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This paper is the first study to examine the financial contagion from the U.S., Japanese and Chinese markets to Asian markets during the Global Financial Crisis (GFC) and Covid-19 Pandemic Crisis. We employ the DCC-EGARCH methodology and daily data of stock returns from 2005 to 2021 to estimate the time-varying correlations and the volatilities of stock markets. Our results show that the correlation between the U.S. and Japanese markets with emerging Asian ones is quite high, implying the interdependence between these markets. Furthermore, we find significant contagion effects from the U.S. equity market to markets in both advanced and emerging economies during the GFC. Nonetheless, during the Covid-19 pandemic, only 3 out of 10 Asian emerging markets had experienced the contagion from the U.S. Our findings also suggest that contagion effects are not strongly related to the level of global integration and Asian markets seem to be more affected by the contagion from Japan and China.

We introduce the realized exponential GARCH model that can use multiple realized volatility measures for the modeling of a return series. The model specifies the dynamic properties of both returns and realized measures, and is characterized by a flexible modeling of the dependence between returns and volatility. We apply the model to 27 stocks and an exchange traded fund that tracks the S&P 500 index and find specifications with multiple realized measures that dominate those that rely on a single realized measure. The empirical analysis suggests some convenient simplifications and highlights the advantages of the new specification.

Cryptocurrencies have gained a lot of attraction across the globe. Most observers of the cryptocurrency market will agree that crypto volatility is in a different league altogether. There has been a growing need to understand the nature of volatility in cryptocurrency. This paper analyzes the performance of four mostly traded, different cryptocurrencies in terms of their risk and return. The relationship between the return and returns volatility among different currencies has been examined considering the daily closing prices from 1 January 2017 to 30 June 2022, using the family of the GARCH model. The study has explored the spillover and asymmetric effect of volatility by using the DCC GARCH model and EGARCH model, respectively. The causal behavior among different cryptocurrencies has also been examined using Granger causality. There has been a strong spillover effect among different cryptocurrencies, Bitcoin and Ether, which are the top two cryptocurrencies with the highest market capitalization which have exhibited an asymmetric impact in their volatility as compared to the other two currencies, which are Litecoin and XRP.

Purpose This study examines dynamic interdependence among stock indices in the Americas and the effects of global crises on these interdependencies that underline contagion patterns across developed and emerging markets. Design/methodology/approach A Dynamic Conditional Correlation (DCC-EGARCH) model is applied to measure time-varying correlations among stock indices from countries in the Americas. Network measures—Normalized Tree Length (NTL), Average Path Length (APL) and Mean Occupation Layer (MOL)—are used to track shifts in market connectivity. At the same time, the Bai and Perron test is used to identify structural changes linked to global events such as COVID-19. Findings Results show that crises increase market cohesion across the Americas, indicated by lower NTL and MOL values. This enhanced connectivity suggests that stock markets respond more synchronously during high-volatility periods, with the United States often central to the contagion network. Originality/value This study contributes to financial contagion research by applying dynamic network analysis to the Americas, offering tools to identify systemic risks. The findings provide insights for policymakers and investors into managing contagion risks in interconnected markets.

This study investigates the dynamic mechanism of financial markets on volatility spillovers across eight major cryptocurrency returns, namely Bitcoin, Ethereum, Stellar, Ripple, Tether, Cardano, Litecoin, and Eos from November 17, 2019, to January 25, 2021. The study captures the financial behavior of investors during the COVID-19 pandemic as a result of national lockdowns and slowdown of production. Three different methods, namely, EGARCH, DCC-GARCH, and wavelet, are used to understand whether cryptocurrency markets have been exposed to extreme volatility. While GARCH family models provide information about asset returns at given time scales, wavelets capture that information across different frequencies without losing inputs from the time horizon. The overall results show that three cryptocurrency markets (i.e., Bitcoin, Ethereum, and Litecoin) are highly volatile and mutually dependent over the sample period. This result means that any kind of shock in one market leads investors to act in the same direction in the other market and thus indirectly causes volatility spillovers in those markets. The results also imply that the volatility spillover across cryptocurrency markets was more influential in the second lockdown that started at the beginning of November 2020. Finally, to calculate the financial risk, two methods—namely, value-at-risk (VaR) and conditional value-at-risk (CVaR)—are used, along with two additional stock indices (the Shanghai Composite Index and S&P 500). Regardless of the confidence level investigated, the selected crypto assets, with the exception of the USDT were found to have substantially greater downside risk than SSE and S&P 500.

