Empirical analysis of lead-lag relationship in the Indian stock market amid the COVID-19 pandemic

Background: The purpose of this study is to explore the trends and causes of established and emerging nations' stock market integration with India. The National Stock Exchange (NSE) indices act as a counterweight to international market indices. This study investigates the sustained interest of foreign investors in the Indian stock market in the wake of capital market reforms, as well as whether it moves in tandem with other markets in Asia and the United States. Methods: Our study examined the possibility of cross-country cointegration between the largest economies and indices around the world using multiple financial econometric models, such as Augmented Dickey-Fuller, Unit Root, Correlation, and Johansen Cointegration. Results: The findings of this study significantly support the notion that Indian and international financial markets are highly integrated. Vector error correction model indicates that the Indian market (NSE) is highly cointegrated with the US market (National Association of Securities Dealers Automated Quotations) and increased volatility signifies global contagion. Conclusion: A cursory examination of the data reveals distinct investment and portfolio diversification options for global investors. This could assist regulators in formulating more effective rules regarding price discovery processes.

Background: The purpose of this study is to explore the trends and causes of established and emerging nations' stock market integration with India. The National Stock Exchange (NSE) indices act as a counterweight to international market indices. This study investigates the sustained interest of foreign investors in the Indian stock market in the wake of capital market reforms, as well as whether it moves in tandem with other markets in Asia and the United States. Methods: Our study examined the possibility of cross-country cointegration between the largest economies and indices around the world using multiple financial econometric models, such as Augmented Dickey-Fuller, Unit Root, Correlation, and Johansen Cointegration. Results: The findings of this study significantly support the notion that Indian and international financial markets are highly integrated. Vector error correction model indicates that the Indian market (NSE) is highly cointegrated with the US market (National Association of Securities Dealers Automated Quotations) and increased volatility signifies global contagion. Conclusion: A cursory examination of the data reveals distinct investment and portfolio diversification options for global investors. This could assist regulators in formulating more effective rules regarding price discovery processes.

Background: The purpose of this study is to explore the trends and causes of established and emerging nations’ stock market integration with India. The National Stock Exchange (NSE) indices act as a counterweight to international market indices. This study investigates the sustained interest of foreign investors in the Indian stock market in the wake of capital market reforms, as well as whether it moves in tandem with other markets in Asia and the United States. Methods: Our study examined the possibility of cross-country cointegration between the largest economies and indices around the world using multiple financial econometric models, such as Augmented Dickey-Fuller, Unit Root, Correlation, and Johansen Cointegration. Results: The findings of this study significantly support the notion that Indian and international financial markets are highly integrated. Vector error correction model indicates that the Indian market (NSE) is highly cointegrated with the US market (National Association of Securities Dealers Automated Quotations) and increased volatility signifies global contagion. Conclusion: A cursory examination of the data reveals distinct investment and portfolio diversification options for global investors. This could assist regulators in formulating more effective rules regarding price discovery processes.

Background: The purpose of this study is to explore the trends and causes of established and emerging nations' stock market integration with India. The National Stock Exchange (NSE) indices act as a counterweight to international market indices. This study investigates the sustained interest of foreign investors in the Indian stock market in the wake of capital market reforms, as well as whether it moves in tandem with other markets in Asia and the United States. Methods: Our study examined the possibility of cross-country cointegration between the largest economies and indices around the world using multiple financial econometric models, such as Augmented Dickey-Fuller, Unit Root, Correlation, and Johansen Cointegration. Results: The findings of this study significantly support the notion that Indian and international financial markets are highly integrated. Vector error correction model indicates that the Indian market (NSE) is highly cointegrated with the US market (National Association of Securities Dealers Automated Quotations) and increased volatility signifies global contagion. Conclusion: A cursory examination of the data reveals distinct investment and portfolio diversification options for global investors. This could assist regulators in formulating more effective rules regarding price discovery processes.

BRICS economies are important in recent times because the economic growth rates will be higher than the growth rates of G-6 economies in the near future. But the year 2020 has smashed up this tendency due to volatile stock markets of BRICS economies. A detailed examination of the BRICS stock market to determine volatility and relationships since the crisis of 2020 is hardly available in the available research. With this in mind, an attempt has been made to track the stock market's volatility and relationship among the BRICS (Brazil, Russia, India, China, and South Africa) stock market return based on the daily for the period from November 18, 2019 to May 7, 2021. This study deals with the statistical test of GARCH family model and ARDL model. GARCH model shows that the stock market of Russia and India are volatile. The EGARCH model demonstrates that leverage effect exists only in the Indian stock market. ARDL test validates a long-run relationship of the stock market of Russia with China and of the Indian stock market with South Africa. ARDL test also shows a short-run relationship running from the Brazil stock market to the other select stock market, from the Indian stock market to the stock markets of Brazil and South Africa, and from the South African stock market to the Indian stock market. So it can finally be said that investors under the BRICS stock markets should design adequate measures to protect their investments by executing appropriate hedging plan.

