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This paper establishes a predictive relationship between financial vulnerability and volatility in emerging stock markets. Focusing on China and India and utilizing GARCH-MIDAS models, we show that incorporating financial vulnerability can substantially improve the forecasting power of standard macroeconomic fundamentals (output growth, inflation and monetary policy interest rate) for stock market volatility. The findings have significant implications for investors to improve the accuracy of volatility forecasts. [ABSTRACT FROM AUTHOR]

Investors step into the stock market with the objective of earning smart returns on their investments. The stock market can help in realising these goals of the investors, however, all investments are subject to risks. The origin of the risk is the uncertainty of realising the desired returns on the investment. This aspect is known as risk of the investment. This paper aims to search the best model to estimate and forecast volatility of Indian and Chinese stock market. The data for the paper is related to the two main indices of Indian Stock Market namely, SENSEX and NIFTY and two indices of Chinese stock market, namely, Shenzhen composite index and Shanghai composite index for the period July 2003 to June 2013. We applied symmetrical as well as asymmetrical GARCH models to the data. Among all the three models i.e. GARCH, EGARCH and TARCH, we found the GARCH (1,1) model as the best model to estimate and forecast the volatility of Chinese stock market for both the daily and weekly return series. For the Indian stock market, the recommended volatility estimation and forecasting model is EGARCH model that captures the leverage effect. We did not find volatility clustering and leverage effect for the monthly return series for both Indian and Chinese stock market. Thus, it is suggested to use the traditional time invariant volatility models for the monthly return series. [ABSTRACT FROM AUTHOR]

Purpose: The Global recession of 2008 was the worst financial crisis in the postworld war economic history that brought in severe disruptions in global investments and capital flows. Not surprisingly, research interest in the field of market integration has considerably increased over the last decade. This paper analyses the dynamics of price integration among Asian financial markets during the postfinancial crisis period. Design/methodology/approach: We employ an Autoregressive Distributed Lag (ARDL) bounds testing approach to cointegration and a Granger Causality/Block Exogeniety test from a Vector Error Correction Model (VECM) on monthly stock index data of five leading Asian economies from April 2009 to March 2020. Findings: The cointegration results could not produce any conclusive evidence of long-run relations between stock markets. There exists weak price convergence among markets, and financial integration is partial and in an imperfect form. Research limitations/implications: Stock price performance in China is closely "coupled" with that in India, but both markets appear to be the short-run predictors of Asian stock returns. The research uses only the benchmark stock indices of the selected economies. Consideration of mid-cap and small-cap segments where foreign investments are significant today can validate the findings further. Practical implications: The asymmetric pattern of price behavior of Asian markets has important implications for the pricing efficiency of national markets and offers arbitrage potentials for global investors to optimize returns through market diversifications on a long-term perspective. The finding definitely will be a great help to investors who are potentially interested in a trading strategy that offers greater returns with limited exposure to market risks. Originality/value: Compared with previous studies, the research uses the most recent data of leading Asian markets and applies the robust method of ARDL Bounds testing approach that allows us to understand better if the economic recoveries and advancement have had an effect on market coupling and stock price transmissions. [ABSTRACT FROM AUTHOR]

Purpose: The purpose if this paper is to examine the turn-of-the-month effect in the equity market of three major emerging countries – Brazil, India and China – from January 2000 to December 2017. Design/methodology/approach: Ordinary least square regression analysis is used to examine the presence of the turn-of-the-month effect and to test the efficiency of the emerging stock markets. The characteristics of the returns during the turn-of-the-month days are compared with that of the non-turn-of-the-month trading days. Findings: The average returns during turn-of-the-month days for all the considered emerging market indices are significantly higher than the non-turn-of-the-month days for the full sample. For the subsample analysis, the average returns for Brazil and India for pre-GFC period are higher on the turn-of-the-month days than on the non-turn-of-the-month days. However, the effect disappears in China during the GFC period. During the crisis period, the results show that the turn-of-the-month effect disappears in Brazil and India, whereas for China, the effect is significant. For the post-GFC period, the-turn-of-the-month effect reappears for all the countries. Practical implications: The results have important implications for both traders and investors. The authors' results indicate that the market participants can time the stock markets of these countries by taking long positions especially during the times when the turn-of-the-month effect is highly significant. Originality/value: To the best of the authors' knowledge, this paper is the first to study the turn-of-the-month effect, in the key emerging countries such as Brazil, China and India. Second, the authors divide the sample into three subperiods based on the 2008 GFC such as pre-GFC, GFC and post-GFC to understand the dynamic behavior of turn-of-the-month effect over time. Most importantly, the authors control for the day-of-the-week effect while examining the turn-of-the-month effect. [ABSTRACT FROM AUTHOR]

