Spillover effect and Granger causality investigation between China’s stock market and international oil market: A dynamic multiscale approach

Peng, Yufang; Chen, Weidong; Wei, Pengbang; Yu, Guanyi

doi:10.1016/j.cam.2019.112460

Cited by 41 publications

(18 citation statements)

References 66 publications

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“…Additionally, African stock markets have a strong relationship with crude oil prices in the short and medium term, but this nexus is relatively weak in the long run. Peng et al ( 2020 ) explore the relationship between China’s stock market and global oil prices. Živkov et al ( 2020 ) focus on the strength of the connectedness between oil prices and stock markets of Visegrad group countries at various time-horizons.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-scale Features of Interdependence Between Oil Prices and Stock Prices

Hung

2022

Asia-Pac Financ Markets

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This paper investigates the time-varying connectedness between oil prices and the stock prices in African markets. We employ a wavelet-based dynamic conditional correlation framework, which allows us to look into the time-varying correlation between oil and African stock markets in time and frequency domains. Empirical results show the interdependence between oil prices and African stock market prices are time-varying and spread across various wavelet scales. More importantly, the dynamic relationship between oil prices and stock returns in these countries varies more frequently and at a lower level in the short run. However, we find the long and medium-range co-movements between them except during the Covid-19 period when short-term integration increased considerably, which might help portfolio managers and investors mitigate risk. We identify the hedge ratios and optimal portfolio weights for practical implications based on the said assets' dynamic conditional correlation.

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Section: Literature Reviewmentioning

confidence: 99%

Multi-scale Features of Interdependence Between Oil Prices and Stock Prices

Hung

2022

Asia-Pac Financ Markets

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“…The relationship between the China stock market and the international crude oil market is the essential driver to measure the country's economic and financial development. Peng et al, (2020) evaluate the multiscale integration and volatility effects of the China stock markets and oil market indices. The study results show a strong and positive trend between crude oil prices and stock market returns.…”

Section: Oil Prices and Financial Developmentmentioning

confidence: 99%

The asymmetric effect of COVID-19 outbreak, commodities prices and policy uncertainty on financial development in China: evidence from QARDL approach

Jiang

Zhang

Razi

et al. 2021

Economic Research-Ekonomska Istraživanja

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COVID-19 epidemic has brought uncertainty to each sector of the global economy, including the financial sector. The widely spread disease has a drastic effect, which massively created health and financial crisis and virtually pushed the economy to the brink of recession. The study focused on the financial development of China during the COVID-19 pandemic and analyzed the long-term and short-term impact of COVID-19, oil prices, gold prices, and global economic policy uncertainty on the financial development of the country. The QARDL model, Wald test, and Granger causality tests are employed to assess the daily data of variables from January 1, 2020, to March 15, 2021. The study's empirical results revealed that an increase in the number of COVID-19 registered patients has an unprecedented negative effect on financial development, whereas the oil prices co-moved with the financial performance. On the other hand, gold prices and global economic policy uncertainty are negatively correlated with financial development. This study offers various policy recommendations to help the investors, government, and decision-makers to make better decisions for the improvement of the financial development of China.

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“…Scholars have proposed a variety of methods to characterize the co-movement of financial market. The correlation coefficient method [6] , [7] , Granger causality method [8] , Copula method [9] , [10] , spillover method [11] , [12] , [13] , [14] , [15] , [16] , [17] , transfer entropy [18] and Wavelet theory [19] are used for the research of co-movement. These methods describe and reflect the co-movement between markets from different perspectives.…”

Section: Introductionmentioning

confidence: 99%