Commodities play a vital role in the development of emerging economies, like India. From this perspective, the study presents dynamic correlation in the prices of gold, crude oil, exchange rate and Indian stock market from April 01, 2014 to March 28, 2018. VARMA-BEKK-GARCH model is estimated for return and volatility spillovers across markets. Bidirectional returns spillover was found between Nifty and WTI and WTI and Gold pair. Whereas the bidirectional volatility spillover between Nifty and Gold pair. From the DCC-GARCH correlational analysis, Gold was found to be effective hedging commodity for Indian stock investors than Crude Oil. The asymmetric impact of shocks in covariance is observed between Nifty 50 and all other variables. The study focuses to aid investors and portfolio diversifiers while taking investment decisions.

Exposure of the banking system to the Global Financial Crisis attracted attention to the study of riskiness and spillover. This paper studies the pattern of systemic risk and size effect in the Indian banking sector. Based on market capitalization, three public sector banks and three from the private sector were taken. Data are taken from the year 2007 to 2020. The analysis is done through quantile- CoVaR  (Conditional Value at Risk) and TENET (Tail-Event-Driven Network) measure. State variables like Indian market volatility and global risk measures negatively influence the Indian banks’ returns. Liquidity risk is a crucial aspect of private banks. Public banks experience public confidence even in the distress period. Large banks like HDFC and SBI bank offer the highest degree of systemic risk contribution. The role of private banks in transmitting systemic risk has been intensifying since 2015. Small-sized banks like PNB and BOB have become significant receivers and transmitters of risk.

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The study explored a vital issue on market microstructure that is the relationship between volatility, index returns and trading volume within the Indian stock market. The daily closing prices of select indices, from 2 March 2009 to 31 July 2018, were taken. The investor's fear index India Volatility Index (VIX) was used as an implied volatility measure popularly the investor's fear index, whereas Nifty50 daily returns and trading volume were considered. Toda-Yamamoto causality test provided limited information about the causal relationship among the variables. The results of the Toda-Yamamoto test captured only the central values of the dependent variable's distribution; therefore, quantile regression models (QRM) were estimated. Through QRM, the asymmetric impact of returns was estimated on volume and volatility changes. The asymmetric relationship of stock returns with changes in volatility and volume distribution was found significant. It was found to be stronger at extreme ends of the dependent variable's distribution. The study supports the behavioural justification for a contemporaneous negative return-volatility relationship but conditionally.Evidence of investor's heterogenous beliefs is found. The evidence of leverage effect was also significant for the lagged period. The contemporaneous negative relationship was found between volatility and volume changes highlighting that the investors in Indian markets are risk-averse. But the positive lagged effect of changes in volatility on trading volume supports sequential arrival of information hypothesis and affirmed the presence of noise traders. They make the information arrival sequential and allow the slow diffusion of information. India VIX potentially can be used for portfolio hedging purposes.

Emerging markets, including India, are witnessing an influx of foreign capital. The article investigates the role of exchange rate which influences both the net foreign institutional investments (FIIs) and the stock markets, using monthly data, from January 2008 to May 2018. The effects of real effective exchange rate are studied through non-linear ARDL co-integration. The long-run relationship is found in all the three models constructed. The results highlight the nature of FII flows in relation to exchange rate asymmetry. Real rupee depreciation has a long-run effect on their debt flows. The ‘adjustment asymmetry effect’ of exchange rate is found for equity flows in the long run. The similar effect is observed for the Nifty 50 model. Due to high volatility, even positive stock returns do not attract equity FII flows. In the short run, rupee depreciation in real terms negatively influences Nifty returns. The S&P 500 returns explain FII flows indicating information asymmetry. These outcomes serve a vital input for key stakeholders such as potential FIIs, domestic traders, regulators and policymakers.