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Using 5-min data of Chinese stock market index and eight Chinese commodity futures (soybean, wheat, corn, gold, silver, copper and aluminum, crude oil) from March 26, 2018 to October 22, 2020, we analyze the dynamic spillover connectedness of returns and realized moments, including realized volatility, realized skewness, and realized kurtosis, duri...
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... before presenting the 22 determinants of the TSI, we apply Jarque-Bera tests to check the normal distribution of 23 the data series. The results are shown in Table 3. As shown, the Jarque-Bera statistic tests indicate that all TSI series are not 5 normally distributed, which suggests that the use OLS regressions might be suboptimal. ...Similar publications
Unlike previous studies investigating the interdependence and co-movements between different commodity prices, this paper examined the co-explosivity versus leading effects between the Brent oil prices and agricultural commodity prices. Empirically, we first date-stamped bubbles in the Brent and a set of agricultural commodities. Second, we examine...
This study investigates the impacts of the Russia-Ukraine conflict on agricultural commodities price volatilities. The analysis is conducted considering the movements in crude oil prices and their consequences in the global and Brazilian agricultural commodities markets. We employ a bivariate DCC-GARCH model to examine the volatility spillover and...
Citations
... For example, [17] show evidence in support of global economic activity as a good predictor of energy market volatility. With respect to high moment spillover, based on daily realized data of Chinese stock market index and eight Chinese commodity futures, [18] derive similar conclusions with [6]. Moreover, through the combination of quantile VAR model and time-varying parameter vector autoregressive (TVP-VAR) model, [19] further investigate the realized high-moments spillover among crude oil, gold, economic policy uncertainty (EPU) and four Chinese financial sectors including bank, trust, insurance and security under different market conditions. ...
This study investigates the impact of oil market uncertainty on the volatility of Chinese sector indexes. We utilize commonly used realized volatility of WTI and Brent oil price along with the CBOE crude oil volatility index (OVX) to embody the oil market uncertainty. Based on the sample span from Mar 16, 2011 to Dec 31, 2019, this study utilizes vector autoregression (VAR) model to derive the impacts of the three different uncertainty indicators on Chinese stock volatilities. The empirical results show, for all sectors, the impact of OVX on sectors volatilities are more economically and statistically significant than that of realized volatility of both WTI and Brent oil prices, especially after the Chinese refined oil pricing reform of March 27, 2013. That implies OVX is more informative than traditional WTI and Brent oil prices with respect to volatility spillover from oil market to Chinese stock market. This study could provide some important implications for the participants in Chinese stock market.
... Studying the spillover effects between markets can further understand the characteristics of information transmission and risk contagion (Zhang et al. 2022). Different scholars use different methods to discuss the volatility spillovers between markets. ...
... The spillover index model can demonstrate the volatility transmission mechanism between markets. Many scholars used it to explore the volatility spillover relationship between the stock, energy, or commodity markets (Sheng et al. 2023;Walid et al. 2020;Zhang et al. 2022). Therefore, this article uses the two models for empirical analysis. ...
The interest rate derivatives market is an important force in promoting the development of the bond market and is an effective tool to manage interest rate risk. The research on price discovery and volatility spillover of the market can help provide valuable reference information for investors. Based on treasury bond futures and interest rate swaps, the paper aims to discuss the price discovery function and spillover structure of the interest rate derivatives market. The paper establishes the information share model and spillover index model for empirical analysis. The results show that: First, the calculation results of the information share model show that the price discovery of treasury bond futures and interest rate swap markets is stronger than that of the spot market. Second, based on structural break analysis, treasury bond futures and interest rate swaps do not have breakpoints, while the treasury bond spot has three breakpoints. The paper divides the entire sample into four stages based on structural breakpoints and finds that the price discovery ability of the interest rate derivative market dynamically changed. Third, as a net spillover in the market, treasury bond futures have developed relatively stable. Both treasury bond futures and interest rate swaps have spillover effects on the spot market, indicating that China’s interest rate derivatives market can impact the treasury bond spot market.
... Finally, the authors show that gold and oil have hedge and safe haven potential against downside stock and cryptocurrency prices. Using the same methodology, Zhang et al. (2023) examines the time-varying spillovers of realized moments between Shanghai Shenzhen stock (CSI300) market and commodity futures (Aluminum, copper, corn, gold, oil, silver, soybean, and wheat) markets in China. The results support evidence of an increase in spillovers between stock and commodity markets during US-China trade war and COVID-19 periods. ...
... Thus, the realized high-order moments contain additional information to the stock-commodity connections than cannot be detected when spillovers in return and volatility are considered. Therefore, our models assist market actors for an accurate forecasting and asset pricing (Zhang et al., 2023). The dynamic spillover network analyses of this paper employ the novel time-varying parameter vector autoregressions (TVP-VAR) model proposed by Antonakakis and Gabauer (2017). ...
