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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.

In this study, we investigate the volatility spillover effects across uncertainty indices (Infectious Disease Equity Market Volatility Tracker (IDEMV) and Geopolitical Risk Index (GPR)), carbon emissions, crude oil, natural gas, and green assets (green bonds and green stock) under extreme market conditions based on the quantile connectedness approach. The empirical findings reveal that the total and directional connectedness across green assets and other variables in extreme market conditions is much higher than that in the median, and there is obvious asymmetry in the connectedness measured at the extreme lower and upper quantiles. Our findings suggest that the uncertainty caused by COVID-19 has a more significant impact on green assets than the uncertainty related to the Russia–Ukraine war under normal and extreme market conditions. Furthermore, we discover that the uncertainty indices are more important in predicting green asset volatility under extreme market conditions than they are in the normal market. Finally, we observe that the dynamic total spillover effects in the extreme quantiles are significantly higher than those in the median.

IntroductionAs China advances its dual carbon targets, the carbon market has become a key policy instrument. However, climate policy uncertainty (CPU) can disrupt expectations and amplify risks in carbon trading prices (CTP), creating challenges for market stability and policy effectiveness.MethodsTo address this issue, this study constructs a weekly China-specific CPU (CCPU) index using text analysis of domestic newspapers and employs the Quantile Vector Autoregression–Diebold-Yilmaz (QVAR-DY) framework to assess its spillover effects on returns and volatility across six regional carbon markets. The quantile Granger-causality test is also applied to further validate the direction and significance of spillovers under different market conditions.ResultsThe analysis shows that spillovers remain moderate under normal conditions but intensify considerably under extreme states, particularly at higher quantiles, as confirmed by the quantile Granger-causality tests. The most striking finding is that spillovers from CCPU to volatility are consistently stronger than to returns, indicating that systemic risk contagion is more pronounced through volatility channels.DiscussionBy integrating a quantile perspective with dynamic spillover analysis, this study reveals the asymmetric transmission of policy uncertainty in China’s carbon markets and provides new insights for risk monitoring and policy design in the low-carbon transition.

Based on the theoretical framework of Multivariate Stochastic Volatility (MSV), this paper combines the Dynamic Generalized Correlation (DGC) model with the t-distribution, establishes the DGC-t-MSV model, and employs the Markov Chain Monte Carlo (MCMC) algorithm based on the Bayesian principle for efficient estimation to investigate the price volatility spillover effects in China’s energy supply chains. The results of this study indicate the following: (1) The upstream crude oil spot price has a positive spillover effect on the midstream freight price. The downstream diesel market price, 92 gasoline market price, and 95 gasoline market price all exert positive volatility spillovers on the midstream crude oil freight price. (2) The volatility spillover effect between the upstream power coal price and the midstream coal freight price exhibits unidirectionality, and the volatility is transmitted from the power coal price to the coal freight price. (3) The upstream natural gas price and the midstream liquefied natural gas market price display asymmetric characteristics. Among them, the upstream natural gas price has a unidirectional and more pronounced positive volatility spillover effect on the midstream liquefied natural gas market price.

The international shipping market, as a vital pillar of global trade, is closely intertwined with the crude oil market. Geopolitical conflicts—through mechanisms such as supply disruptions, rising transportation costs, and heightened market uncertainty—intensify volatility in both markets and amplify their mutual spillover effects. Using data from November 1999 to August 2025 across three markets, this study applies the Diebold–Yilmaz (DY) spillover index and the DCC-GARCH model to analyze the dynamic linkages between the shipping and crude oil markets, with a particular focus on volatility spillovers among the three. The results show significant bidirectional volatility spillovers between international shipping and crude oil markets, with the strongest spillovers occurring within the shipping market itself, reflecting its high degree of internal interconnectedness. Geopolitical conflict risk acts mainly as a net receiver of volatility and, by triggering supply–demand imbalances, prompting behavioral adjustments, and generating lagged policy effects, further amplifies spillovers between shipping and oil markets. This study not only provides a new perspective for understanding the interdependence of global energy and shipping markets under geopolitical uncertainty, but also offers valuable decision-making implications for policymakers and market participants in managing risks.

