Study on VECM-DCC-VARMA-GARCH Method Based on United Test of Dynamic Correlation and Spillover Effect--Analysis on the Linkage of CSI 300 Index Futures and Spot Stock

2021 ◽  
pp. 097215092110262
Author(s):  
Nevi Danila ◽  
Noor Azlinna Azizan ◽  
Eddy Suprihadi ◽  
Bunyamin Bunyamin

Sukuk and conventional bonds gain their popularity in the global market. Hence, an observation of the dynamic correlation and transmission of volatility between these two instruments is relevant. This article investigates the volatility spillover of sukuk and conventional bond markets by using GARCH-BEKK model. Then, we measure the dynamics of the co-movement of both markets by using GARCH-DCC model, and finally, we examine the macroeconomic factors that determine the dynamic conditional correlation between sukuk and conventional bonds in two Association of Southeast Asian Nations (ASEAN) markets (i.e., Indonesia and Malaysia) and four Gulf Cooperation Council (GCC) markets (i.e., Kingdom of Saudi Arabia, UAE, Qatar and Oman). The data reveal unidirectional and bidirectional volatility spillovers of sukuk and bond indices. The results also show strong evidence of dynamic conditional correlation for all markets. On the basis of the BEKK and dynamic conditional correlation (DCC) results, we infer that bonds and sukuk in ASEAN and GCC markets show the efficiency of the markets, which do not offer any diversification benefits to investors for having both instruments in their portfolios. As regards portfolio diversification strategies, investors must pay attention to the co-movements and spillover of both markets accordingly. Finally, only Oman market is influenced by all macroeconomic variables.


2013 ◽  
Vol 838-841 ◽  
pp. 2596-2605
Author(s):  
Ke Dong

Through an analysis about the atmospheric environment SO2space effect of China from 2000 to 2011, this article puts forward that the atmospheric pollution in China is influenced by per capital GDP and industrial structure to a large extent. Meanwhile, environmental efficiency factors which should restrict atmospheric environmental pollution have positive spillover effect to environmental pollution to surrounding area, which indicates that the environmental pollution treatment in China in current stage is only transition of the pollution in a disguised form. In some regions, industries are moved to less developed areas around in order to enhance their environmental compliance, so polluting industries cannot be moved out like those in developed regions and the environment is difficult to improve.


Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Jing Zhang ◽  
Qi-zhi He

This paper examines the spillover effect between bitcoin, gold, crude oil, and major stock markets by using the MSV model with dynamic correlation and Granger causality. The empirical results of the DC-GC-MSV model are logically correct and convergent. The DIC test result has proved that the DC-GC-MSV model is better and more accurate. Bitcoin has no significant Granger causality spillover effect than other assets. As a safe haven product for stock assets, gold price has one-way spillover effect from stock market volatility. Moreover, crude oil has the highest correlation with the stock market. In the recent COVID-19 epidemic and the sluggish economic environment, investors need to consider a balanced asset allocation among low-correlation assets, medium-correlation assets, and high-correlation assets to reduce risks.


2019 ◽  
Vol 16 (4) ◽  
pp. 146-155
Author(s):  
Chia-Ju Lee ◽  
Tuan-Nam Lai ◽  
Chang-Chou Chiang ◽  
Hai-Chin Yu

This study investigates the volatility and co-movement of gold prices across Tokyo, London, and New York gold markets. Using a dynamic conditional correlation (DCC) model, the authors estimate the cross-correlation and volatility of gold in each pair among three markets over the period from 1993 to 2012. Both the time-varying correlations and realized distributions are explored. After estimating the DCC as well as the corresponding distributions of the DCC among the three markets, the results suggest that: (i) the DCC probability distribution of London and New York shows a higher volatility associated with a higher DCC value; (ii) the DCC probability distribution between London and New York as well as between Tokyo and London both express the similar and overlapping pattern, implying that these markets are almost equal, and neither dominates; and (iii) New York exhibits a spillover effect of Tokyo’s variance, while the latter does not influence New York’s variance. The shapes of the distributions show that the distribution of high DCC is wider than that of low DCC, meaning that risk increases with the dynamic correlation. The implications of these gold DCC probability distributions encourage investors to diversify their global portfolios and manage latent risks in different gold markets effectively. Besides, the volatility-threshold DCC model suggests that the correlations are more sensitive to extreme volatility thresholds in London and New York markets, whereas the correlation is significantly affected by all levels of volatility at 50%, 75%, 90%, and 95% thresholds in Tokyo and London markets. Investors may not be able to diversify portfolio risk by choosing London and New York at the same time once gold becomes volatile as a high correlation is observed in the extreme thresholds.


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