欧债危机冲击我国股票与债券市场的风险传染研究？

欧债危机对金融市场产生了显著的冲击,引发了巨大的风险。本文通过构建二元GARCH-BEKK模型,实证检验了欧债危机背景下欧洲股票市场、我国股票市场、国债市场与企业债市场之间的波动溢出效应,揭示了欧债危机冲击我国股票市场、国债市场与企业债市场的风险传染路径。实证表明,欧债危机冲击我国股票市场与债券市场的风险传导路径为：欧债危机引发的风险通过欧洲股票市场传导到我国股票市场,然后传导到企业债市场,最后传导到国债市场。     

Spatial spillover effects and risk contagion around G20 stock markets based on volatility network

Employing the spatial econometric model as well as the complex network theory, this study investigates the spatial spillovers of volatility among G20 stock markets and explores the influential factors of financial risk. To achieve this objective, we use GARCH-BEKK model to construct the volatility network of G20 stock markets, and calculate the Bonacich centrality to capture the most active and influential nodes. Finally, we innovatively use the volatility network matrix as spatial weight matrix and establish spatial Durbin model to measure the direct and spatial spillover effects. We highlight several key observations: there are significant spatial spillover effects in global stock markets; volatility spillover network exists aggregation effects, hierarchical structure and dynamic evolution features; the risk contagion capability of traditional financial power countries falls, while that of “financial small countries” rises; stock market volatility, government debt and inflation are positively correlated with systemic risk, while current account and macroeconomic performance are negatively correlated; the indirect spillover effects of all explanatory variables on systemic risk are greater than the direct spillover effects.     

Contagion effects and risk transmission channels in the housing,stock, interest rate and currency markets: An Empirical Study in China and the U.S.

This paper aims to investigate the crisis linkage and transmission channels within the housing, stock, interest rate and the currency markets in the U.S. and China in the past decade since the 2008 Subprime Mortgage Crisis. Two hybrid models, namely the SWARCH-EVT-Copula and the Bivariate SWARCH-EVT models, are proposed and applied in order to take into account (A) the high/low volatility regimes, (B) the interdependence structure inherited from the joint tail behaviours, as well as, (C) the risk spillover dynamics among financial sectors during market turmoils. We empirically show that the housing and stock markets share the strongest linkage and play central roles in the spreading of shocks. With a highly integrated system, the American financial sectors are under greater exposure to risk contagion and systemic risk during crises than the Chinese markets. Nevertheless, the exchange rate risk of Renminbi remains at an intensive level since its “crawl-like arrangement” and leads to increasing co-movements in the stock and interest rate markets since 2014.     

Risk spillover analysis across worldwide ESG stock markets: New evidence from the frequency-domain

With the increasing global awareness of green environmental protection, the international environmental, social, and governance (ESG) stock markets are developing rapidly together with rising risk linkages across worldwide markets. Therefore, this study explores the risk spillover characteristics of international ESG stock markets in the time and frequency domains and constructs a risk linkage network to further explore the risk contagion mechanism. The results show that in most cases, the developed North American market is the core of outward risk spillover in international ESG stock markets. The entire system presents a small-world structure, and the internal regions display different risk spillover characteristics. Moreover, international ESG markets generally have strong time–frequency spillover and medium-frequency (a month to a year) spillover. In contrast, the high- (a day to a month) and low-frequency (more than one year) spillovers are located at relatively low levels, but they will rise significantly under sudden financial events. The empirical results expand the ESG stock market's theoretical framework and provide a reference for investors and market regulators to reduce the investment risk of ESG.     

Risk spillover and network connectedness analysis of China’s green bond and financial markets: Evidence from financial events of 2015–2020

This study investigated the dynamic return and volatility spillovers, together with the network connectedness analysis between China’s green bond and main financial markets. Based on a multidimensional DCC-GJRGARCH model and the spillover index method, we found significant two-way risk spillovers between the green bond market and traditional bond markets. Moreover, the green bond market was subject to one-way risk spillover from the stock and commodities markets. Meanwhile, risk spillovers between the green bond market, forex market, and monetary market were not significant. Finally, network connectedness analysis provided specific information about connectivity and strength during different subperiods corresponding to financial events. The analysis indicated that under the influence of emergencies, China’s financial market will enhance the risk-spillover level by transforming the same type of market’s internal spillover into cross-market spillover.     

Spillovers between sovereign CDS and exchange rate markets: The role of market fear

As important variables in financial market, sovereign credit default swaps (CDS) and exchange rate have correlations and spillovers. And the volatility spillovers between the two markets become further complicated with the effect of market fear caused by extreme events such as global pandemic. This paper attempts to explore the complex interactions within the “sovereign CDS-exchange rate” system by adopting the forecast error variance decomposition method. The results show that there is a relatively close linkage between the two markets and the total spillover index of the system is dynamic. For most of the past, the exchange rate has a higher spillover effect on the sovereign CDS than vice versa. Moreover, after the market fear variables are introduced, the “sovereign CDS-exchange rate” system and market fear variables present bidirectional spillovers. The results of the study have particular significance for maintaining the financial stability and preventing risk contagion between markets.     