Literature indicates that there are several studies that have focused on stock market relationships and integration but very limited studies are found in this area that have focused for South Asian markets. Few that have studied stock market relationships and market integration in this region have never included Bhutan, probably due to unavailability of data on Bhutanese stock market or because it is too insignificant in terms of regional market. Particularly the relationship between Bhutanese stock market and Indian stock market is expected to be positive and significant given the very close economic ties/dependence with India. Correlation and regression analysis (data sample includes January 2006 to December 2011 for weekly returns) do not show relations between Bhutanese, Indian or any other stock returns except between Indian and Pakistani market returns. Few time varying effects were noticed when correlations were calculated for different set of split data. In general South Asian markets seem quite independent of each other. Indian stock market, which is more proficient in the region, was expected to have some influence but the results do no support this. Based on the Granger causality test, Indian market seems to provide unidirectional effect on most of the neighbouring stock markets.

Foreign Institutional Investment in the Indian Equity Market: An Analysis of Daily Flows during January 1999-May 2002

In the recent years, globalization, economic assimilation and integration among countries and their financial markets have increased interdependency among Asian stock markets. This increased interdependency among the Asian stock markets may have impact on the global investors for their assets allocation decision and on the economic policies of economies for ensuring economic stability. Hence, there is a need to study the extent of integration between the Indian stock market and Asian stock markets, which is the primary objective of this study. The study uses monthly stock price indices of the Asian countries, namely India, Japan, Singapore, Hong Kong and Taiwan. This study covers the period from April 1994 to December 2009. The results obtained from the analysis by employing a wide range of statistical tools: Engle-Granger Test, Johansen Cointegration Test and Granger Causality Test. The results of the study indicate that Indian stock market is not integrated with the Asian stock markets considered in our study. It also implies that international investors can get portfolio diversification benefits in Indian market since Indian stock market is not affected by the market trends in Asian countries.

PurposeThis paper attempts to investigate the presence of seasonal effects in the Indian stock market.Design/methodology/approachThe paper tests the efficiency of the Indian stock market through a number of hypotheses. Week day effects, day‐of‐the‐week, weekend, January and April effects are examined by applying a variety of statistical techniques.FindingsThe results are interesting and contradict some of the findings found elsewhere. The negative Monday effect and the positive January effects are not found in India. Instead the Monday returns are positive while Tuesday returns are negative.Research limitations/implicationsThe seasonal effects in the Indian market have been examined by the two major indices, the Bombay Stock Exchange Index and the National Stock Exchange Index. However, it must be remembered that the Indian economy became deregulated from 1991 and this may have had an impact on the markets.Practical implicationsThis study indicates that the Indian stock market does not exhibit the usual seasonal anomalies such as Monday and January effect. The absence of Monday effect could be due to the settlement period in Indian market. That the tax year ends in March and December has no special significance may explain the non‐existence of JanuaryOriginality/valueMost of the studies on anomalies have dealt with the developed markets. The Indian market has its unique Badla financing, settlement period duration and trading regulations. The presence/absence of the anomalies may provide support to some of the hypotheses used to explain them.

Purpose : This study examined the applicability of the Fama-French three-factor model in the Indian stock market. The model comprised of three independent variables: beta/market risk, size, and value. The study aimed to validate these factors to determine their capacity to elucidate the risk-return relationship in the Indian stock market. Methodology : Three independent variables––namely, market risk (beta), size, and value were considered. Data spanning 16 years was collected from January 2006 to December 2021. Portfolios were constructed using equities from the Nifty 500 index, the 91-day Treasury bill as the risk-free rate, and the Nifty 200 index return used as a proxy for market returns. Findings : The study revealed a significant presence of size in the Indian stock market, according to its findings; however, the influence of value was determined to be minor. Another significant finding that emerged from the investigation was the confirmation that the CAPM does carry some weight. Practical Implications : Investors across diverse categories in the stock market can leverage the current study’s findings when incorporating any stock into their portfolios. Investors may evaluate any single variable or a combination of variables to assess the risk–return trade-off of a specific stock. For instance, investors might select low-beta stocks for less risk, small-cap stocks due to their superior performance compared to large-cap companies, or undervalued stocks that yield more profits than overvalued equities. Originality : Most previous studies concentrated on small datasets. However, the current research utilized a substantial dataset and incorporated key indexed equities, specifically the NSE Nifty 500. A significant discovery, in contrast to prior investigations, was the validation of the beta.