This paper investigates whether the Covid-19 virus negatively impacts Indian companies dependent on the Chinese supply chain. As the virus originated from China, there was an anti-China sentiment observed all over the world. Our empirical study analyzes the impact of lockdown and relief measures announced by the government on companies dependent on Chinese suppliers vis-à-vis companies that are not dependent on Chinese suppliers on selected stocks in the Indian stock market. We find no negative sentiment among Indian investors on Chinese supply chain-dependent companies in India. [ABSTRACT FROM AUTHOR]

Copyright of International Research Journal of Business Studies is the property of Prasetiya Mulya Publishing, Universitas Prasetiya Mulya and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study examines the impact of domestic and foreign shocks on the real and financial sector of BRIC countries. For this purpose, we use a structural vector autoregressive (SVAR) model over the extended period of 1997 to 2016. We conclude that domestic policy shocks have a more substantial impact on Brazilian, Indian, and Russian economy than foreign shocks, while foreign shocks have more contribution in the case of China. Interestingly, results show the negative impact of policy shocks on bank credit provided, implying its role in multiplying the impact of shocks on real variables. Surprisingly EPU of USA has a positive impact on stock markets of India and China, implying capital flight phenomenon, where investor transfer investment from risky to safer places. [ABSTRACT FROM AUTHOR]

This paper uses continuous and discrete wavelet tools to evaluate the dynamic correlation and causality between the U.S. economic policy uncertainty (EPU) and stock markets in China and India from 1997 to 2018. The dynamic correlation in the time-frequency domain is obtained by continuous wavelet coherence, and the causality over time and frequencies is tested by the linear and non-linear Granger causality based on discrete wavelet transform. The results show that the interaction between EPU in the U.S. and stock returns in China and India is weak in the short term but gradually becomes stronger in the long term, especially when significant financial events occur. There is no Granger causality in the short term; however, there is unidirectional or bidirectional causality in the medium and long term. These conclusions may provide useful reference for policymakers and investors in Chinese and Indian stock markets to prevent cross-country risk contagion from the U.S. [ABSTRACT FROM AUTHOR]

Copyright of International Research Journal of Business Studies is the property of Prasetiya Mulya Publishing, Universitas Prasetiya Mulya and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The dynamics of integration is studied in two time frames: short-term and long-term. The focus of this paper is to evaluate the short-term integration dynamics of stock exchanges of India (Bombay Stock Exchange - BSE) and China (Shanghai Stock Exchange - SSE) with US (S&P500) and Europe (FTSE100). Granger Causality Test was used to identify the direction of causality, and impulse response was used to identify the effect of shocks from one market to the other markets. The outputs suggest that BSE Granger-causes FTSE100 and S&P500. But SSE does not Granger-cause S&P500 and FTSE100. This result contradicts the findings of the prior research (Janak Raj and Sarat, 2008), which suggest that Indian stock markets do not Granger-cause US, European and other developed markets. The impulse response of S& P500 to BSE suggests that any shocks from BSE to S&P500 survive for a period of two days. This study would help portfolio managers and investors to view diversification from a new perspective and take advantage of it. [ABSTRACT FROM AUTHOR]

Brazil, Russia, India, and China (BRIC), the most rapidly developing countries in the twenty-first century, are expected to become the four dominant economies by the year 2050. This paper examines the risk and return characteristics of their stock markets over the past few years. The markets in Brazil, Russia, and China produce significantly positive mean excess return. China and Brazil not only have the largest Sharpe ratio, but also have the better fit to a conditional risk-return model in terms of stock returns variations. In all markets, the systematic risk, beta, is still the most important factor in explaining returns variations. Other risk measures, including unsystematic risk, skewness, and kurtosis, provide limited incremental explanatory power. However, while the intercept coefficient in the regression model is not significantly different from zero in Brazil, Russia, and India, the coefficient for China is significantly positive, indicating that the Chinese stock market generates positive abnormal risk-adjusted returns. [ABSTRACT FROM AUTHOR]