The risk spillover between China’s economic policy uncertainty and commodity markets: Evidence from frequency spillover and quantile connectedness approaches

Based on the frequency spillover method extended by Baruník and Křehlík (2018), we explore the risk spillover relationship between China’s economic policy uncertainty (CNEPU) and commodity futures in different frequency domains with daily settlement price data of 14 commodity futures in China. The results show that the risk spillover relationship between CNEPU and the commodity market mainly occurs in the short term. Quantile connectedness results show that economic policy uncertainty, which mainly plays the role of risk transmitter, is more closely related to the commodity market during the market boom and recession. Soybeans, soybean meal, and corn have shown high investment value in the process of market recovery, which is exposed to less risk spillover from policy uncertainty. Finally, the economic crisis with different characteristics will have specific impacts on asymmetric risk spillovers based on certain impact mechanisms.     

Did Vietnam stock market avoid the “contagion risk” from China and the U.S.? The contagion effect test with dynamic correlation coefficients

This paper examines the Vietnamese stock market with an extension of the recent investigation of risk contagion effects. Daily data spanning October 9, 2006–June 19, 2009 are sourced for the empirical validation of the risk contagion between the stock markets in Vietnam, China, and the U.S. To facilitate the validation of contagion effects with market related coefficients, this paper constructs a bivariable EGARCH model of dynamic condition correlation coefficients. First, we examine whether there are contagion effects when there is a financial crisis in the Vietnamese stock market. Next, we verify whether the contagion risk triggered by the crisis can affect the Vietnamese market and examine which market influences the Vietnamese market the most. We find that compared to the U.S. stock market, the Chinese stock market brings more contagion risk to the Vietnamese market, and these effects gain more significance after the sub-prime mortgage crisis.     

The default contagion of contingent convertible bonds in financial network

This paper investigates the impact of contingent convertible (CoCo) bonds on systemic risk using Eisenberg-Noe’s financial network method, in which the network is linked by debt relationships. As an efficient method for addressing the problem of “too big to fail,” CoCo bonds have received widespread attention, particularly because the trigger for CoCo bonds is a systemic risk event. Thus, the impact of CoCo bonds on systemic risk needs to be addressed. To solve this problem, we adopt default contagion and loss amplification due to network linkage to measure systemic risk, from which we can ascertain the potential impact on it of CoCo bonds. The results show that CoCo bonds enhance the spillover effect of the issuer’s default; meanwhile, sufficient CoCo bonds partly offset the impact of default contagion from other banks. Furthermore, CoCo bonds enhance the amplification effect of loss due to network linkage, but the amplification effect diminishes after the bankruptcy cost is considered. Finally, the numerical test provides some insight into how the issuance of writedown (WD) bonds influences commercial banks in China. Our study not only offers suggestions to the regulators of CoCo bonds but also contributes to related studies.     

The impact of VIX on China’s financial market: A new perspective based on high-dimensional and time-varying methods

Based on the new perspective of high-dimensional and time-varying methods, this paper analyzes the contagion effects of US financial market volatility on China’s nine financial sub-markets. The results show evidence of non-linear Granger causality from the US financial volatility (VIX) to the China’s financial markets. Increased US financial volatility has a negative next-day impact on the stock, bond, fund, interest rate, foreign exchange, industrial product and agricultural product markets, and a positive next-day impact on the gold and real estate markets. US financial volatility has the greatest impact on industrial product market, following by stock, agricultural product, fund, real estate, bond, gold, foreign exchange, and interest rates. Major risk events such as the global financial crisis can cause an enhanced contagion effect of US financial volatility to China's financial markets. This paper supports the achievements of China's actions to prevent and resolve major financial risks in the period of the COVID-19 epidemic.     

Industry risk transmission channels and the spillover effects of specific determinants in China’s stock market: A spatial econometrics approach

This paper uses a spatial econometrics approach to study the industry risks in China’s stock market. We comprehensively consider the real linkage and information risk transmission channels and analyze the risk spillovers of specific determinants. Our empirical results show the following: 1) The real linkage channel and information channel are both effective transmission channels for driving spillover effects, and the information channel is of the utmost importance. 2) The spillover effects of specific determinants exist and are persistent. The superposition of spillover effects may lead to extreme risk. 3) The transmission channels and spillover effects are asymmetric in different regimes.     

