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.

Asian frontier markets present compelling investment opportunities for investors seeking higher returns and low correlation with traditional assets. As such, it is important for financial market participants to understand the volatility transmission mechanism across these markets in order to make better portfolio allocation decisions. This study investigates the magnitude of return and volatility spillovers from the international crude oil markets on the Asian frontier oil and gas stock markets. In particular, we construct mean return and volatility spillover models to discuss whether regional (DSE, CSE, HNX, HOSE) and global (ICE) market impacts are crucial for the determination of oil & gas stock returns in Bangladesh, Sri Lanka, and Vietnam by employing ARMA(1,1)-GARCH(1,1) model. Using daily returns from January 4, 2010 to December 31, 2019, the findings of this paper show that the Brent oil and WTI crude oil markets influence the Sri Lanka and Vietnamese oil and gas stock markets. WTI price changes, however, have a relatively minor impact on Sri Lanka companies. For Bangladesh, it is noticeable that none of the spillover effects is statically significant. The results are explained by different levels of the reform process in the energy sector as well as by the importance of oil in these markets. In general, these frontier markets, especially the Bangladesh and Sri Lanka may offer promising diversification benefits due to low correlations with developed equity markets. These results are important for economic policymakers and investors in understanding the magnitude of volatility spillover effects of the international crude oil on these markets. Investors can use this information to make better portfolio allocation decisions to reduce risks and enhance returns.

In this paper, various Value-at-Risk techniques are applied to stock indices of 9 Asian emerging financial markets. The results from our selected models are then backtested by Unconditional Coverage, Independence, Joint Tests of Unconditional Coverage and Independence and Basel tests to ensure the quality of Value-at-Risk (VaR) estimates. The main conclusions are: (1) Timevarying volatility is the most important characteristic of stock returns when modelling VaR; (2) Financial data is not normally distributed, indicating that the normality assumption of VaR is not relevant; (3) Among VAR forecasting approaches, the backtesting based on in- and out-of-sample evaluations confirms its superiority in the class of GARCH models; Historical Simulation (HS), Filtered Historical Simulation (FHS), RiskMetrics and Monte Carlo were rejected because of its underestimation (for HS and RiskMetrics) or overestimation (for the FHS and Monte Carlo); (4) Models under student’s t and skew student’s t distribution are better in taking into account financial data’s characters; and (5) Forecasting VaR for futures index is harder than for stock index. Moreover, results show that there is no evidence to recommend the use of GARCH (1,1) to estimate VaR for all markets. In practice, the HS and RiskMetrics are popularly used by banks for large portfolios, despite of its serious underestimations of actual losses. These findings would be helpful for financial managers, investors and regulators dealing with stock markets in Asian emerging economies.

The subprime mortgage crisis in the United States (U.S.) in mid-2008 suggests that stock prices volatility do spillover from one market to another after international stock markets downturn. The purpose of this paper is to examine the magnitude of return and volatility spillovers from developed markets (the U.S. and Japan) to eight emerging equity markets (India, China, Indonesia, Korea, Malaysia, the Philippines, Taiwan, Thailand) and Vietnam. Employing a mean and volatility spillover model that deals with the U.S. and Japan shocks and day effects as exogenous variables in ARMA(1,1), GARCH(1,1) for Asian emerging markets, the study finds some interesting findings. Firstly, the day effect is present on six out of nine studied markets, except for the Indian, Taiwanese and Philippine. Secondly, the results of return spillover confirm significant spillover effects across the markets with different magnitudes. Specifically, the U.S. exerts a stronger influence on the Malaysian, Philippine and Vietnamese market compared with Japan. In contrast, Japan has a higher spillover effect on the Chinese, Indian, Korea, and Thailand than the U.S. For the Indonesian market, the return effect is equal. Finally, there is no evidence of a volatility effect of the U.S. and Japanese markets on the Asian emerging markets in this study.

This paper investigates financial contagion between the US and emerging European markets during recent turbulent periods, including the COVID-19 pandemic, the Russia-Ukraine war, the US presidential election, and changes in US tariff policy in 2025. The S&P 500 and STOXX Europe 50 indices are employed to capture global and regional factors affecting emerging markets in Europe. Using daily returns from January 2013 to April 2025, I conduct a structural break test and endogenously determine the start date of the COVID-19 pandemic. This approach avoids the arbitrary selection of crisis dates, allowing event timings and their market impact to be determined empirically. The paper then estimates returns models for individual emerging markets using the Bayesian approach. The results indicate that emerging countries’ stock markets are more affected by regional factors than the US market. Moreover, there is a significant increase in the influence of the STOXX Europe 50 on emerging markets during periods of turmoil. Finally, the paper evaluates whether there is contagion across markets based on Bayesian estimation in the dynamic conditional correlation DCC model. To account for the heavy tails associated with financial returns, this paper assumes that the corresponding error terms follow a skewed Student’s t-distributions. The findings also confirm stronger evidence of contagion from regional factors to the examined emerging markets during the episodes of market turmoil. Additionally, the results indicate interdependence, but no contagion, from the US market to emerging European markets.