◽  
Trading Strategy ◽  
Trading Strategies ◽  
Risk Indicators ◽  
Future Price ◽  
Trading Costs ◽  
Excess Returns ◽  
Systemic Risks

In this paper, we construct five systemic risk indicators and test their performances based on four different datasets. It is observed that the five indicators can accurately indicate the increment of systemic risks during the periods of sub-prime crisis and European debt crisis. Trading strategies based on the risk indicators are further designed to test the warning ability of future price drops. The backtests reveal that trading based on the five indicators provides satisfied excess returns when the trading costs are included. Our results provide insights to find new network-based risk indicators to early warn the systemic risks in financial markets.

scholarly journals SYSTEMIC RISK IN TAIWAN STOCK MARKET

2012 ◽  
Vol 13 (5) ◽  
pp. 895-914 ◽  
Author(s):  
Her-Jiun Sheu ◽  
Chien-Ling Cheng
Keyword(s):  
Financial Crisis ◽  
Stock Market ◽  
Systemic Risk ◽  
Financial Crises ◽  
Idiosyncratic Risk ◽  
Risk Indicators ◽  
2008 Financial Crisis ◽  
Independent Variables ◽  
The Impact ◽  
Dot Com

Recent financial crises resulted from systemic risk caused by idiosyncratic distress. In this research, taking Taiwan stock market as an example and collecting data from 2000 to 2010 which contained the 2001 dot-com bubble and the 2007–2009 financial crisis, we adopt the CoVaR model to empirically explore the impact of sector-specific idiosyncratic risk on the systemic risk of the system and attempt to investigate the links between financial crises, systemic risk and the idiosyncratic risk of a sector-specific anomaly. The result showed sector-specific marginal CoVaR, i.e., ΔCoVaR, perfectly explained Taiwan stock market disturbance during the 2001 dot-com bubble and 2007–2008 financial crisis. Thus, by identifying the larger ΔCoVaR sectors, i.e. the systemic importance sectors, and by exploring the risk indicators, independent variables, of these systemic importance sectors, investors could practically employ the sector-specific ΔCoVaR measure to deepen the systemic risk scrutiny from a macro into a micro prudential perspective.

scholarly journals Cyclical Patterns of Systemic Risk Metrics

2021 ◽  
Vol 21 (28) ◽  
Author(s):  
Plamen Iossifov ◽  
Tomas Dutra Schmidt
Keyword(s):  
Financial Crisis ◽  
Systemic Risk ◽  
Global Financial Crisis ◽  
Policy Implications ◽  
Risk Indicators ◽  
Stress Indicators ◽  
Composite Indices ◽  
Credit Cycle ◽  
Run Up ◽  
High Level

We analyze a range of macrofinancial indicators to extract signals about cyclical systemic risk across 107 economies over 1995–2020. We construct composite indices of underlying liquidity, solvency and mispricing risks and analyze their patterns over the financial cycle. We find that liquidity and solvency risk indicators tend to be counter-cyclical, whereas mispricing risk ones are procyclical, and they all lead the credit cycle. Our results lend support to high-level accounts that risks were underestimated by stress indicators in the run-up to the 2008 global financial crisis. The policy implications of conflicting risk signals would depend on the phase of the credit cycle.

scholarly journals Financial Market and Inflation

2020 ◽  
Vol 12 (6) ◽  
pp. 90
Author(s):  
Yuqing Qi
Keyword(s):  
Systemic Risk ◽  
Predictive Accuracy ◽  
Predictive Ability ◽  
Two Dimensions ◽  
Risk Indicators ◽  
Risk Level ◽  
Individual Risk ◽  
Inflation Risk ◽  
The Future ◽  
The Individual

Based on two dimensions of system risk, this paper studies the changes in the future inflation risk level, and uses the out-of-sample quantile R2  to further evaluate the predictive accuracy of different systemic risk indicators on inflation risk. Firstly, we compute two systemic risk indicators, MES and volatility, with data of Chinese financial institutions. And then we explore the amplification effect of these indicators on future inflation risk, under the framework of quantile regression. We find that systematic risk indicators have a strong predictive ability for the inflation level at various quantiles. MES indicator that reflects individual risk can better predict future deflation risk, while volatility index has a stronger ability to predict inflation risk. We also find that systemic risk indicators of different dimensions have different effects on inflation risk and deflation risk. In general, the MES index, which captures the individual risk of the organization, have a greater impact on the future inflation risk. While indicator that measures volatility in financial markets has more influence on the extreme lower tail of inflation rates. Finally, we predict the distribution of inflation in China from March 2020 to June 2021, and visually show the distribution trend of future inflation with forecast fan charts.