... This result is consistent with the findings ofZhang, Jin, Bouri, Gao, and Xu (2023a, 2023b), who concluded that higher moment risk provides additional information that volatility cannot reveal.Content courtesy of Springer Nature, terms of use apply. Rights reserved.Realized higher moments and trading activity ...
This study investigates the informativeness of realized higher moments of stock index returns, namely, realized skewness and kurtosis, in explaining trading activity in the futures market to investigate whether information flows from price risk to trading activity. By analyzing high-frequency data covering a twelve-year period, we discover that futures trading activity can be attributed to high-moment market risks, as observed in the significant explanatory power of realized high moments even after controlling for other risk factors. The results are robust to the use of various adjusted measures of high-moment risk, their subcomponents, various measures of trading activity, and data attributes. This study suggests that realized high moments are a market risk and cannot be combined with volatility risk and other risk measures. Most importantly, this study finds that there exists a flow of market information from price risk to trading activity.
... Mensi et al. (2018) demonstrated that BRICS returns move in lockstep with oil prices in the long run, contrary to gold. Dai et al. (2022) and Zhang et al. (2022) indicated that major events such as the oil price crisis, COVID-19, and trade disputes between China and the US would cause/intensify the spillover effects between commodity (oil and gold) and the Chinese stock markets. Similarly, Mensi et al. (2021) reported time-varying volatility spillovers between developed and emerging BRICS stock markets and strategic commodity markets, amplified by major events. ...
... Jondeau and Rockinger (2006) asserted that higher moments are crucial for efficient asset pricing, optimal hedging, and optimal asset allocations. Indeed, many scholars have studied the risk spillovers of the higher-order moments across various financial markets (see Del-Brio et al., 2017;Bouri et al., 2021;Ahmed, 2022;Gomez-Gonzalez et al., 2022;Zhang et al., 2022a;Apergis, 2023;Bouri, 2023;Nekhili and Bouri, 2023 among others). ...
... To examine asymmetric or fat-tail risk linked to critical upside (downside) risk in both bullish and bearish market scenarios, measuring higher moments could be very helpful (Amaya et al., 2015;Zhang et al., 2022). Additionally, Yu et al. (2015) demonstrate that non-Gaussian behavior in the carbon and energy markets is gaining attention, indicating that return-volatility-based analysis may not be sufficient to comprehend the underlying risk spillover between the carbon and energy markets. ...
... Shi andZhou 2022). Zhang et al. (2022aZhang et al. ( , 2022b analyze volatility risk spillovers among commodity markets at different time frequencies and conclude that at high frequencies, bearish market conditions amplify volatility spillovers in commodity markets. The COVID-19 shock, and the global financial crisis had a more substantial impact on extreme volatility spillovers, according to Cao's analysis of extreme volatility spillovers in financial markets under several severe shocks (Cao 2022). ...
... The majority of studies focus on the spillover impacts of commodities, with few studies on the spillover effects of nickel (Martino and Parson 2013;Shahani and Taneja 2022;Dai et al. 2022;Mensi et al. 2022;Golitsis et al. 2022;Lin and Chen 2019), especially in important global marketplaces like the Shanghai Commodity Exchange (COMEX) in New York, the London Metal Exchange (LME), and the Shanghai Futures Exchange (SHFE). Zhang et al. (2022aZhang et al. ( , 2022b analyze high-frequency data on the Chinese stock market indices and eight commodity futures and conclude that there is a spillover between the stock and commodities markets during periods of extreme shocks. Tiwari et al. (2022) use a QVAR model to analyze the volatility spillover between energy and agricultural markets and observe the changes in market volatility spillover before and during the COVID-19 outbreak. ...
To investigate the variations in price volatility spillover effects on different time scales across the LME, SMM, and Chinese spot nickel markets. This study uses data from the London Metal Exchange (LME), Shanghai Nonferrous Metals (SMM) nickel daily closing prices, and Chinese nickel daily market prices from April 1, 2015, to September 30, 2022, to categorize the above three markets’ yield series into four-time scales: very short, short, medium, and long term. The multivariate BEKK-GARCH approach is then used to analyze the spillover effects at different time scales. The study’s findings reveal that (1) the LME, SMM, and spot nickel prices are all impacted by external shocks and inherent volatility at all time scales; (2) at the very short time scale, there is a two-way volatility spillover effect between the SMM nickel and the other two markets, in addition, a one-way volatility spillover effect between the LME and spot nickel price; (3) at the short time scale, the volatility spillover effect between the LME and spot nickel price disappears; (4) at the medium-term time scale, there is no volatility spillover effect between the LME, SMM, and spot nickel price; (5) at the long-term time scale, the SMM has a volatility spillover effect on spot nickel price.