Modelling contagion of financial crises

Inspired by the empirical findings, we include international traders to capture linkage between markets and propose a two-market heterogeneous agents model to simulate financial crisis with contagion effect. This paper manages to calibrate sudden crash behavior of US and UK stock markets during “Black Monday” of 1987 besides smooth crisis and disturbing crisis categorized in literature. It is implied that financial crisis and its contagion could be endogenous, which supports a scenario of over-valuation causing a financial crisis. In addition, the model shows that financial system could be fragile in which small shock(s) hitting individual market’s fundamental could cause financial crisis spreading to the other market. This also supports a scenario of external shock triggering a financial crisis. Lastly, to demonstrate the relevance of our model to financial markets, we manage to match typical stylized facts, especially cross-correlation which is exclusive to a multiple-market case.     

COVID-19 and asymmetric volatility spillovers across global stock markets

In this study, I improve the assessment of asymmetry in volatility spillovers, and define six asymmetric spillover indexes. Employing Diebold-Yilmaz spillover index, network analysis, and my developed asymmetric spillover index, this study investigates the time-varying volatility spillovers and asymmetry in spillovers across stock markets of the U.S., Japan, Germany, the U.K., France, Italy, Canada, China, India, and Brazil based on high-frequency data from June 1, 2009, to August 28, 2020. I find that the global markets are well connected, and volatility spillovers across global stock markets are time-varying, crisis-sensitive, and asymmetric. Developed markets are the main risk transmitters, and emerging markets are the main risk receivers. Downside risk dominates financial contagion effects, and a great deal of downside risk spilled over from stock markets of risk transmitters into the global markets. Moreover, during the coronavirus recession, the total degree of volatility spillover is staying at an extremely high level, and emerging markets are the main risk receivers in the 2020 stock markets crash.     

Predicting failure risk using financial ratios: Quantile hazard model approach

This study examines the role of financial ratios in predicting companies’ default risk using the quantile hazard model (QHM) approach and compares its results to the discrete hazard model (DHM). We adopt the LASSO method to select essential predictors among the variables mentioned in the literature. We show the preeminence of our proposed QHM through the fact that it presents a different degree of financial ratios’ effect over various quantile levels. While DHM only confirms the aftermaths of “stock return volatilities” and “total liabilities” and the positive effects of “stock price”, “stock excess return”, and “profitability” on businesses, under high quantile levels QHM is able to supplement “cash and short-term investment to total assets”, “market capitalization”, and “current liabilities ratio” into the list of factors that influence a default. More interestingly, “cash and short-term investment to total assets” and “market capitalization” switch signs in high quantile levels, showing their different influence on companies with different risk levels. We also discover evidence for the distinction of default probability among different industrial sectors. Lastly, our proposed QHM empirically demonstrates improved out-of-sample forecasting performance.     

Risk spillover from crude oil prices to GCC stock market returns: New evidence during the COVID-19 outbreak

In this study, we examine oil price extreme tail risk spillover to individual Gulf Cooperation Council (GCC) stock markets and quantify this spillover’s shift before and during the COVID-19 pandemic. A dynamic conditional correlation generalized autoregressive heteroscedastic (DCC- GARCH) model is employed to estimate three important measures of tail dependence risk: conditional value at risk (CoVaR), delta CoVaR (ΔCoVaR), and marginal expected shortfall (MES). Using daily data from January 2017 until May 2020, results point to significant systemic oil risk spillover in all GCC stock markets. In particular, the effect of oil price systemic risk on GCC stock market returns was significantly larger during COVID-19 than before the pandemic. Upon splitting COVID-19 into two phases based on severity, we identify Saudi Arabia as the only GCC market to have experienced significantly higher exposure to oil risk in Phase 1. Although all GCC stock markets received greater oil systemic risk spillover in Phase 2 of COVID-19, Saudi Arabia and the United Arab Emirates appeared more vulnerable to oil extreme risk than other countries. Our empirical findings reveal that investors should carefully consider the extreme oil risk effects on GCC stock markets when designing optimal portfolio strategies, minimizing portfolio risk, and adopting dynamic diversification process. Policymakers and regulators should also enact awareness, oversight, and action plans to minimize adverse oil risk effects.     

Detecting contagion in Asian exchange rate markets using asymmetric DCC-GARCH and R-vine copulas

This study uses asymmetric DCC-GARCH models and copula functions to study exchange rate contagion in a group of twelve Asia-Pacific countries. Using daily data between November 1991 and March 2017, we show that extreme market movements are mainly associated with the high degree of interdependence registered by countries in this region. Evidence of contagion is scarce. Asymmetries do not appear to be important. Specifically, currency co-movements are statistically identical during times of extreme market appreciation and depreciation, indicating that phenomena such as the fear of “appreciation” do not appear to be relevant in the region’s foreign exchange markets.     

