Transmission of shocks between Chinese financial market and oil market

Abstract

Purpose The purpose of this paper is to empirically investigate the volatility spillover between the Chinese stock market, investor’s sentiment and oil market, specifically during the 2014‒2016 turmoil period. Design/methodology/approach This study used the daily and monthly China market price index, oil-price index and composite index of Chinese investor’s sentiment. The authors first use the DCC GARCH model in order to study the correlation between variables. Second, the authors use a continuous wavelet decomposition technique so as to capture both time- and frequency-varying features of co-movement variables. Finally, the authors examine the spillover effects by estimating the BEKK GARCH model. Findings The wavelet coherency results indicate a substantial co-movement between oil and Chinese stock markets in the periods of high volatility. BEKK GARCH model outcomes confirm this relation and report the noteworthy bidirectional transmission of volatility between oil market shocks and the Chinese investor’s sentiment, chiefly in the crisis period. These results support the behavioral theory of contagion and highlight that the Chinese investor’s sentiment is a channel through which shocks are transmitted between the oil and Chinese equity markets. Thus, these results are important for Chinese authorities that should monitor the investor’s sentiment to better control the interaction between financial and real markets. Originality/value This study makes three major contributions to the existing literature. First, it pays attention to the recent 2015 Chinese stock market bumble. Second, it has gone some way toward enhancing our understanding of the volatility spillover between the investor’s sentiment, investor’s sentiment variation, oil prices and stock market returns (variables of interest) during oil and stock market crises. Third, it uses the continuous wavelet decomposition technique since it reveals the linkage between variables of interest at different time horizons.

The dynamic network connectedness and hedging strategies across stock markets and commodities: COVID-19 pandemic effect

“…More interestingly, the Nikkei225 of Brent is the strongest pairwise transmitter market of volatility shocks, while the Brent of CAC40 is the weakest. Undoubtedly, this can affect the Chinese economy and its impact is (Zhang and Chen, 2014;Chen, 2015;Luo and Ji, 2018;Abdelhedi and Boujelb ene, 2019). Moreover, we showed that the SP500-Gold pairwise exhibits a strong bidirectional connectedness, but has a weak connectedness with DAX stock markets.…”

Oil price shocks and emerging stock markets revisited

“…Smyth and Narayan (2018) clearly emphasize upon the dearth of studies focusing on the developing countries that are witnessing an economical transitory phase, and their stock markets are at an embryonic stage, with only exception to China. In comparison to the other emerging countries, abundant literature is available that focusses upon the various aspects of the oil–stock relationship in China (Abdelhedi and Boujelbène-Abbes, 2020; Zhu et al , 2016). Withstanding China, limited number of studies can also be traced for other emerging countries, such as for Central and Eastern Europe (Asteriou and Bashmakova, 2013), India (Ghosh and Kanjilal, 2016), Russia (Bhar and Nikolova, 2010), African continent countries (Gupta and Modise, 2013), Brazil, Russia, India, China, and South Africa (BRICS) country group (Bagchi, 2017; Tiwari et al , 2019), Vietnam (Narayan and Narayan, 2010).…”

Gold-oil-exchange rate volatility, Bombay stock exchange and global financial contagion 2008: Application of NARDL model with dynamic multipliers for evidences beyond symmetry

“…In the existing literature, some of the researchers have utilized only gold prices (Arfaoui & Ben Rejeb, 2017;Shakil et al, 2018;Tuna, 2018) and others have utilized only oil prices (Lardic & Mignon, 2008;Sahu et al, 2014;Mollick & Nguyen, 2015;Wen et al, 2017;Abdel-Latif et al, 2018;Areli Bermudez Delgado et al, 2018;Pandey & Vipul, 2018;Kumar, 2019;A. Mouna, 2019;Narayan, 2019;Charfeddine & Barkat, 2020) in order to examine their linkages with stock market indexes.…”

“…In the existing literature, a number of research articles has explored symmetrical linkages between oil price volatility and stock indexes (Asad, 2011;Bagchi, 2017;Kisswani & Elian, 2017;Mongi, 2017;Roubaud & Arouri, 2018;Areli Bermudez Delgado et al, 2018;Husain et al, 2019;A. Mouna, 2019;Narayan, 2019;Nouira, Hadj Amor, & Rault, 2019) but limited efforts have been made to estimate oil price-stock price nexus asymmetrically (Kocaarslan & Soytas, 2019;Kumar, 2019;Charfeddine & Barkat, 2020;Chkir et al, 2020).…”