Return transmission and asymmetric volatility spillovers between oil futures and oil equities: New DCC-MEGARCH analyses

Tsuji, Chikashi

doi:10.1016/j.econmod.2018.05.007

Cited by 35 publications

(14 citation statements)

References 61 publications

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“…(3) GARCH (Generalized Autoregressive Conditional Heteroskedasticity) family models and multi-GARCH family models are based on the variance of error, which can describe the risk spillover between markets, including VECH-GARCH, BEKK-GARCH and DCC-GARCH models. Because the volatility spillover level is indirectly analyzed by measuring variance, this method has the problem of multicollinearity [25][26][27][38][39][40]. 4The CoVaR (Conditional Value at Risk) model is based on conditional probability and describes conditional risk value.…”

Section: Literature Reviewmentioning

confidence: 99%

Financial Risk Contagion in Stock Markets: Causality and Measurement Aspects

Gao

2019

Sustainability

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As global financial markets become highly dependent on each other, risk contagion among stock markets is a primary feature of progressing globalization, which poses uncertainties for government agencies. The deficiency of previous studies is that it is difficult to accurately grasp the direction of risk diffusion in different time periods, and to depict the intensity of risk contagion constantly. Research on causality and measurement of financial risk contagion based on nonlinear causality tests and dynamic Copula methods will help governments to allocate financial resources reasonably and effectively, thus promoting the sustainable development of the social economy and financial markets. Taking the Chinese stock market as an example, this paper evaluated the risk contagion effect between the Chinese stock market and six other stock markets including developed and emerging markets from January 2006 to December 2018. From the aspect of causality, the nonlinear Granger causality test was applied to the entire time period and the phased time periods involving specific events like the subprime mortgage crisis and the Chinese stock market crash. From the aspect of measurement, the dynamic Markov state transition Copula model was used to describe the asymmetrically dependent structure of markets, from which was derived the time-varying lower tail dependence coefficients. The results have been summarized as follows. Firstly, after the outbreak of the subprime mortgage crisis, the stock markets in developed and emerging markets unilaterally affected the Chinese stock market, indicating that China was the recipient at this stage. Then, after the outbreak of the Chinese stock market crash, the Chinese stock market had a risk contagion effect on both Japanese and Russian stock markets, indicating that China became a source of financial risk contagion within a limited area at this stage. Lastly, in terms of the degree of risk contagion, the lower tail dependence coefficients of the Chinese stock market and other markets were significantly increased after the occurrence of specific risk events, while the risk contagion degree of developed markets was higher than that of emerging markets. Policymakers can recognize and apply the characteristics of risk contagion at different stages to refrain from unreasonable institutional arrangements, thus improving the sustainability of economic development.

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Section: Literature Reviewmentioning

confidence: 99%

Financial Risk Contagion in Stock Markets: Causality and Measurement Aspects

Gao

2019

Sustainability

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“…Further, using univariate GARCH models and oil returns, Ewing and Malik (2017) exhibited that incorporating structural breaks into GARCH models reduced the persistence in oil return volatility, and they concluded that when structural breaks are taken into account, both good and bad news more significantly affected oil return volatility. Moreover, by applying a new DCC-MEGARCH model, Tsuji (2018c) explored return and volatility transmission between international oil equities and WTI crude oil futures, in which structural break analyses were also conducted for robustness checks. Furthermore, Tsuji (2018aTsuji ( , 2018b also investigated the linkages between stock return structural breaks and their volatility persistence, although the examinations were by univariate analyses.…”

Section: Related Literature Reviewmentioning

confidence: 99%

Structural Breaks and Volatility Spillovers: The Case of the US and Canadian Stock Markets

Tsuji¹

2019

jmr

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This paper investigates the relations of structural breaks and volatility spillovers by using the US and Canadian stock return data. Specifically, applying spillover MGARCH models without and with structural break dummy variables to the two stock returns, this study derives the following interesting evidence. (1) First, we reveal that for both the US and Canadian stock returns, the volatility persistence parameter values in our spillover MGARCH models decline when structural break dummy variables are incorporated. (2) Second, we further clarify that when we do not take structural breaks into account, the spillover effect was unidirectional from Canada to the US. However, when we take structural breaks into consideration, the results from our spillover MGARCH model with structural break dummies demonstrate that the volatility spillover effects between the US and Canada become bidirectional. (3) Third, we furthermore reveal that around the Lehman Brothers bankruptcy in 2008, the time-varying volatilities derived from our spillover MGARCH model with structural break dummy variables show slightly higher values than those volatilities from our spillover MGARCH model with no structural break dummy variable.

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“…The return linkages between international equity markets have recently been the subject of investigation by both academic researchers and industry participants, and there are some extant studies on return transmission in financial markets (e.g., Arouri et al, 2011aArouri et al, , 2011bArouri et al, , 2012Sadorsky, 2012;Syriopoulos et al, 2015;Tsuji, 2018aTsuji, , 2018bTsuji, , 2019. However, regardless of its significance, existing studies focusing on return transmission in Asian stock markets are limited.…”

Section: Introductionmentioning

confidence: 99%

Exploring Return Transmission in Asian Stock Markets

Tsuji¹

2019

jmr

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This paper investigates the return transmission between four Asian stock markets in Japan, China, Korea, and Taiwan. Specifically, applying a vector autoregression (VAR) model, this study derives the following interesting findings and interpretations. First, our results reveal that (1) rapid cross-country and autoregressive return transmission between the four Asian stock markets recently decreased, and (2) recently, the effects from the Japanese stock market to the other three Asian stock markets became weaker. Furthermore, our results clarify that (3) the return transmission effect from the Chinese stock market to the other three Asian stock markets is generally weak, also meaning that the Chinese stock market evolves autonomously.

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Return transmission and asymmetric volatility spillovers between oil futures and oil equities: New DCC-MEGARCH analyses

Cited by 35 publications

References 61 publications

Financial Risk Contagion in Stock Markets: Causality and Measurement Aspects

Financial Risk Contagion in Stock Markets: Causality and Measurement Aspects

Structural Breaks and Volatility Spillovers: The Case of the US and Canadian Stock Markets

Exploring Return Transmission in Asian Stock Markets

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