scholarly journals A Bayesian Entropy Approach to Sectoral Systemic Risk Modeling

Entropy ◽  
2020 ◽  
Vol 22 (12) ◽  
pp. 1371
Author(s):  
Radu Lupu ◽  
Adrian Cantemir Călin ◽  
Cristina Georgiana Zeldea ◽  
Iulia Lupu
Keyword(s):  
Real Estate ◽  
Systemic Risk ◽  
Risk Measures ◽  
Estimation Method ◽  
Spillover Effects ◽  
Expected Shortfall ◽  
Risk Modeling ◽  
Capital Goods ◽  
Entropy Estimation ◽  
Marginal Expected Shortfall

We investigate the dynamics of systemic risk of European companies using an approach that merges paradigmatic risk measures such as Marginal Expected Shortfall, CoVaR, and Delta CoVaR, with a Bayesian entropy estimation method. Our purpose is to bring to light potential spillover effects of the entropy indicator for the systemic risk measures computed on the 24 sectors that compose the STOXX 600 index. Our results show that several sectors have a high proclivity for generating spillovers. In general, the largest influences are delivered by Capital Goods, Banks, Diversified Financials, Insurance, and Real Estate. We also bring detailed evidence on the sectors that are the most pregnable to spillovers and on those that represent the main contributors of spillovers.

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 Backtesting Marginal Expected Shortfall and Related Systemic Risk Measures

2019 ◽  
Author(s):  
Denisa Banulescu ◽  
Christophe Hurlin ◽  
Jeremy Leymarie ◽  
Olivier Scaillet
Keyword(s):  
Systemic Risk ◽  
Risk Measures ◽  
Expected Shortfall ◽  
Marginal Expected Shortfall

A Riskmas Carol

2020 ◽  
pp. 097215092097073
Author(s):  
Matteo Foglia ◽  
Eliana Angelini
Keyword(s):  
Stock Market ◽  
Event Study ◽  
Systemic Risk ◽  
Risk Measures ◽  
Expected Shortfall ◽  
Study Approach ◽  
Santa Claus ◽  
Marginal Expected Shortfall ◽  
Positive Effect ◽  
Systemically Important Banks

Do you believe in Santa Claus (rally)? This study investigates the existence of the ‘Santa Claus rally’ in bank systemic risk. Christmas rally describes a persistent rise in the stock market during the final week of December through the first two trading days in January. In this article, we evaluate this calendar effect, focusing on systemic risk measures for global systemically important banks (GSIBs). First, we estimate the three popular systemic risk measures (DCoVaR, marginal expected shortfall [MES] and SRISK), and then we use an event study approach to analyse the reaction of risk. The results support the existence of Santa Claus. We find that the arrival of Santa Claus has a positive effect on systemic risk, that is, a reduction in bank systemic risk.

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.

Conditional excess risk measures and multivariate regular variation

2019 ◽  
Vol 36 (1-4) ◽  
pp. 1-23
Author(s):  
Bikramjit Das ◽  
Vicky Fasen-Hartmann
Keyword(s):  
Risk Factors ◽  
Asymptotic Behavior ◽  
Systemic Risk ◽  
Regular Variation ◽  
Risk Measures ◽  
Excess Risk ◽  
Multivariate Regular Variation ◽  
Marginal Expected Shortfall ◽  
Broad Framework ◽  
Heavy Tailed

Abstract Conditional excess risk measures like Marginal Expected Shortfall and Marginal Mean Excess are designed to aid in quantifying systemic risk or risk contagion in a multivariate setting. In the context of insurance, social networks, and telecommunication, risk factors often tend to be heavy-tailed and thus frequently studied under the paradigm of regular variation. We show that regular variation on different subspaces of the Euclidean space leads to these risk measures exhibiting distinct asymptotic behavior. Furthermore, we elicit connections between regular variation on these subspaces and the behavior of tail copula parameters extending previous work and providing a broad framework for studying such risk measures under multivariate regular variation. We use a variety of examples to exhibit where such computations are practically applicable.

scholarly journals Modelling systemic risk using neural network quantile regression

2021 ◽  
Author(s):  
Georg Keilbar ◽  
Weining Wang
Keyword(s):  
Neural Network ◽  
Quantile Regression ◽  
Systemic Risk ◽  
Value At Risk ◽  
Risk Measures ◽  
Risk Index ◽  
Hazard Index ◽  
Nonlinear Effects ◽  
Spillover Effects ◽  
Conditional Value At Risk

AbstractWe propose a novel approach to calibrate the conditional value-at-risk (CoVaR) of financial institutions based on neural network quantile regression. Building on the estimation results, we model systemic risk spillover effects in a network context across banks by considering the marginal effects of the quantile regression procedure. An out-of-sample analysis shows great performance compared to a linear baseline specification, signifying the importance that nonlinearity plays for modelling systemic risk. We then propose three network-based measures from our fitted results. First, we use the Systemic Network Risk Index (SNRI) as a measure for total systemic risk. A comparison to the existing network-based risk measures reveals that our approach offers a new perspective on systemic risk due to the focus on the lower tail and to the allowance for nonlinear effects. We also introduce the Systemic Fragility Index (SFI) and the Systemic Hazard Index (SHI) as firm-specific measures, which allow us to identify systemically relevant firms during the financial crisis.

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.

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