Dynamic interaction of risk–return trade-offs between oil market and China’s stock market: An analysis from the risk preferences perspective

The risk–return trade-off refers to the compensation required by investors for bearing risks, which can be viewed as the risk preference of investors in a market. The current study investigates the dynamic interdependence of risk–return trade-offs between China’s stock market and the crude oil market from the perspective of risk preference of investors, which is designed to explore the transmission process of investors’ risk preference in both markets. Specifically, this study applies the time-varying parameter GARCH-M model, namely TVP-GARCH-M model, to characterize the time-dependent risk–return trade-offs (investors’ risk preferences) in the crude oil and China’s stock markets, then examines their relationship through Granger causality tests. Results show that a variation in risk preferences of the oil market investors can dramatically cause a variation in risk preferences of the Chinese stock market investors, while the risk preference of investors in the Chinese stock market does not lead to that in the crude oil market, which is in accordance with expectations. The dynamic effect of investors’ risk appetite in the crude oil market is further examined by the TVP-VAR model. The findings of this work suggest that there generally exists a positive impact of investors’ risk preference in the oil market and that the effect is time-varying to a greater degree during the short and medium term. Moreover, responses of the Chinese stock market investors’ risk preference were more significant during the 2008 financial crisis. Additionally, the empirical results remain robust when applying alternative crude oil prices and China’s stock prices.     

Risk spillover analysis of China’s financial sectors based on a new GARCH copula quantile regression model

This study employs a new GARCH copula quantile regression model to estimate the conditional value at risk for systemic risk spillover analysis. To be specific, thirteen copula quantile regression models are derived to capture the asymmetry and nonlinearity of the tail dependence between financial returns. Using Chinese stock market data over the period from January 2007 to October 2020, this paper investigates the risk spillovers from the banking, securities, and insurance sectors to the entire financial system. The empirical results indicate that (i) three financial sectors contribute significantly to the financial system, and the insurance sector displays the largest risk spillover effects on the financial system, followed by the banking sector and subsequently the securities sector; (ii) the time-varying risk spillovers are much larger during the global financial crisis than during the periods of the banking liquidity crisis, the stock market crash and the COVID-19 pandemic. Our results provide important implications for supervisory authorities and portfolio managers who want to maintain the stability of China’s financial system and optimize investment portfolios.     

Time-varying asymmetric volatility spillover between global markets and China’s A,B and H-shares using EGARCH and DCC-EGARCH models

This paper investigates the volatility spillover and dynamic conditional correlation between three types of China’s shares including A, B and H-shares with 12 major emerging and developed markets from 2002 to 2017 using EGARCH and multivariate DCC-EGARCH models. Both models found that Chinese equities are more related with their neighbouring countries such as Singapore, Japan, Australia and ASEAN-5 than with US, Germany and UK. The EGARCH model, with an auxiliary term added to capture the volatility spillover, found no volatility spillover between A-share markets and other advanced and emerging markets during the GFC and extended-crisis periods while this behaviour is not observed for B-share and H-share markets. However, the multivariate DCC model found strong evidence of contagion effect in both return correlations and volatility spillover for all China’s markets. In addition, both models found increased regional and global integration in A-share and B-share markets but not the H-share market. Finally, the results from both models provide clear evidence of distinct behaviours associated with return and volatility spillover in these three share types, suggesting foreign investors should consider the heterogeneity in volatility spillover and return correlations of these Chinese share types when forming investment strategies.     

Risk spillovers and hedge strategies between global crude oil markets and stock markets: Do regime switching processes combining long memory and asymmetry matter?

This paper investigates risk spillovers and hedge strategies between global crude oil markets and stock markets. In the paper, we propose a multivariate long memory and asymmetry GARCH framework that integrates state-dependent regime switching in the mean process with multivariate long memory and asymmetry GARCH in the variance process. Our results first show that there are linear risk spillovers running from the US stock markets to the WTI oil market in the short term. However, the linear risk spillover effect running from the oil market to the US stock market can only exist in the long term. In addition, there is a bidirectional linear risk spillover effect between the European stock markets and the Brent oil market in the short and long terms. Furthermore, there is no linear risk spillover effect between the Dubai oil market and the Chinese stock market. Second, the nonlinear risk spillovers running from the WTI oil market to the US stock market can be found in the tranquil regime. Moreover, there is also a nonlinear risk spillover effect running from the European stock markets to the Brent oil market in the tranquil regime. In addition, the nonlinear risk spillover effect running from the Brent oil markets to the European stock market can be found in the crisis regime. Furthermore, there is bidirectional nonlinear Granger causality between the Dubai crude oil market and the Chinese stock market in the tranquil regime. Finally, dynamic hedge effectiveness shows that the regime switching process combined with long memory and asymmetry behavior seems to be a plausible and feasible way to conduct hedge strategies between the global crude oil markets and stock markets.