Systemic risk of the Chinese stock market based on the mobility measures of the marginal expected shortfall

2021 ◽  
Author(s):  
Xiaohang Liu ◽  
Handong Li
Keyword(s):  
Stock Market ◽  
Systemic Risk ◽  
Expected Shortfall ◽  
Chinese Stock Market ◽  
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.

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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