... However, the current body of literature has overlooked the reaction of intraday semi-realized variances to various frequency wavelengths. The literature only tests whether daily volatility disturbances in one market can predict error variances in another market (Mensi et al., 2017;Zhang et al., 2022aZhang et al., , 2022b. For instance, a few studies use the Diebold and Yilmaz (2012) generalized VAR framework and examine whether innovations in financial assets, commodities or aggregate sectors contribute towards the error variance in forecasting the H step ahead daily first moment (Hung & Vo, 2021;Mensi et al., 2021b;Zhang & Hamori, 2021) or second moment returns (Elsayed et al., 2022;Husain et al., 2019;Iqbal et al., 2022) of other asset classes. ...
... The volatility relationships between different US sectors are important for both short-and long-term investors as it helps them draw crucial conclusions about the general mechanism of sectoral volatility (BenSaïda, 2019). Zhang et al., (2022aZhang et al., ( , 2022b) also find that the spillover of shocks between financial and non-financial assets intensifies during COVID-19 by employing a symmetrical TYP-VAR approach and when relying upon the higher order of moments as compared with return-based connectedness. Similarly, Liao et al. (2021) also demonstrate that the COVID-19 period has intensified the spillover of shocks between different financial assets and thereby increases the risks of market contagion. ...
... First good and bad volatility spillovers provide additional insight into the risk transmission between different sectors; Xu et al. (2019) demonstrate that bad volatility spillover enhances the asymmetrical spillover of volatility between financial and commodity market more so than good volatility. Second, during the COVID-19 time frame, Zhang et al., (2022aZhang et al., ( , 2022b also find that the spillover of shocks between financial and non-financial assets intensifies when relying upon the higher order of moments compared with return-based connectedness. Third, the fluctuation of equities market risk over time results in volatility clustering, rather than volatility remaining constant throughout a period (Zheng et al., 2014). ...
This article is the first one to examine the moderating role of bitcoin sentiment indices on the short term and long-term time–frequency-based good and bad network connectedness of all US sectors. In more detail, the paper quantifies the above relationship between the 11 US sectoral high frequency returns and then identifies the moderating impact of bitcoin investors’ fear and greed sentiment on good and bad network connectedness during pre-Covid-19 and Covid-19. For the said purpose, we decompose the returns into good and bad volatility, and rely on time and frequency dependent spillover measures and quantify a spillover symmetrical and asymmetrical measure for network connectedness for different investment horizons. Furthermore, we also quantify the NET good–bad volatility transmission and reception capability of all our sectors within the frequency dependent network. The extracted good and bad network connectedness indices are then regressed on multiple thresholds of bitcoin sentiment indices. Quantile regression results revealed that fear, extreme fear, greed and extreme greed moderate the short term and long term good and bad volatility spillovers within the network connectedness. Finally, we also utilize hedge ratios and optimal portfolio weight selection strategies to explain whether short positioning in the US sectoral returns can be used to hedge against bitcoin sentiment risk.
... Hence, it is considered that the forecast step has limited effect on the dynamic joint total connectedness and our results are robust. Similarly, Zhang et al. (2023) also confirm that the variation of forecast step does not significantly change the spillovers between commodity and stock markets. ...
This paper examines the return and volatility connectedness among green finance markets including the green bonds, clean energy and socially responsible stocks, especially focusing on the connectedness changes induced by the COVID-19 pandemic. Specially, by employing the TVP-VAR extended joint connectedness approach, five representative time series, i.e., S&P Green Bond index (GBI), S&P Global Clean Energy index (GCEI), S&P Global 1200 ESG index (ESGI), S&P Global 1200 Carbon Efficient index (CEI) and S&P Global 1200 Fossil Fuel Free index (FFFI), are used to measure the connectedness among the markets over the sample period spanning from 1 January 2014 to 31 December 2021. The empirical results show that GBI is the main net receiver of return spillovers, followed by ESGI, CEI and FFFI, whereas GCEI is the main net transmitter of return spillovers during the full sample period. The dynamic total connectedness of five indices' returns is strongly fluctuating and especially exhibits a significant spike after the outbreak of COVID-19 pandemic. Interestingly, the findings of volatility connectedness indicate that ESGI, CEI, and FFFI are net transmitters of volatility spillovers during the full, pre-COVID-19 and post-COVID-19 periods. In addition, the network connectedness analysis highlights the role of GCEI in facilitating the transmission of financial contagion during the full and pre-COVID-19 periods, while that during the post-COVID-19 period shows that GBI receives more spillovers from others, indicating that the connections of the five indices tend to be stronger during market downturn periods caused by extreme events. Our results provide implications for investors and policymakers.
