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The purpose of this research is to investigate the short-term capital markets' reactions to the public announcement first local detection of novel corona virus (COVID 19) cases in 12 major Asian capital markets.

Using the constant mean return model and the market model, an event study methodology has been implied to determine the cumulative abnormal returns (CARs) of 10 pre and post-event trading days. The statistical significance of the data was assessed using both parametric and nonparametric test statistics.

First discovery of local COVID 19 cases had a substantial impact on all 12 Asian markets on the event day, as shown by statistically significant negative average abnormal return (AAR) and cumulative average abnormal return (CAAR). The single factor ANOVA result has also demonstrated that there is no variability among 12 regional markets in terms of short-term market responses. Furthermore, there is little evidence that these major Asian stock market indices differ significantly from the FTSE All-World Index which might suggest possible spillover impact and co-integration among the major Asian capital markets. The study further discovers that market capitalization and liquidity did not have any significant impact on market reaction to announcement.

The study's contribution might have been compromised by the absence of socio-demographic, technical, financial and other significant policy factors from the analysis.

These findings will be considerably helpful in tackling this unprecedented epidemic issue for personal and institutional investors, industrial and economic experts, government and policymakers in assessing the market in special circumstances, diversifying risk and developing financial and monetary policy proposals.

This paper is the first to examine the effects of local COVID 19 detection announcement on major Asian capital markets. This study will add to the literature by investigating unusual market returns generated by infectious illness outbreaks and the overall market efficiency and investors' behavioral pattern of major Asian capital markets.

The study aims is to explore the cointegration level among major Asian stock indices from pre- COVID-19 to post COVID-19 times.

Johansen cointegration test is employed to know the long run relationship among the stock market indices of Hong Kong, Indonesia, Malaysia, Korea, India, Japan, China, Taiwan, Israel and South Korea. The empirical testing was done to analyze whether any significant change has been induced by the COVID-19 pandemic on the cointegrating relationship of the selected markets or not. Through statistics of trace test and maximum eigen value, total number of cointegrating equations present among all the indices during different study periods were analyzed.

The presence of cointegration was found during all the sample periods and the findings suggests that the selected stock markets are associated with each other in general. During COVID-19 crisis period the cointegration level was reduced and again it regained its original level in the next year and again reduced in the subsequent next year. So, the cointegrating relationship among selected stock market indices remains dynamic and no evidence of impact of COVID-19 on this dynamism was found.

The study has explored the level of cointegration among the major stock indices of Asian nations in the pre, during, post-crisis and the most recent periods. The interconnectedness of the stock markets during the COVID-19 times has been compared with similar periods in different years immediately preceding and succeeding the COVID-19 times which has not been done in any of the existing study.

This paper aims to study the daily returns and volatility spillover effects in common stock prices between China and four countries in Southeast Asia (Vietnam, Thailand, Singapore and Malaysia).

The analysis uses a vector autoregression with a bivariate GARCH-BEKK model to capture return linkage and volatility transmission spanning the period including the pre- and post-2008 Global Financial Crisis.

The main empirical result is that the volatility of the Chinese market has had a significant impact on the other markets in the data sample. For the stock return, linkage between China and other markets seems to be remarkable during and after the Global Financial Crisis. Notably, the findings also indicate that the stock markets are more substantially integrated into the crisis.

The results have considerable implications for portfolio managers and institutional investors in the evaluation of investment and asset allocation decisions. The market participants should pay more attention to assess the worth of across linkages among the markets and their volatility transmissions. Additionally, international portfolio managers and hedgers may be better able to understand how the volatility linkage between stock markets interrelated overtime; this situation might provide them benefit in forecasting the behavior of this market by capturing the other market information.

This paper would complement the emerging body of existing literature by examining how China stock market impacts on their neighboring countries including Vietnam, Thailand, Singapore and Malaysia. Furthermore, this is the first investigation capturing return linkage and volatility spill over between China market and the four Southeast Asian markets by using bivariate VAR-GARCH-BEKK model. The authors believe that the results of this research’s empirical analysis would amplify the systematic understanding of spillover activities between China stock market and other stock markets.

