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This paper aims to attempt to capture the co-movement of the Indian equity market with some of the major economic giants such as the USA, Europe, Japan and China after the occurrence of global financial crisis in a multivariate framework. Apart from these cross-country co-movements, the study also captures an intertemporal risk-return relationship in the Indian equity market, considering the covariance of the Indian equity market with the other countries as well.

To account for dynamic correlation coefficients and risk-return dynamics, vector autoregressive (1) dynamic conditional correlation–asymmetric generalized autoregressive conditional heteroskedastic model in a multivariate framework and exponential generalized autoregressive conditional heteroskedastic model in mean with covariances as explanatory variables are used. For an in-depth analysis, Markov regime switching model and optimal hedging ratios and weights are also computed. The span of data ranges from August 10, 2010 to August 7, 2015, especially after the global financial crisis.

The Indian equity market is not completely decoupled from mature markets as well as emerging market (China), but the time-varying correlation coefficients are on a downward spree after the global financial crisis, except for the US market. The Indian and Chinese equity markets witness a highest level of correlation with each other, followed by the European, US and Japanese markets. Both the optimal portfolio hedge ratios and portfolio weights with two asset classes point out toward portfolio risk minimization through the combination of the Indian and US equity market stocks from a US investor viewpoint. A negative co-movement between the Indian and US market increases the conditional expected returns in the Indian equity market. There is an insignificant but a negative relationship between the expected risk and returns.

The study provides an insight to the international as well as domestic investors and supports the construction of cross-country portfolios and risk management especially after the occurrence of global financial crisis.

The present study contributes to the literature in three senses. First, the period relates to the events after the global financial crisis (2007-2009). Second, the study examines the co-movement of the Indian equity market with four major economic giants such as the USA, Europe, Japan and China in a multivariate framework. These economic giants are excessively following the easy money policies aftermath the financial crisis so as to wriggle out of deflationary phases. Finally, the study captures risk-return relationship in the Indian equity market, considering its covariance with the international markets.

This study is designed to examine the dynamic interrelationships between four cryptocurrencies (Bitcoin, Ethereum, Dogecoin and Cardano) and the Indian equity market. Additionally, the study seeks to investigate the potential safe haven, hedge and diversification uses of these digital currencies within the Indian equity market.

This study employs the wavelet approach to examine the time-varying volatility of the studied assets and the lead-lag relationship between stocks and cryptocurrencies. The authors execute the entire analysis using daily data from 1st October 2017 to 30th September 2023.

The result of the study shows that financial distress due to the pandemic and the Russian invasion of Ukraine have a negative effect on the Indian equities and cryptocurrency markets, escalating their price volatility. Also, the connectedness between the returns of stock and digital currency exhibits a strong positive relationship during periods of financial distress. Additionally, cryptocurrencies serve as a tool of diversification or hedging in the Indian equities markets during normal financial circumstances, but they do not serve as a diversifier or safe haven during periods of financial turmoil.

This study contributes to understanding the relationship between the Indian equity market and four cryptocurrencies using wavelet techniques in the time and frequency domains, considering both normal and crisis times. This can offer valuable insights into the potential of cryptocurrencies inside the Indian equities markets, mainly with respect to varying financial conditions and investment horizons.

Many studies have shown that from a theoretical and empirical point of view, downside risk-based measures of risk are better than the traditional ones. Despite academic appeal and practical implications, downside risk has not been thoroughly examined in markets outside developed country markets. Using downside beta as a measure of downside risk, this study examines the relationship between downside beta and stock returns in Indian equity market, an emerging market with unique investor, asset and market characteristics.

This is an empirical study done by using ranked portfolio return analysis and regression analysis methodologies.

The study results show that downside risk, as measured by downside beta, is distinctly priced in the Indian equity market. There is a direct positive relationship between downside beta and contemporaneous realized returns, indicating a premium for downside risk. Downside risk carries a higher weightage than upside potential in the aggregate return of the stock portfolios. Downside beta is a better measure of systematic risk than conventional market beta and downside coskewness.

The empirical results support the adoption of downside beta in practice and provide a case for replacing traditional beta with downside beta in asset pricing applications, trading and investment strategies, and capital allocation decision-making.

This is one of the first in-depth studies examining downside beta in Indian equity markets using a broad sample of individual stock returns covering a wide time range of 22 years. To the best of our knowledge, this study is the first one to compare downside beta and downside coskewness using individual stock data from the Indian equity market.

The purpose of the study is to investigate the long-run and short-run dynamic relationship between crude oil prices and the movement of Sensex for the period of 2000–2018.

The study uses the augmented Dickey–Fuller test for the presence of unit root, Johansen cointegration test for estimating the cointegration among the variables. Further, in the case of no cointegration found, the study employed the vector autoregression (VAR) model to estimate the long-run relationship and the Granger causality/Wald test for short-run relationship. The study also conducted tests for the prerequisites of the model: serial correlation, heteroskedasticity and normality of data.

