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Energy and agricultural commodities and markets have been examined extensively, albeit separately, for a number of years. In the energy literature, the returns, volatility and volatility spillovers (namely, the delayed effect of a returns shock in one asset on the subsequent volatility or covolatility in another asset), among alternative energy commodities, such as oil, gasoline and ethanol across different markets, have been analysed using a variety of univariate and multivariate models, estimation techniques, data sets, and time frequencies. A similar comment applies to the separate theoretical and empirical analysis of a wide range of agricultural commodities and markets. Given the recent interest and emphasis in bio-fuels and green energy, especially bio-ethanol, which is derived from a range of agricultural products, it is not surprising that there is a topical and developing literature on the spillovers between energy and agricultural markets. Modelling and testing spillovers between the energy and agricultural markets has typically been based on estimating multivariate conditional volatility models, specifically the Baba, Engle, Kraft, and Kroner (BEKK) and dynamic conditional correlation (DCC) models. A serious technical deficiency is that the Quasi-Maximum Likelihood Estimates (QMLE) of a Full BEKK matrix, which is typically estimated in examining volatility spillover effects, has no asymptotic properties, except by assumption, so that no valid statistical test of volatility spillovers is possible. Some papers in the literature have used the DCC model to test for volatility spillovers. However, it is well known in the financial econometrics literature that the DCC model has no regularity conditions, and that the QMLE of the parameters of DCC has no asymptotic properties, so that there is no valid statistical testing of volatility spillovers. The purpose of the paper is to evaluate the theory and practice in testing for volatility spillovers between energy and agricultural markets using the multivariate Full BEKK and DCC models, and to make recommendations as to how such spillovers might be tested using valid statistical techniques. Three new definitions of volatility and covolatility spillovers are given, and the different models used in empirical applications are evaluated in terms of the new definitions and statistical criteria.

This study examines the volatility spillovers in four representative exchanges and for six liquid cryptocurrencies. Using the high-frequency trading data of exchanges, the heterogeneity of exchanges in terms of volatility spillover can be examined dynamically in the time and frequency domains. We find that Ripple is a net receiver on Coinbase but acts as a net contributor on other exchanges. Bitfinex and Binance have different net spillover effects on the six cryptocurrency markets. Finally, we identify the determinants of total connectedness in two types of volatility spillover, which can explain cryptocurrency or exchange interlinkage.

One of the most crucial variables in investment selections is volatility. Unexpected information causes an investor to trade unusually in the market, which influences market volatility. Furthermore, various market sectors are affected differently by this type of trading behaviour. This research investigates the impact of COVID-19 on stock market volatility in India using a generalised autoregressive conditional model. The research was conducted using daily closing prices of stock indices include Nifty 50 and Nifty 500, from September 8, 2019, to July 9, 2021. In this article, the TGARCH model (1,1) was utilized to evaluate the volatility of NSE listed shares. The stock market's volatility has been calculated using the NSE's closing price. To reduce the skewness in the stock price data distribution, the natural logarithm of each price data is employed in the estimations. During the pre-COVID and COVID periods, the conditional volatility of the daily return series showed signs of volatility variations. Furthermore, the study aimed to compare stock price returns in pre-COVID19 and post-COVID19 scenarios to global indexes such as the NASDAQ, Nikkei 225, and FTSE. The stock market in India suffered volatility throughout the epidemic, according to the findings. Consequently, the study recommends NSE stock exchange bond indices to explore the volatility spillover influence between foreign exchange and the stock market in India. In this work, the positive definite covariance matrix is given, therefore a multivariate GARCH with BEKK model is used to estimate the covariance correlation and identify the consequences that stock market downturns can create. SPSS and Eviews software are used to analyze the data. The Augmented Dickey-Fuller (ADF) and KPSS unit root tests have been used to determine whether a time series is stationary or nonstationary. Whereas it corrects for heteroscedasticity and autocorrelation consistency in ADF test statistics, the study employed the KPSS unit root test to estimate the right result. In addition, to investigate the impact of COVID19 on stock market volatility in terms of negative and positive shocks in financial decisions, the TGARCH model captures asymmetry. The finding that the variable has a negative and statistically significant coefficient suggests that the COVID-19 outbreak lowered stock market volatility in India. In terms of historical errors, the coefficients represent the persistence of volatility for each nation. NIFTY and NASDAQ have the largest and longest-term spillover effect. According to the findings, India is the least sensitive to external shocks.