China and India are the world's most populous and rapidly developing countries and are often discussed together. We examine life insurance firms in these two countries between 2008 and 2016 using a game cross-efficiency model, which comparatively measures insurers' performance using Nash equilibrium weights. We investigate whether there is an optimal business model for three business dimensions: assets, funding and income. From our second-stage regressions we conclude that strategic focus is superior in terms of assets and income to diversification at the insurer level. At the capital market and economic levels, economic development, unemployment, stock market development and other variables are also important. Our study provides useful insights into how a business model can be made more efficient in large emerging markets. [ABSTRACT FROM AUTHOR]

The present study investigates dynamics of contagion and spillover of volatility amongst stock markets of five economies ; US, UK and Japan and two Asian emerging economies viz. India and China. The period of study is eleven years; January 1, 2009-December 31, 2019 and the data is daily closing prices of their stock market indices. The study makes a distinction between contagion and spillover whereby a shock is considered spillover if its impact is seen with a lag of one period only, while contagion is a residual transmission (Masson, P (1998); Dungey, M and Martin, V L (2007)). The results revealed substantial contagion and information flows from one market to another, be it developed or emerging. Further although US markets still decides the direction of markets, the importance of other markets has increased over the years. Further, US market on its own now appears to look for clues from both developed and emerging markets including India and China. The two emerging markets of Asia, India and China observe a lot of co-movement in returns with spillovers being linked to the developed markets. The study also tested for pre-conditions of stationarity, autocorrelation and heteroscedasticity. [ABSTRACT FROM AUTHOR]

Flow of information and volatility coming from stock markets of other countries have significant impact on the stock market of a country. Volatility is even higher in the case the countries enjoy good economic conditions among themselves. The present manuscript aims to probe into the spread of impacts over a large range of returns and volatility in four major equity markets of Asia viz. India, China, Hongkong and Japan for a period of 18 years ranging from 2000 to 2017. The study uses VAR based GARCH model to determine the volatility spillover among the chosen countries for the period under assessment. The empirical outcomes of the study present that all selected markets have responded to their own lag of conditional volatility along with news shocks. The impact of conditional variance is higher in comparison to shocks which is an indication that markets fundamentals are stronger than corrections or shocks. The results of cross country spillover show that volatility of Shanghai Stock Exchange of China and shocks from Japan and Hongkong markets assert a significant effect over volatility of Indian equity market. Volatility of stock markets of Japan and China is not affected by the cross market volatility and shocks spillover from India. In contrast, volatility of Hongkong market is affected by shocks and volatility of Indian equity markets. Findings of the research have meaningful insights for the Governments and regulators, academicians, researchers, investors and fund managers in framing investment strategies in the chosen markets. [ABSTRACT FROM AUTHOR]

Volatility is an important component in risk return analysis of financial assets. It imparts liquidity to the financial system and also serves as an information source for rational decision making. Since the latter half of the 20th century, volatility in stock returns has been found to be time varying and exhibiting patterns and therefore, various models have been developed to capture such dynamic properties of volatility. The introduction of Autoregressive Conditional Heteroscedasticity (ARCH) models by Engle in 1982 has led to a better understanding of the behaviour of stock market volatility than the traditional measures including standard deviation. The present study attempts to model various aspects including clustering, leverage effect and spillover effect of stock market volatility in Indian and Chinese stock markets during 2001-2016 using daily time-series data with Generalised Autoregressive Conditional Heteroscedasticity (GARCH) models. Volatility has been seen to be highly persistent in both the markets. The T-GARCH model has been applied in order to assess the presence of information asymmetry that bad news impacts volatility more than good news. Our results reveal that both Indian and Chinese stock markets' volatility shows time varying behaviour. The theoretical reasoning of the asymmetric impact of news that bad news affects volatility more than good news has been confirmed in both markets. Furthermore, the spillover effect of volatility across the two markets has been tested using the T-GARCH-X model. The results show unidirectional spillover effect of volatility from Chinese stock market to the Indian stock market. This implies that shocks from Chinese stock market impact conditional volatility in the Indian stock markets only but not vice-versa. [ABSTRACT FROM AUTHOR]

When analysing the data periods including the pre-financial and financial crisis periods, the results show that investors might make profits by using Variable Length Moving Average (VMA) trading rules as buying signals rather than as selling signals shown for the Brazil, Russia, India and China (BRIC) stock markets. However, investors may find it difficult to make profits in a financial crisis period, suggesting that more detailed information should be investigated, since the significant results shown during the full period might not reveal the differences between the pre-financial and financial crisis periods. [ABSTRACT FROM AUTHOR]