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The paper tries to analyse empirically the impact of India's economic policy uncertainty (EPU) index on different macro-economic variables of India, like import, export, interest rate, exchange rate, inflation rate and stock market during pre-COVID-19 and COVID-19 era.

Although there exist several works where relationship and volatility among the stock markets and macro-economic indicators during the COVID-19 pandemic have been estimated, but till now none of the studies examined the effect of EPU index on different macro-economic variables in the Indian context along with the stock market due to the outbreak of COVID-19 pandemic. This is considered a noteworthy gap and hence opens up a new dimension for examination. To get a clear picture, monthly data from January, 2012 to September, 2021 have been considered where January, 2012–February, 2020 is taken as the pre-COVID-19 period and March, 2020–September, 2021 as COVID-19 period. All the data are converted into log natural. The authors applied DCC-GARCH model to investigate the impact of EPU index on volatility of selected variables over the study period across a multivariate framework and Markov regime-switching model to examine the switching over of the variables.

The results of dynamic conditional correlation - multivariate generalized autoregressive conditional heteroskedasticity (DCC-MGARCH) model indicates the presence of volatility in the dependent variables arising out of economic policy uncertainty considering the segmentation of the study period into pre-COVID-19 and COVID-19. The results of Markov regime-switching model show the variables make a significant move from low-volatility regime to high-volatility regime due to the presence of COVID-19.

It can be implied that impact of EPU in terms of volatility on the Indian Stock Market will lead to unfavourable investment conditions for the prospective investors. Even, the different macro-economic variables are to suffer from the volatility arising out of EPU across a long time horizon as confirmed from the DCC-MGARCH model.

The study is original in nature. It adds superior values from the new and significant findings from the study empirically. Application of DCC-MGARCH model and Markov regime switching model makes the study an innovative one in terms of methodology and findings.

The purpose of this paper is to examine the dynamic relationship between crude oil price volatility and stock markets in the emerging economies like BRIC (Brazil, Russia, India and China) countries in the context of sharp continuous fall in the crude oil price in recent times.

The stock price volatility is partly explained by volatility in crude oil price. The author adopt an Asymmetric Power ARCH (APARCH) model which takes into account long memory behavior, speed of market information, asymmetries and leverage effects.

For Bovespa, MICEX, BSE Sensex and crude oil there is an asymmetric response of volatilities to positive and negative shocks and negative correlation exists between returns and volatility indicating that negative information will create greater volatility. However, for Shanghai Composite positive information has greater effect on stock price volatility in comparison to negative information. The study results also suggest the presence long memory behavior and persistent volatility clustering phenomenon amongst crude oil price and stock markets of the BRIC countries.

The present study makes a number of contributions to the existing literature in the following ways. First, the author have considered crude oil prices up to January 31, 2016, so that the study can reflect the impact of declining trend of crude oil prices on the stock indices which is also regarded as “new oil price shock” to measure the volatility between crude oil price and stock market indices of BRIC countries. Second, the volatility is captured by APARCH model which takes into account long memory behavior, speed of market information, asymmetries and leverage effects.

The present study aims to investigate the impact of liquidity management on profitability of Indian Fast-Moving Consumer Goods (FMCG) firms, as well as the relationship among them, using econometric models.

Liquidity indices like current ratio, liquid ratio, absolute liquid ratio and cash conversion cycle are taken as explanatory variables, whereas age of creditors, age of debtors, age of inventory, sales and inter-temporal growth in sales are taken as control variables. Profitability is measured in terms of return on investment. The sample size is restricted to 18 Indian FMCG firms, and the secondary data for analysis are retrieved from Prowess Database of Centre for Monitoring Indian Economy for 10-year period from 2001-2002 to 2010-2011. Apart from using descriptive statistics and Pearson’s correlation analysis, panel data regression analysis like fixed-effects model and random effects model are used in the study. Hausman test is also used to make a choice between these two models.

The study results reveal a strong negative relationship between the measures of liquidity management and firms’ profitability, but firms’ size has a strong positive affiliation with profitability.

The study has been constrained by the sample size and the nature of the data, which could have well affected the results.

This study has identified critical management practices which are expected to help out finance managers and practitioners in recognizing vital areas for improving the financial performance of their firm’s operation.