This paper examines and confirms the varying volatility of the relationship between cryptocurrency and currency markets at different time periods, such as when the market encountered multiple risk events including the US-China trade war, COVID-19, and the Russian-Ukraine war. We employ the Diagonal BEKK model and find that the co-volatility spillover effects between the returns of cryptocurrencies and currencies, with the exception of Tether and the U.S. dollar index, evolved significantly. Furthermore, the co-volatility spillover effects between cryptocurrencies and EUR have the largest effects and fluctuations. Large-cap cryptocurrencies (Bitcoin and Ethereum) have greater co-volatility spillover effects between them and currencies. Regarding the ability of cryptocurrencies to act as safe-haven for currencies, we observe that Bitcoin, Ethereum, and Tether served as safe-havens during the US-China trade war, and Bitcoin was a safe-haven during COVID-19. During the 2022 Russian-Ukraine war, Bitcoin and Tether were safe-havens. Interestingly, our findings point out that Bitcoin provides a more consistent safe-haven function for currency markets. Overall, by including multiple global risk events and a comprehensive dataset, the results support our conjecture (and earlier studies) indicating that the capabilities of cryptocurrency are time-varying and related to market status and risk events with different natures.

This study concentrates on examining the volatility spillover effects between the exchange rate (IRR to USD) and the leading export-oriented industries (i.e., petrochemical, basic metals and minerals) in Tehran Stock Exchange before and after the COVID-19 pandemic. Using DCC- and asymmetric DCC-GARCH approaches, the data sample (from 15 December 2018 to 24 April 2021) has been partitioned into two sub-samples: before and after the official announcement of COVID-19 outbreak. The results demonstrate that from the pre- to post-COVID-19 periods, first, the average returns of all industries have sharply fallen; second, the volatility of all variables has been significantly augmented in different horizons; third, for all industries, not only has the fractal market hypothesis approved in both separated periods, but also analysing the values of the fractional difference parameter, together with the outcomes of GARCH models, supports in the higher-risk post-COVID-19 period, wherein the effects of exogenous shocks last longer than their impacts in the alternative lower-risk period. Furthermore, our investigations demonstrate that the asymmetric spillover (based on the ADCC-GARCH models) in both pre- and post-COVID-19 periods are confirmed in all three industries, except for minerals after the novel coronavirus.Ultimately, the results not only corroborate the increase in the volatility spillover effects right after the COVID-19 but also substantiate that the exchange rate contributes most of the spillover effects into the petrochemical and minerals industries, which have been almost twice as much as those of the basic metals.

Background This paper investigates the effects of price volatility spillover in the poultry industry’s input markets, including soybean meal, day-old chicks and corn, and the foreign exchange market as an independent market, on the wholesale egg market in Iran. The experimental investigation is based on dynamic conditional correlation (GARCH-DCC). It is one of the most powerful and accepted methods for studying market volatility, whose representation is based on conditional variance. On the other hand, eggs are one of the main food items in the food basket of Iranian households, playing an important role in ensuring part of the food security of the country. However, the price volatilities of its inputs, which make up more than 70% of egg production costs, cause the instability of its price and the confusion of the producers of this sector. This is although in the relevant literature, there is little research on the issue of volatility spillover effects on agricultural product markets, especially in the country. Results The findings show that any shock in the input market leads to volatility and instability in the market; on the other hand, these volatilities maintain their stability. In addition, there is a spillover of exchange market volatility into corn and soybean meal input markets. Conclusions In that context, this article emphasizes the knowledge of market relationships and their consequences, thereby suggesting appropriate policies to control and support the domestic poultry industry.

This paper investigates the volatility spillover effects from the crude oil market to domestic stock markets using high-frequency data. We propose an enhanced methodology, the MS-HAR-TVP model, which extends the standard HAR framework. Our model decomposes crude oil price impacts on domestic financial markets into trend and jump volatility spillover components via the TVP framework, while incorporating a Markov switching mechanism to capture regime changes in volatility dynamics. This paper selects the CSI coal index and the CSI new energy index as the representatives of the domestic energy stock market, uses the rolling window method and the MCS test method to evaluate the predictive performance of the model, and compares it with other commonly used models. The empirical results show that (1) the decomposed high-frequency volatility spillover has obvious volatility clustering and asymmetry and the trend and jump spillover have significant improvement in the predictive ability of future volatility; (2) the short-term trend of crude oil is opposite to the trend of the new energy index, but the same as the short-term trend of the coal index, indicating that the impact of crude oil prices on different energy stock markets is different; and (3) the MS-HAR-TVP model and MS-HAR-TVP-J/TCJ model combined with the crude oil volatility spillover have significantly higher in-sample and out-of-sample prediction accuracy than other models in high volatility periods, indicating that the model proposed in this paper can better characterize and predict the volatility characteristics of the domestic energy stock market.

