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This study aims to analyse whether investment in green and sustainable stocks provide some cushion during current precarious time. To compare the impact of COVID-19 on the volatility of sustainable and market-capitalisation-based stocks, daily returns from Greenex, Carbonex, Large-Cap, Mid-Cap and Small-Cap index have been analysed over a period of six years from 2015 to 2021.

At the outset, logarithmic return of all selected indices has been tested for possible unit root and heteroscedastic. On confirmation of stationarity and heteroscedasticity of data, auto-regressive conditional heteroscedastic models have been applied. Thereafter, volatility is modelled through best suitable model as suggested by Akaike and Schwarz information criterions.

The findings indicate the positive impact of COVID-19 on the volatility of the indices. Asymmetric power ARCH model indicates highest significant impact of COVID-19 over the volatility of Large-Cap index, whereas exponential GARCH model detected highest significant impact of COVID-19 over the volatility of Mid-Cap Index.

To the best of the authors’ knowledge, the present study is original in the sense that it aimed at comparing the possible impact of COVID-19 over sustainable and market-capitalisation-based indices.

The present study tries to explore the various fund attributes that influence the mutual fund performance. Further, study examined the effect of mutual fund attributes namely, Net Asset Value (NAV), Portfolio turnover ratio (PTR), fund size (AUM), expense ratio (ExpR) and fund age (Age) on mutual fund's performance using gross return and risk-adjusted performance measures.

The study evaluated balanced panel data (short panel) comprising 81 Indian equity mutual fund schemes for the period of 2013–2019. The study estimated relationship between fund attributes (Net asset value, Portfolio turnover ratio, Fund age, fund size and Expense ratio) and fund performance (using gross return and risk-adjusted performance measures), through panel data regression using fixed-effects model as suggested by Hausman specification test on transformed data (due to high multicollinearity), with cluster-robust estimators due to the presence of heteroskedasticity in the model.

The findings of the study suggested that using gross return as fund performance measure, PTR, NAV, AUM, Age exhibit significant relationship with the fund performance whereas using risk-adjusted performance measures (Treynor ratio and Jensen alpha) NAV and ExpR significantly influences the fund performance. Identification of the significant relationship between fund characteristics and fund performance offers valuable insights to the investors and fund managers for rationally managing their portfolio with the ultimate objective of the wealth maximization.

The study considered only 81 equity mutual fund schemes. Some of the data were not available at the time of the study due to the policy of the company. The present study contributes significantly in examining the expected association between fund attributes and fund performance in the context of Indian mutual fund industry where this relationship were explored less.

The findings of the present study will help the investors to take the rational investment decision with the ultimate objective of maximum return with minimal risk. The findings also offer significant germane to the stakeholders in making rational decision-making process.

There is dearth of study concerning the relationship between mutual fund characteristics and fund performance with respect to Indian mutual fund industry. Therefore, study provides valuable insights to the area of the portfolio selection and management with respect to Indian mutual funds.

Climate change-related weather events significantly affect rice production. In this paper, we investigate the impact of and interrelationships between agriculture inputs, climate change factors and financial variables on rice production in India from 1970–2021.

This study is based on the time series analysis; the unit root test has been employed to unveil the integration order. Further, the study used various econometric techniques, including vector autoregression estimates (VAR), cointegration test, autoregressive distributed lag (ARDL) model and diagnostic test for ARDL, fully modified least squares (FMOLS), canonical cointegrating regression (CCR), impulse response functions (IRF) and the variance decomposition method (VDM) to validate the long- and short-term impacts of climate change on rice production in India of the scrutinized variables.

The study's findings revealed that the rice area, precipitation and maximum temperature have a significant and positive impact on rice production in the short run. In the long run, rice area (ß = 1.162), pesticide consumption (ß = 0.089) and domestic credit to private sector (ß = 0.068) have a positive and significant impact on rice production. The results show that minimum temperature and direct institutional credit for agriculture have a significant but negative impact on rice production in the short run. Minimum temperature, pesticide consumption, domestic credit to the private sector and direct institutional credit for agriculture have a negative and significant impact on rice production in the long run.

The present study makes valuable and original contributions to the literature by examining the short- and long-term impacts of climate change on rice production in India over 1970–2021. To the best of the authors’ knowledge, The majority of the studies examined the impact of climate change on rice production with the consideration of only “mean temperature” as one of the climatic variables, while in the present study, the authors have considered both minimum as well as maximum temperature. Furthermore, the authors also considered the financial variables in the model.