In today’s environmentally conscious world, carbon trading has emerged as a widely accepted economic instrument to mitigate the externalities resulting from deteriorating environmental problems. Consequently, the use of market-based mechanisms to address environmental issues has reached a global consensus. Many countries are implementing progressive steps by establishing carbon markets to promote low-carbon development and meet their carbon reduction targets. However, the inherent risks in carbon trading markets may hamper the formation of a reasonable carbon price signal, leading to inadequate stimulation of low-carbon technology investments and potential failure to achieve national emission reduction goals. Therefore, managing the risks associated with carbon trading markets is crucial. This study focuses on measuring the risk of China’s carbon market, with the primary aim of exploring carbon price fluctuation patterns and precisely measuring market risks. The risks associated with China’s carbon market are quantified and analyzed using the exponential generalized autoregressive conditional heteroskedasticity (EGARCH) model, extreme value theory (EVT), and the value at risk (VaR) method. Results show that (1) the effect of external shocks on each carbon market is asymmetrical, and positive shocks exert considerable leverage effects on carbon price fluctuations. (2) EVT can be used to effectively fit the risks in the carbon markets. The risks of each carbon market show different characteristics. The risk of Hubei and Guangdong carbon markets is relatively small, and the dynamic VaR is nearly ±0.2. (3) Compared with the performance of the Chinese carbon market, the performance of the European Union Emission Trading Scheme is more stable, and its dynamic VaR for most of the period is within ±0.1, which is considerably lower than the VaR of other Chinese carbon markets. This study also proposes suitable policy implications to ensure the healthy and sustainable development of China’s carbon market.

Focusing on the Russia–Ukraine war, this paper investigates natural gas futures volatilities. Applying several hybrid GARCH and EGARCH models, which innovatively incorporate both fat-tailed distribution errors and structural breaks, we derive the following new evidence. First, our hybrid modeling approach is effective in timely capturing the natural gas futures volatility spike when tensions simmered on the Russia–Ukraine border. Second, the hybrid modeling approach is effective for not only GARCH modeling but also EGARCH modeling. Third, the volatility estimates from our hybrid models have predictive power for the volatilities of nonhybrid models. Fourth, the volatility estimates from the nonhybrid models lag behind the volatilities of our hybrid models.

In this study, we utilize various GARCH-type models to estimate and forecast volatility on S&P 500 returns and compare the results between the two financial crises, the GFC of 2008 (Global Financial Crisis of 2008) and the COVID-19 financial crisis. These two financial crises are different from the forming reasons by whether mainly caused by the financial factors. This study also makes the evaluations on the performance of these GARCH-type models in estimating and forecasting volatility, which may provide the efficient models for reference for the research of the volatility of the future potential financial crisis. We find that as for the AIC/BIC assessments on the estimation of volatility, the GJR-GARCH model performs better during the GFC of 2008, while the EGARCH model has the better performance during the COVID-19 financial crisis. With respect to the QLIKE loss function evaluation on the forecasting ability of volatility, the GJR-GARCH model performs better during the GFC of 2008, while symmetric GARCH model has better volatility forecasting ability during the COVID-19 financial crisis.

This study investigated the spillover effects of geopolitical risks on energy (crude oil, coal and natural gas) markets. The empirical evidence is based on the CoVaR index and the CAViaR-EGARCH model. Results demonstrate that the spillover effects of geopolitical risks on the global energy market are nonlinear, asymmetric and time-varying. With each 1% rise in global geopolitical risks, the left tail risks in the crude oil, coal, and natural gas markets decreased by 0.179%, 0.119% and 0.113%, while the right tail risks increased by 0.144%, 0.135% and 0.097%, respectively. In addition, the magnitude of energy crises triggered by different geopolitical events varies. Lastly, the spillover effects of GPR on energy markets vary considerably across nations, with more substantial effects observed on average in BRICS than in G7 countries. The primary implication is to provide references for government and energy investors to avoid energy market risks timely.