The study uses the multivariate GARCH-BEKK model (which was first proposed by Baba et al. (1990) and then further developed by Engle and Kroner (1995)) to examine the return and volatility spillover between India and four leading Asian (namely, China, Japan, Singapore and Hong Kong) and two global (namely, the United Kingdom and the United States) equity markets.

The study employs a multivariate GARCH-BEKK model to quantify return correlation and volatility transmission across the pre- and post-2008 global financial crisis periods (apart from other conventional time series modelling like cointegration, Granger causality using vector error correction model (VECM)).

The results show a tendency of the Indian stock market index to move along with the US and Hong Kong market indices. The decrease in the value of the co-integration coefficient during the recession was explained by reduced investor confidence in developing countries. The result further shows a clear distinction in terms of volatility spillover between the Asian market vis-a-vis US and UK markets. Volatility transmission from India to Asian markets was found to be significantly higher as compared to the US and UK. So also, the study’s results show a puzzling result giving us comparable co-integration ranks for phase 2 (expansion) and phase 3 (slow-down) of the business cycle in most cases.

In Granger causality testing, the results were unable to ascertain the difference between phase 2 (expansion) and phase 3 (slowdown). However, the multivariate GARCH (MGARCH)-BEKK model showed a clear reduction in volatility transmission to NIFTY50 (is the flagship index on the National Stock Exchange of India Ltd. (NSE)) as India entered slow-down. This shows that the Indian economy does go through different business cycles, and the changes in parameters hence prove hypothesis 3 to be true with respect to volatility transmission to India from International markets.

The results show that for all countries, the volatility transmitted to India increases significantly going from phase 1 (recession) to phase 2 (expansion) and reduces again once the countries enter slow-down in phase 3 (slowdown). This shows that during expansion shocks and impulses in international markets affect the Indian markets significantly, supporting the increase in co-integration in phase 2 (expansion). During expansion, developing markets like India become profitable for investors, due to the high growth rate when compared to developed countries. This implies that a significant amount of capital enters Indian markets, which is susceptible to the volatility of international markets. The volatility transmission from India to the US and UK was insignificant in phase 1 (recession and recovery) and phase 3 (slow-down) showing a weak linkage between the markets during volatile time periods.

The purpose of this paper is to address the opposing views of the relationship between directors’ and officers’ liability insurance (D&O insurance) and stock price crash risk in a major Asian emerging stock market.

This paper finds an endogenous relationship between D&O insurance and stock price crash risk. Hence, the two-stage least squares regression analysis is used to address the endogeneity issue when the relationship is examined. Moreover, this paper further controls the quality of other corporate governance mechanisms to investigate whether D&O insurance still has an effect on stock price crash risk.

The effect of D&O insurance coverage is significantly negatively related to firm-specific stock price crash risk in Taiwan. More importantly, even when the quality of other corporate governance mechanisms is controlled, the negative relationship between D&O insurance coverage and firm-specific stock price crash risk remains significant. The evidence supports that D&O insurance serves as an effective external monitoring mechanism, strengthens corporate governance, and thus reduces stock price crash risk.

Emerging Asian markets suffer a dearth of research on the relationship of D&O insurance coverage and the firm-specific stock price crash risk. Investigating the relationship in Taiwan, the present study fills the research void. The findings show that D&O insurance plays an important role in reducing stock price crash risk of Taiwanese firms even when other corporate governance mechanisms are in place.

The volatility of bitcoin (BTC) and time horizon is the center point for investment decisions. However, attention is not often drawn to the relationship between BTC and equity indices. Thus, the purpose of this paper is to investigate the volatility and time frequency domain of BTC with stock markets.