This study clarifies how risks spread across economic sectors and indicates the sectors that are the most affected to help investors with asset allocation and to support them in risk management. Although the Japanese stock market is one of the relatively large global stock markets, no studies have explored volatility spillovers among its sectors. Using the forecast error variance decomposition of the vector autoregressive model, this study examines the volatility spillovers among sectors classified on the Tokyo Stock Exchange. Our findings show that the pattern of volatility spillovers across sectors in the Japanese stock market differs between a few years preceding the coronavirus disease 2019 (pre-COVID-19), from 2014 to 2019, and during the COVID-19 period, in 2020. Although the energy resources and bank sectors are risk receivers in the pre-COVID-19 period, these sectors are risk transmitters during the COVID-19 period. We also find that volatility spillovers in the Japanese stock market are mainly driven by negative realized semivariance. These results are useful for asset allocation and risk management.

Purpose The purpose of this paper is to investigate the volatility spillover from crude oil and gold to the BRICS stock markets, after removing the effect of co-movement of prices of crude oil and gold. Design/methodology/approach Three multivariate GARCH models (dynamic conditional correlation, constant conditional correlation, and Baba, Engle, Kraft and Kroner) are used to capture the dynamic relationship between the crude oil and gold returns. The innovations from gold and oil are orthogonalized, and the EGARCH model is employed for the spillover analysis. The influences of oil price shocks and gold price shocks are tested on the returns of each of the BRICS equity markets. Findings There is evidence of volatility spillover from both the crude oil and gold to the BRICS stock markets. A sub-sample analysis suggests that the volatility spillover from gold was not significant before the financial crisis of 2008, but became significant post-crisis. The volatility asymmetry, which was not significant before the crisis, also became significant after it. Originality/value This study examines the volatility spillover to the BRICS stock markets from crude oil and gold, after accounting for the co-movement in their prices. It can help equity investors to judge whether gold can provide incremental diversification benefit, if used in conjunction with crude oil. The study also provides insights into the changes caused by the 2008 financial crisis on this volatility spillover mechanism.

Recent research shows that the efforts to limit climate change should focus on reducing the emissions of carbon dioxide over other greenhouse gases or air pollutants. Many countries are paying substantial attention to carbon emissions to improve air quality and public health. The largest source of carbon emissions from human activities in some countries in Europe and elsewhere is from burning fossil fuels for electricity, heat, and transportation. The prices of fuel and carbon emissions can influence each other. Owing to the importance of carbon emissions and their connection to fossil fuels, and the possibility of [1] Granger (1980) causality in spot and futures prices, returns, and volatility of carbon emissions, crude oil and coal have recently become very important research topics. For the USA, daily spot and futures prices are available for crude oil and coal, but there are no daily futures prices for carbon emissions. For the European Union (EU), there are no daily spot prices for coal or carbon emissions, but there are daily futures prices for crude oil, coal and carbon emissions. For this reason, daily prices will be used to analyse Granger causality and volatility spillovers in spot and futures prices of carbon emissions, crude oil, and coal. As the estimators are based on quasi-maximum likelihood estimators (QMLE) under the incorrect assumption of a normal distribution, we modify the likelihood ratio (LR) test to a quasi-likelihood ratio test (QLR) to test the multivariate conditional volatility Diagonal BEKK model, which estimates and tests volatility spillovers, and has valid regularity conditions and asymptotic properties, against the alternative Full BEKK model, which also estimates volatility spillovers, but has valid regularity conditions and asymptotic properties only under the null hypothesis of zero off-diagonal elements. Dynamic hedging strategies by using optimal hedge ratios are suggested to analyse market fluctuations in the spot and futures returns and volatility of carbon emissions, crude oil, and coal prices.