The study found that both the variables, crude oil prices and Sensex are integrated of order 1, that is, I (1), and there is no cointegration between them. Further, the results proliferated from the VAR model unfold the marked effect of previous month crude oil prices (lag 1) on the movement of Indian stock market represented by Sensex considered as the benchmark index. Furthermore, VAR–Granger causality/block exogeneity Wald tests results indicated that there is a causal relationship between the crude oil prices and Sensex under the VAR environment. The model does not have any serial correlation and heteroskedasticity indicating toward the unbiased and robust estimates.

The study is conducted till the year 2018, and data for the present period (post-2018) is excluded due to ongoing trade issues between the USA and oil-exporting countries such as Iran. The current COVID-19 outbreak has also put serious issues. Due to limited time and availability of standardized data, researchers have considered Sensex as equity index only, but for more generalized research outcome few other equity indexes could have been taken for study.

The study is completely original in nature and is an extensive study of the relationship between the crude oil price and Indian stock market with reference to causality between the variables.

This study aims to investigate the persistence ability of accounting variables, namely, abnormal earnings, book value, accruals and cash flows over a period of time and their valuation relevance in Indian scenario.

The study utilizes the generalized version of the Ohlson model which links market prices with abnormal earnings, book value and earning components (accruals and cash flows). Fixed-effect panel data regression is used to analyze six years of data on the sample units to determine the persistence and valuation relevance.

The findings provide evidence on the construct of persistence and value relevance of earnings and book value of equity in the Indian context. The findings further confirm that investors in India are fixated on earnings and fail to attend separately to the cash flow and accrual components of earnings while undertaking their investment decisions.

The empirical findings of the study will enable the analysts and investors to understand the relevance and persistence of accounting variables in case of an emerging market like India.

The study extends the extant literature on value relevance studies in developed markets to an emerging market like India and enriches it in several ways.

This study aims to investigate the risk-return trade-off in the Indian equity market at both the aggregate equity market level and in the cross-sections of stock return using alternative risk measures.

The study uses weekly and monthly data of 3,085 Bombay Stock Exchange-listed stocks spanning over 20 years from January 2000 to December 2019. The study evaluates the risk-return trade-off at the aggregate equity market level using the value-weighted and the equal-weighted broader portfolios. Eight different risk proxies belonging to the conventional, downside and extreme risk categories are considered to analyse the cross-sectional risk-return relationship.

The results show a positive equity premium on the value-weighted portfolio; however, the equal-weighted portfolio of these stocks shows an average return lower than the return on the 91-day Treasury Bills. The inverted size premium mainly causes this anomaly in the Indian equity market as the small stocks have lower returns than big stocks. The study presents a strong negative risk-return relationship across different risk proxies. However, under the subsample of more liquid stocks, the low-risk anomaly regarding other risk proxies becomes moderate except the beta-anomaly. This anomalous relationship seems to be caused by small and less liquid stocks having low institutional ownership and higher short-selling constraints.

The findings have important implications for investors, managers and practitioners. Investors can incorporate the effects of different highlighted anomalies in their investment strategies to fetch higher returns. Managers can also use these findings in their capital budgeting decisions, resource allocations and other diverse range of direct and indirect decisions, particularly in emerging markets such as India. The findings provide insights to practitioners while valuing the firms.

The study is among the earlier attempts to examine the risk-return trade-off in an emerging equity market at both the aggregate equity market level and in the cross-sections of stock returns using alternative measures of risk and expected returns.

The volatile nature of foreign portfolio flows, especially flows into debt market, has large implications on financial and macroeconomic stability in recipient countries. It is necessary to identify the main drivers of portfolio investments in bond market of developing economies to design effective policies to enhance resilience of the economy and help in managing capital flow volatility. The determinants of foreign portfolio investment to Indian equity market have been examined in literature, but flows to bond market remain unexplored. Thus, the purpose of this paper is to identify the possible determinants of foreign portfolio flows to Indian bond market both in the short and in the long run.

This study carries out a time series analysis by deploying autoregressive distributed lag (ARDL) approach to cointegration of monthly data of the period from January 2002 to December 2016 for the Indian economy. A mix of pull and push factors has been analysed in this study. Domestic growth, domestic stock market performance, interest rate differential, exchange rate, volatility in exchange rate, stock market returns in other emerging economies, foreign output growth and dummy variables to trace the external developments such as global financial crisis and unconventional monetary policies of advanced economies have been used as explanatory variables.

The dominant pull factor such as interest rate differential explains the dynamics of flows in Indian bond market. The relationship between capital movements and interest rate differentials is the most accepted paradigm in international finance (Haynes, 1988). Among other domestic factors are stock market performance, volatility in exchange rates and domestic growth rates which are found to be significant drivers of foreign portfolio bond flows to India. The study also confirmed that global conditions could induce a fast outflow of capital from India.

The study concludes that both domestic factors and external factors are equally important in determining the foreign portfolio investments in the Indian debt market.