scholarly journals Systemic risk in the banking system: measuring and interpreting the results

2019 ◽  
Vol 14 (3) ◽  
pp. 34-47
Author(s):  
Olena Bezrodna ◽  
Zoia Ivanova ◽  
Yulia Onyshchenko ◽  
Volodymyr Lypchanskyi ◽  
Serhii Rymar
Keyword(s):  
Systemic Risk ◽  
Financial Risk ◽  
Banking System ◽  
Cumulative Effect ◽  
Risk Indicators ◽  
Risk Indicator ◽  
Regime Types ◽  
Systemic Crisis ◽  
Integral Assessment ◽  
Systemically Important Banks

Highly concentrated banking system risks and the cumulative effect due to their accumulation act as a driver for improving the macro-prudential policy implemented by central banks. For this reason, an effectively and comprehensively assessed systemic risk in the banking system is declared an express condition for the early detection of its production sources and blocking of potential spreading channels, reducing the possible implementation. In light of this, the article develops an approach to the aggregated systemic risk assessment and interpretation of its results. The proposed approach is based on the considered influence exerted by financial risks of systemically important banks on the destabilized banking system and interconnections between banks in the context of the possible crisis impulse spreading. The following steps should be accomplished to form an aggregated systemic risk indicator in the banking system. Firstly, the differentiation of systemically important banks by the degree of their systemic importance; secondly, an integral assessment of the bank operation riskiness within certain bank groups; thirdly, the cumulative composition of the corresponding integral indicators, taking into account their weighting coefficients based on two criteria, namely values of the systemic importance indicator differentiating the bank groups, and the correlation of their risks. Interpreting the quantitative measurement results with regard to the systemic risk in the banking system is followed by the recommendations below: the systemic risk grading into high, medium and low levels and the respective definition of the threshold aggregated systemic risk indicator value which informs about the possible systemic crisis when approached; justification of the selected supervision regime types (strengthened, moderate or weakened) for systemically important banks, depending on the riskiness level specific for their operation and the systemic importance degree. The developed approach to measuring the systemic risk by means of constructing an aggregated indicator and interpreting the obtained results was being tested considering the financial risk indicators of the systemically important banks in Ukraine during 2009–2018.

scholarly journals Measurement of Systemic Risk in a Common European Union Risk-Based Deposit Insurance System: Formal Necessity or Value-Adding Process?

Risks ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 137 ◽  
Author(s):  
Aida Barkauskaite ◽  
Ausrine Lakstutiene ◽  
Justyna Witkowska
Keyword(s):  
European Union ◽  
Systemic Risk ◽  
Deposit Insurance ◽  
Current System ◽  
Banking System ◽  
Risk Indicators ◽  
Insurance Fund ◽  
Insurance System ◽  
Deposit Insurance System ◽  
The Stability

Scientific discussions have emphasized that the main problem with the current deposit insurance system is that the current system does not evaluate the risks that banks assume to calculate the deposit insurance premiums in many countries of the European Union (E.U.). Thus, the prevailing system does not safeguard a sufficient level of stability in the banking system. Scientific studies show that the deposit insurance system should consider not only the risk indicators for individual banks, but it must also consider the systemic risk of banks that affects the stability of the banking system. Hence, the question arises as to whether measurements of systemic risk in a common E.U. risk-based deposit insurance system are a formal necessity or if they are a value-adding process. Expanding the discussion of scientists, this article analyzes how contributions to insurance funds would change the banks of Lithuania following the introduction of the E.U.’s overall risk-based deposit insurance system and after taking into consideration the additional systemic risk. The research results that were obtained provide evidence that the introduction of a risk-based deposit insurance system would redistribute payments to the deposit insurance fund between banks operating in Lithuania, and, thereby, would contribute to a reduction in the negative effects of the deposit insurance system and would improve the stability in the financial system.

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