Purpose This study aims to determine the mutual association between the volatility of macroeconomic indicators (MIs) and India’s tourism demand. Design/methodology/approach Bivariate generalized autoregressive conditional heteroscedasticity (GARCH) models are applied to estimate the volatility spillover effect (VSE) from one market to another. Compared to the other methods, bivariate GARCH has wide acceptance for estimating the VSE. The monthly MIs and tourism demand data (2012–2021) are gathered for empirical analysis. Findings The evidence of the growth-led tourism (GLT) demand is seen. In the short term, tourism-led growth (TLG) is indicated. However, this TLG does not sustain itself in the long run. There is significant evidence in favour of the VSE from the MIs to the tourism demand ensuring GLT in India. Practical implications The main implication of the current study is to ignore the short-term influence of tourism demand on the economy because it does not sustain itself in the long run. However, the long-term influence of macroeconomic indicators on tourism demand should be seen with caution. Hedging, if possible, may be considered to protect the tourism sector’s interests from adverse economic fallouts. Originality/value There is a lack of studies on the volatility (especially on the VSE) between MIs and tourism demand. Hence, this study fills the research gap and presents a novel and unique contribution to the extent of the knowledge body on the topic and significantly contributes. 设计/方法论/方法 双变量GARCH模型用于估计从一个市场到另一个市场的波动溢出效应(VSE)。与其他方法相比, 双变量GARCH在估计波动溢出效应时得到了广泛的接受。收集2012-2021年的月度管理信息系统和旅游需求数据进行实证分析。 目的 该研究旨在确定宏观经济指标(MIs)的波动与印度旅游需求之间的相互关系。 研究发现 GLT(增长主导的旅游需求)的证据显而易见。从短期来看, 旅游导向型增长(TLG)可行。然而, 这种旅游导向型增长并不能长期维持下去。有重要的证据支持印度管理信息系统到旅游导向型增长的旅游需求波动溢出效应。 实际意义 当前研究的主要启示是忽略了旅游需求对经济的短期影响, 因为从长远来看, 它无法自我维持。然而, 宏观经济指标对旅游需求的长期影响应谨慎看待。如有可能, 可考虑对冲, 以保护旅游业的利益不受不利的经济影响。 创意/价值 目前对管理信息需求与旅游需求之间的波动（尤其是波动溢出效应）的研究较少。因此, 本研究填补了这个研究空白, 并对该主题知识体系的内容呈现新颖而独特的促进作用, 有显著的贡献作用。 Diseño/metodología/enfoque Los modelos GARCH bivariantes se aplican para estimar el efecto indirecto de la volatilidad (VSE) de un mercado a otro. En comparación con otros métodos, el GARCH bivariante goza de gran aceptación para estimar el VSE. Para el análisis empírico se recopilan los MI mensuales y los datos de demanda turística (2012–2021). Objetivo El estudio se centra en medir la relación mutua entre la volatilidad de los indicadores macroeconómicos (MI) y la demanda turística de la India. Conclusiones Se observan indicios de GLT (demanda turística impulsada por el crecimiento). A corto plazo, se evidencia el TLG (crecimiento impulsado por el turismo). Sin embargo, este TLG no se mantiene a largo plazo. Existen pruebas significativas a favor del VSE de los MI a la demanda turística que garantizan el GLT en India. Implicaciones prácticas La principal implicación del presente estudio es desestimar la influencia a corto plazo de la demanda turística en la economía porque no se sostiene a largo plazo. Sin embargo, la influencia a largo plazo de los indicadores macroeconómicos en la demanda turística debe considerarse con cautela. Por ello, la cobertura de riesgos puede plantearse para proteger los intereses del sector turístico de las repercusiones económicas adversas. Originalidad/valor Existe una carencia de estudios sobre la volatilidad (especialmente en el VSE) entre los MI y la demanda turística. En consecuencia, este estudio realiza una aportación investigadora mediante una contribución novedosa y única en la ampliación del conocimiento sobre el tema de análisis.