The empirical analysis conducted in this study suggests that direct and indirect measures can be taken to increase and stabilise foreign investments in the Indian bond market. Direct policy measures refer to those tools which are under the ambit of policymakers. Indirect measures comprise those tools that are not under the direct control of the fiscal and monetary authorities but require coordinated efforts of the government and private sector. In this context, strengthening of not only financial and economic but also administrative institutions will be necessary. Creditworthiness and policy credibility should be improved to address erratic foreign portfolio investment in debt market of India.

This study is an original research study. This study adds to the existing literature and is expected to guide policymakers on the specific aspect of the management of capital flows as it gets affected by changes in monetary and fiscal policies.

The Indian commodity market requires large investments and enhanced trading activity both in the national as well as the regional commodity markets. The participation of non‐professional people trading commodity markets makes the market a risky venture. Non‐professional participants simply add to the volatility factor of the market. There is a dire need for professional experts who are able to provide advice on commodity trading and build commodity inclusive portfolios. Such professional awareness, expertise, and guidance in commodity trading can come from professional commodity traders called commodity trading advisors (CTAs). The purpose of this paper is to offer arguments and insights as to why the Indian commodity market needs the participation of the CTAs. The money brought in by CTA advised clients will add to the depth, liquidity, and trade which in turn will make commodity prices more efficient. As a regulatory measure, the Indian market too can adopt guidelines structured for CTAs by Commodity Future Trading Commission and National Futures Association. The CTAs can bring the Indian commodity market at par with developed commodity markets like Chicago Board of Trade.

The paper reviews and discusses the various issues related to CTAs applicability in India. The goal of the paper is to outline the need for allowing CTAs activity in Indian commodity market and discuses the key operational and policy considerations in developing the commodity market for CTAs in India.

The recent expansion of Indian commodity market has not been very structured. The market has expanded with the expansion in demand for commodities both in spot and derivative market. There have been constraints through policy restrictions and at the same time there has been an effort for liberalization of the commodity market to bring them at par with international commodity market. Of late, the Indian equity market has been very volatile. Participation of CTAs will provide much required downside protection to traditional portfolios and they will also provide the expertise in commodity derivative trading to participants and help build the commodity inclusive portfolios with better return and lesser risk.

This is the first paper that initiates thoughts on allowing CTAs to participate in the Indian commodity market. The paper builds on the concept that CTAs would add the desired price discovery, volume, and depth to the Indian commodity market. The Indian commodity market, despite being quite old, has recently broken free from the restrictive policies and has ushered into an era of initiates supporting commodity derivative market development. To the best of the authors' knowledge, there exists no literature on CTAs participation in India.

The purpose of this paper is to examine the direction of causality between foreign institutional investment (FII) trading volume and stock market returns in the Indian context. There is evidence of uni‐directional causalities from stock returns to FII flows across various sample periods. The paper attempts to establish whether net FII trading volume causes variations in stock market returns or vice versa.

Using daily data on three different measures of FII trading volume as proxy for FII trading behaviour and S&P CNX Nifty returns, Granger‐causality approach is applied to investigate the bi‐directional causality between net FII trades and returns.

Bi‐directional causality between net FII investment and Indian stock market return is observed. In general, the FIIs seem to be chasing the Indian stock market returns. It is found that FII trading behaviour resulting in heavy trading volumes may cause variations in stock market returns only in the very short‐term, but afterwards, it is the stock market returns which cause changes in FII trading behaviour.

Since foreign equity investors monitor the movement of stock prices, and furthermore, the role of FIIs' exerting impact on Indian stock markets tends to be growing, the authorities will have to develop an environment where FIIs would maintain their positions with confidence, thereby making the markets, as well as investments, more stable. This research considered only stock market returns to test its relationship with three measures of FII trading volume; more macroeconomic as well as microeconomic variables may further be considered for the purpose.

The paper contributes some empirical evidence using three different measures of FII trading volume as proxy of FII trading behaviour, and its bi‐directional relationship with Indian stock market returns.

This paper aims to investigate the effect of economic policy uncertainty (EPU) shocks on Indian equity market sectors. The effect of domestic (Indian) and foreign (USA) EPU shocks is examined on ten major Bombay Stock Exchange sectors.

The study uses data covering the period from September 2005 to July 2023 and uses the methodology of quantile regression to investigate the heterogenous response of stock market sectors under diverse market conditions explained through the analysis of conditional quantiles distribution.

The results demonstrate that domestic and foreign EPU shocks negatively affect most of the sectors in bearish market conditions. Industrials, commodities, utilities, consumer discretionary and financial services are the most affected sectors by domestic EPU. However, the information technology sector is found to be immune to domestic EPU shocks but negatively affected by foreign EPU shocks. On the other hand, energy, financial services and fast-moving consumer goods sectors are found to be immune to foreign EPU shocks but are negatively affected by domestic EPU shocks.

Understanding the heterogeneous response of different sectors to EPU shocks could help investors and portfolio managers identify portfolio diversification opportunities.

This study makes an inaugural attempt to examine the responses of Indian stock market sectors to domestic and foreign EPU shocks using the approach of quantile regression and unveils the previously unexamined diverse reactions of Indian stock market sectors to EPU shocks originating from both India and USA.