We identify networks of volatility spillovers and examine time-varying spillover intensities with daily implied volatilities of U.S. Treasury bonds, global stock indices, and commodities. The U.S. stock market is the center of the international volatility spillover network, and its volatility spillover to other markets has intensified since 2008. Moreover, U.S. quantitative easing alone explains 40%–55% of intensifying spillover from the United States. The addition of interest rate and currency factors does not diminish the dominant role of quantitative easing. Our findings highlight the primary contribution of U.S. unconventional monetary policy to volatility spillovers and potential global systemic risk. This paper was accepted by Neng Wang, finance .

The main purpose of the paper is to analyze the conditional correlations, conditional covariances, and co-volatility spillovers between international crude oil and associated financial markets. The prices of oil and its interactions with financial markets make it possible to determine the associated prices of financial derivatives, such as carbon emission prices. The approach taken in the paper is different from others in the literature; the purpose is to examine the usefulness of modeling and testing volatility spillovers in the oil and financial markets. The paper investigates co-volatility spillovers (namely, the delayed effect of a returns shock in one physical or financial asset on the subsequent volatility or co-volatility in another physical or financial asset) between the oil and financial markets. The oil industry has four major regions, namely North Sea, the USA, Middle East, and South-East Asia. Associated with these regions are two major financial centers, namely the UK and the USA. For these reasons, the data to be used are the returns on alternative crude oil markets, returns on crude oil derivatives, specifically futures, and stock index returns in the UK and the USA. Given the importance of the Chinese financial and economic systems, the paper also analyzes Chinese financial markets, where the data are more recent. The USA and China are the world’s two largest economies and the UK is the world’s sixth largest economy (and second in the existing EU) behind the USA, China, Japan, Germany, and India. Moreover, the USA and the UK are associated with WTI and Brent oil, respectively. One of the purposes of the paper is to examine how China might be different from the USA and the UK, which seems to be borne out in the empirical analysis. Based on the conditional covariances to test the co-volatility spillovers, dynamic hedging strategies will be suggested to analyze market fluctuations in crude oil prices and associated financial markets.

Investment in Green energy is becoming a popular alternative asset class for investors, primarily due to its environment-friendly attributes. However, there is a dire need for subjective evaluation of this emerging asset class based on the risk-return dynamics to which investors are exposed. To respond to this call, in this study, we conduct this evaluation utilizing a unique and rich data set consisting of daily prices of exchange-traded funds (ETFs) established on different asset classes. We use Vector autoregression and Baba-Engle-Kraft-Kroner parameterization of multivariate GARCH models and assess the relative strength of return and volatility spillovers from the Green and Grey energy markets. Our results reveal the return shocks originated in the Green energy market and transmitted to other markets are more pronounced. It is also observed that the potential to earn high returns and the weak correlation of Green energy ETFs with the traditional asset classes are the crucial factors helpful in inviting attention and investment of investors after 2015. Although our results further suggest that the role of Grey energy is diminishing, as shown by the Impulse response functions and the coefficients of multivariate ARCH and GARCH. Nonetheless, for some asset classes, e.g., Bonds, the volatility spillovers that originated in the Grey energy market are still prominent and robust.

As a major global exchange, the Stock Exchange of Hong Kong (SEHK) only requires semi-annual reporting whereas other major exchanges including the ones in Chinese mainland require quarterly reporting. We argue against the traditional view that higher reporting frequency is necessarily more beneficial. The decision on reporting frequency depends on how the information is being processed by the recipient traders and the results are not obvious. Using a sample of Chinese companies duallisted in both China A share market and SEHK (AH shares) as the experimental group and mainland’s companies listed on SEHK (H shares) only as the control group, we apply the difference-in-difference (DID) method to investigate the impacts of reporting frequency on stock information quality. The results suggest that after China A share market require quarterly financial reporting for all listed companies in 2002, the information asymmetry of the H tranche of AH stocks increases. Different from prior studies, the results suggest a negative association between stock information quality and financial reporting frequency. We argue that the increased information asymmetry in the H tranche is caused by the noise spilled over from the A tranche. We conduct multivariable GARCH tests and find evidence supporting this conjecture.