The Indian stock market, one of the largest and most dynamic among emerging economies, has experienced substantial growth and transformation over the past few decades. Liberalization policies introduced in the early 1990s, combined with globalization and technological advancements, have contributed to the increasing participation of both domestic and foreign investors. Despite these advancements, the market continues to display inefficiencies and behavioral tendencies that offer fertile ground for the study of stock market anomalies. Traditional financial theories, particularly the Efficient Market Hypothesis (EMH), suggest that stock prices fully incorporate all available information, thereby negating the possibility of consistently achieving abnormal returns. However, empirical evidence from markets worldwide indicates recurring deviations from this principle. These deviations, termed "anomalies," represent patterns of returns inconsistent with EMH and challenge the notion of rational investor behavior. The existence of such anomalies offers potential opportunities for investors to formulate profitable strategies while simultaneously questioning the universality of traditional financial models. This thesis primarily investigates three well-documented stock market anomalies within the context of the Indian stock market: the size effect, momentum effect, and value effect. The size effect refers to the consistent outperformance of small-cap stocks relative to large-cap stocks, even after adjusting for risk. The momentum effect highlights the phenomenon where stocks exhibiting superior past performance continue to generate positive returns over short- to medium-term horizons. Lastly, the value effect indicates that stocks with lower valuation metrics, such as low Price-to-Earnings (P/E) or Price-to-Book (P/B) ratios, outperform their high-growth counterparts in the long run. Although these anomalies have been extensively studied in developed markets like the United States and Europe, emerging markets such as India present unique attributes that warrant deeper analysis. The Indian market is characterized by higher volatility, greater information asymmetry, regulatory transitions, and a significant presence of retail investors, all of which potentially amplify or alter the behavior of these anomalies. Behavioral finance factors such as overconfidence, herd behavior, and loss aversion are particularly prevalent among Indian investors, further influencing the manifestation of inefficiencies in the market. The findings reveal that all three anomalies are present and statistically significant in the Indian stock market, albeit with varying degrees of persistence across different periods. The size effect is notably strong, supporting the argument that small-cap stocks offer higher risk-adjusted returns compared to large-cap stocks. Momentum strategies based on prior winners also demonstrate substantial profitability over short horizons, highlighting potential for tactical asset allocation. The value effect remains evident, particularly during market downturns, suggesting that undervalued stocks provide resilience and long-term outperformance. These insights carry practical implications for investors, fund managers, and policymakers. By acknowledging the presence of anomalies, investment strategies can be optimized to exploit these inefficiencies. Simultaneously, regulators and policymakers may consider measures to enhance transparency and reduce information asymmetry, contributing to improved market efficiency.

In this paper we examine the information spillover and volatility spill-over relationship for Indian stock market. We cover data during 1992-2011. We examine if there has been an increase in volatility persistence in the Indian stock market on after the process of financial liberalization initiated in India. Further, we examine the shifts in stock price volatility and the nature of events that apparently cause the shifts in volatility. We examine if there has been an increase in volatility persistence in the Indian stock market on after the process of financial liberalization initiated in India. This paper explores to develop alternative models from cointegration, VECM, Variance De-composition Analysis, Granger causality, Block Exogeneity wald test, Impulse Response Analysis and alternative forms of the Autoregressive Conditional Heteroscedasticity (ARCH) or its generalisation, the Generalised ARCH (GARCH) family, to estimate volatility in the Indian equity market return. Bidirectional informational spillover is confirmed. The bidirectional volatility spill-over, persistence and clustering is also confirmed in the sample series. Our findings have implications for policy makers, hedgers and investors. The research contributes to present investment literature for emerging markets such as India.

The Indian stock market is considered to be one of the earliest in Asia, which has been in operation since 1875. However, it remained largely outside the global integration process until 1991. A number of developing countries in association with the International Finance Corporation and the World Bank took steps to establish and revitalize their stock markets as an effective way of mobilizing and allocating funds. In line with the global trend, reform of the Indian stock market also started with the establishment of Securities and Exchange Board of India (SEBI), although it became more effective after the stock market scam in 1991. With the establishment of SEBI and technological advancement, the Indian stock market has now reached the global standard. The major indicators of stock market development show that significant development has taken place in the Indian stock market during the post-reform period. This chapter seeks to examine in this context whether reform in the Indian stock market has led to integration with the developed stock markets in the world. The study finds that contrary to general belief, the Indian stock market is not cointegrated with the developed market as yet. Of course, some short-term impact does exist, although it is found to be unidirectional for obvious reasons. That is to say, the developed stock markets, viz., the US, UK and Hong Kong stock markets, Granger cause the Indian stock market but not vice versa. However, the study does not find any causality between the Japanese stock market and the Indian stock market. It is derived from the study that although some positive steps have been taken up, which are responsible for the substantial improvement of the Indian stock market, these are perhaps not sufficient enough to become a matured one, hence not integrated with the developed stock markets so far.

Market efficiency has always been the concern of market regulators, investors, and researchers. Market efficiency tests showed different and mixed evidences in the developing markets. The present study deals with the testing of weak form of efficiency and the efficient market hypothesis on Indian stock market in the form of random walk, during the period of 2007–2008 based on closing prices and daily returns on the Indian stock market three representative indices: S&P CNX 500, CNX 100, and BSE 200. The paper discusses and examines three types of anomalies namely Monday Effect, Friday Effect and Day of the Week effect. The findings of this study bring out that none of the above anomalies exist in the Indian stock market as information ally efficient. Serial correlation and run test also support the Random Walk Theory and market efficiency hypothesis.

Price and Volatility Spillovers Across North American, European and Asian Stock Markets: With Special Focus on Indian Stock Market