Is there Inter-Regional Systemic Risk Contagion? An Investigation of Inter-Regional Systemic Risk Spillover Effects using the ESS-indicator and Bank CDS Spreads

2012 ◽  
Author(s):  
Wolfgang Lahmann
Keyword(s):  
Systemic Risk ◽  
Spillover Effects ◽  
Risk Spillover Effects

scholarly journals On Spillover Effect of Systemic Risk of Listed Securities Companies in China Based on Extended CoVaR Model

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Ze-Jiong Zhou ◽  
Shao-Kang Zhang ◽  
Mei Zhang ◽  
Jia-Ming Zhu
Keyword(s):  
Systemic Risk ◽  
Spillover Effect ◽  
Market Value ◽  
Strong Relationship ◽  
Spillover Effects ◽  
Financial Industry ◽  
Short Period ◽  
Securities Industry ◽  
Countermeasures And Suggestions ◽  
Risk Spillover Effects

Based on the daily data from January 2, 2019, to September 30, 2020, this paper uses the extended CoVaR model to measure the spillover effect of systemic risk among top 10 securities companies by market value in China, All Share Brokerage Index, All Share Financials Index, All Share Insurance Index, and CSI Banks Index. The conclusions are as follows: (1) there are risk spillover effects among 10 securities companies, which are asymmetric and bidirectional and highly volatile in a short period of time; (2) the spillover effect of systematic risk of securities companies is not necessarily related to the market value of securities companies but has a strong relationship with the stock market; (3) there are risk spillover effects between the sample securities companies and the four major indexes, but there are significant differences in the size of the spillover effects; (4) the securities industry has a great risk spillover effect on the financial industry, but the risk spillover effect of other financial sectors on the securities industry is very small. Finally, we put forward countermeasures and suggestions.

Analysis of risk spillover effect of copper option in China

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Wuyi Ye ◽  
Yiqi Wang ◽  
Jinhai Zhao
Keyword(s):  
Value At Risk ◽  
Risk Model ◽  
Spillover Effect ◽  
Spillover Effects ◽  
Futures Market ◽  
Content Type ◽  
Conditional Autoregressive ◽  
Derivative Markets ◽  
The Impact ◽  
Risk Spillover Effects

Purpose The purpose of this paper is to compare the changes in the risk spillover effects between the copper spot and futures markets before and after the issuance of copper options, analyze the risk spillover effects between the three markets after the issuance of the options and can provide effective suggestions for regulators and investors who hedge risks. Design/methodology/approach The MV-CAViaR model is an extended form of the vector autoregressive model (VAR) to the quantile model, and it is also a special form of the MVMQ-CAViaR model. Based on the VAR quantile model, this model has undergone continuous promotion of the Conditional Autoregressive Value-at-Risk Model (CAViaR) and the Multi-quantile Conditional Autoregressive Value-at-Risk Model (MQ-CAViaR), and finally got the current form of the model. Findings The issuance of options has led to certain changes in the risk spillover effect between the copper spot and its derivative markets, and the risk aggregation effect in the futures market has always been significant. Therefore, when supervising the copper product market and investors using copper derivatives to avoid market risks, they need to pay attention to the impact of futures on the spot market, the impact of options on the futures market and the risk spillover effects of spot and futures on the options market. Practical implications The empirical results of this paper can be used to hedge market risk investment strategies, and the changes in market relationships also provide an effective basis for the supervision of the copper product market by the supervisory authority. Originality/value It is the first literature research to discuss the risk and the impact of spillover effects of copper options on China copper market and its derivative markets. The MV-CAViaR model can capture the mutual risk influence between markets by modeling multiple markets simultaneously.