This paper investigates the impact of structural breaks on volatility spillovers between Asian stock markets (China, Hong Kong, India, Indonesia, Japan, Korea, Singapore, and Taiwan) and the oil futures market. To this end, we apply the bivariate DCC-GARCH model to weekly spot indices during the period 1998-2015. The results reveal significant volatility transmission for the pairs between the Asian stock and oil futures markets. Moreover, we find a significant variability in the time-varying conditional correlations between the considered markets during both bullish and bearish markets, particularly from early 2007 to the summer of 2008. Using the modified ICSS algorithm, we find several sudden changes in these markets with a common break date centred on September 15, 2008. This date corresponds to the collapse of Lehman Brothers which is considered as our breakpoint to define the global financial crisis. Also, we analyse the optimal portfolio weights and time-varying hedge ratios based on the estimates of the multivariate DCC-GARCH model. The results emphasize the importance of overweighting optimal portfolios between Asian stock and the oil futures markets.

This paper examines the role of illiquidity and duration factor in understanding the momentum profit in the Korean stock market. We find that the foreigner/institutional illiquidity factor explains the momentum effect. In addition, this paper finds that duration factor defined as the difference in returns of short-duration and long-duration stocks captures well the momentum profits. That is, a two-factor model with the market and duration factor performs much better than competing asset pricing models in explaining the momentum effect. Finally, when controlling for the duration factor, the explanatory power of the foreign/institutional illiquidity factor on the momentum profits disappears. In sum, our empirical finding indicates that the duration factor is the most important ingredient in understanding the momentum effect in the Korean stock market.

This study examines the co-integration relationships between Association of Southeast Nations (ASEAN) stock indices as a way to assess the feasibility of policy initiatives to strengthen market integration in ASEAN and identify implications for portfolio investors.

The authors employ threshold co-integration tests and a non-linear autoregressive distributed lag (NARDL) model to study the asymmetric dynamics of ASEAN equity markets. The study’s data cover the 2009–2022 period for seven member states: Cambodia, Indonesia, Malaysia, the Philippines, Singapore, Thailand and Vietnam.

The authors find evidence supporting co-integration relationships; adjustment toward equilibrium is asymmetric in the short run and symmetric in the long run for these countries. While co-movement in ASEAN equity markets seems encouraging for initiatives seeking to foster financial integration in regional economies, the benefits for international portfolio diversification appear to be neutralized.

The issue of stock market integration is important among policymakers, investors and academics. This study examines the level of stock market integration in ASEAN during the 2009–2022 period. For this purpose, advanced co-integration techniques are applied to different frequencies of data (daily, weekly and monthly) for comparison and completeness. The empirical analysis of this study is conducted using the Enders and Siklos (2001) co-integration and threshold adjustment procedure. This advanced co-integration technique is superior compared to other co-integration techniques by permitting asymmetry in the adjustment toward equilibrium.

The purpose of this paper is to look at the contemporaneous movement of the stock market indices of the five most COVID-infected countries, namely, the USA, Brazil, Russia, India and UK after the first wave along with market indices of the three least affected countries, namely, Hong Kong, South Korea and New Zealand during the first wave.

Data have been collected from the website of Yahoo finance on daily closing values of five indices. Augmented Dickey–Fuller test with its three forms has been applied to check the stationarity of the select five indices at the level and at the first difference before the pandemic, during the pandemic and post-first wave of the pandemic. Johansen cointegration test is applied to find out that there is no cointegration among the select five indices.

The five countries do neither fall in the same economic and political zone nor do they have the same economic status. But during the period of pandemic and the new-normal period, the cointegration is very distinct. The developing and developed nations thus stood at an indifferentiable stage of the economic crisis which is well reflected in their stock markets. However, the least three COVID-affected countries do not show any cointegration during the pandemic time.

The comovement even seen during the normal time in the other studies is not compared to a similar period in earlier years. But, in this study to look into the exclusive effect of COVID pandemic, the period most affected with it is compared with the period after it and that in the immediate past year had no effect.