Risk spillover effects from global crude oil market to China’s commodity sectors

Energy ◽  
2020 ◽  
Vol 202 ◽  
pp. 117208 ◽  
Author(s):  
Juan Meng ◽  
He Nie ◽  
Bin Mo ◽  
Yonghong Jiang
Keyword(s):  
Crude Oil ◽  
Spillover Effects ◽  
Oil Market ◽  
Risk Spillover Effects

scholarly journals BANK PANICS AND FIRE SALES, INSOLVENCY AND ILLIQUIDITY

2018 ◽  
Vol 21 (06) ◽  
pp. 1850040 ◽  
Author(s):  
T. R. HURD
Keyword(s):  
Systemic Risk ◽  
Banking System ◽  
Spillover Effects ◽  
Mathematical Representation ◽  
Regulatory Regime ◽  
Main Stream ◽  
Dual Channel ◽  
Modelling Assumptions ◽  
Stock Flow ◽  
External Economy

Banking system crises are complex events that in a short span of time can inflict extensive damage to banks themselves and to the external economy. The crisis literature has so far identified a number of distinct effects or channels that can propagate distress contagiously both directly within the banking network itself and indirectly, between the network and the external economy. These contagious effects, and the potential events that trigger these effects, can explain most aspects of past crises, and are thought to be likely to dominate future financial crises. Since the current international financial regulatory regime based on the Basel III Accord does a good job of ensuring that banks are resilient to such contagion effects taken one at a time, systemic risk theorists increasingly understand that future crises are likely to be dominated by the spillovers between distinct contagion channels. The present paper aims to provide a model for systemic risk that is comprehensive enough to include the important contagion channels identified in the literature. In such a model one can hope to understand the dangerous spillover effects that are expected to dominate future crises. To rein in the number and complexity of the modelling assumptions, two requirements are imposed, neither of which is yet well-known or established in the main stream of systemic risk research. The first, called stock-flow consistency, demands that the financial system follows a rigorous set of rules based on accounting principles. The second requirement, called asset-liability symmetry, implies that every proposed contagion channel has a dual channel obtained by interchanging assets and liabilities, and that these dual channel pairs have a symmetric mathematical representation.

scholarly journals Multiscale Systemic Risk and Its Spillover Effects in the Cryptocurrency Market

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-22
Author(s):  
Xu Zhang ◽  
Zhijing Ding
Keyword(s):  
Systemic Risk ◽  
Extreme Values ◽  
Wavelet Packet ◽  
Granger Causality Test ◽  
Spillover Effects ◽  
Wavelet Packet Decomposition ◽  
Medium Term ◽  
Causality Test ◽  
Long Run ◽  
Recession Period

Since the advent of Bitcoin, the cryptocurrency market has become an important financial market. However, due to the existence of the cryptocurrency bubble, investors face more difficulties in risk portfolios. We adopt wavelet packet decomposition, nonlinear Granger causality test, risk spillover network, and STVAR model; retain the mature research of multiscale systemic risk based on time and frequency; and thus extend systemic risk to different regimes. We found that when frequency is combined with regimes, the risk spillover center will undergo subversive changes in the long run. We also proposed that BTC will be more robust at extreme values (like longest and shortest periods), while cryptocurrencies with smaller market capitalization will be stronger in the medium term. At the same time, the recession period will also spur on it.

Risk spillover in financial markets based on support vector quantile regression

2020 ◽  
pp. 1-11
Author(s):  
Wangsong Xie
Keyword(s):  
Quantile Regression ◽  
Financial Markets ◽  
Financial Market ◽  
Financial Risk ◽  
Spillover Effects ◽  
Market Risk ◽  
Support Vector ◽  
Non Linear ◽  
Risk Spillover Effects ◽  
Financial Market Risk

In terms of financial market risk research, with the rapid popularization of non-linear perspectives and the improvement of theoretical reasoning, scholars have slowly broken through the cage of linear ideas and derived new and more practical methods from non-linear perspectives to make up for the shortcomings of traditional research. Based on the support vector classification regression algorithm, this research combines the typical facts and characteristics of financial markets, from the perspective of quantile regression and SVR intelligent technology in computer science, to explore the research method of financial market risk spillover effects from a nonlinear perspective. Moreover, this research integrates statistical research, machine learning and other related research methods, and applies them to the measurement of financial risk spillover effects. The empirical analysis shows that the method proposed in this paper has certain effects, and financial risk analysis can be performed based on the risk spillover effect measurement model constructed in this paper.

Sign in / Sign up

Export Citation Format

Share Document