Rashmi Chaudhary

The integration of sustainability factors into investment decisions has transformed modern finance, with investors increasingly seeking to align their portfolios with environmental and societal values. The critical question remains as to whether the sustainability factor is priced in emerging markets. This study aims to provide a comprehensive analysis of the risk-adjusted return performance of three sustainability indexes of the emerging Indian market: NIFTY environmental, social and governance index (N100ESG), S&P BSE carbon-based thematic index (CARBONEX) and S&P BSE GREENEX (GREENEX) in comparison to the broad-based NSE 100 index (NIFTY100). The sample period for the analysis spans from January 2015 to August 2024. We have used risk-adjusted measures to evaluate the performance of sustainability indices. Additionally, we have analyzed downside risk, market-timing ability and volatility persistence using various generalized autoregressive conditional heteroskedasticity (GARCH)-type models. The findings indicate that sustainable investments offer competitive returns with better downside protection, especially for long-term investors. Among the indices, N100ESG demonstrated superior overall performance, while GREENEX stood out for risk–return resilience. However, reliance on market-cap criteria may dilute ESG purity. A more nuanced regulatory framework is essential to enhance the effectiveness of sustainable investing in India.

Investors are exploring new ways to navigate the constantly changing equity markets in emerging economies. Smart beta strategies, which use rule-based algorithms to boost returns and manage risk, are emerging as a promising solution. This research investigates how effective smart beta strategies are for retail investors. In contrast to a benchmark index, the study evaluates various smart beta strategies, including high alpha, beta, low volatility, value, growth, quality, momentum investing and combination strategies that consist of more than one factor. Based on the daily data from 1 October 2009 to 31 March 2023 of these strategic indices, the findings reveal that all strategies excluding the beta factor strategy consistently outperform benchmark index on various risk-adjusted return metrics. Notably, the momentum strategy closely followed by the alpha strategy is the best-performing approach for medium- and long-term horizons. Based on the data, these smart beta strategies have a greater downside beta than an upward beta, which suggests that they are more sensitive to declining markets. Comparatively, the combination strategies exhibit reduced sensitivity to declining markets, highlighting the safety and benefits of diversification among smart beta strategies. Furthermore, many of these techniques regularly outperform market returns and show effective market timing. These insightful observations help investors understand how smart beta strategies can be beneficial and strategically used in volatile market environments.

The importance of Environmental, Social, and Governance (ESG) aspects in investment decisions has grown significantly in today’s volatile financial market. This study aims to answer the important question of whether investing in ESG-compliant companies is a better option for investors in both developed and emerging markets. This study assesses ESG investment performance in diverse regions, focusing on developed markets with high GDP, specifically the USA, Germany, and Japan, alongside emerging nations, India, Brazil, and China. We compare ESG indices against respective broad market indices, all comprising large and mid-cap stocks. This study employs a variety of risk-adjusted criteria to systematically compare the performance of ESG indices against broad market indices. The evaluation also delves into downside volatility, a crucial factor for portfolio growth. It also explores how news events impact ESG and market indices in developed and emerging economies using the EGARCH model. The findings show that, daily, there is no significant difference in returns between ESG and conventional indices. However, when assessing one-year rolling returns, ESG indices outperform the overall market indices in all countries except Brazil, exhibiting positive alpha and offering better risk-adjusted returns. ESG portfolios also provide more downside risk protection, with higher upside beta than downside beta in most countries (except the USA and India). Furthermore, negative news has a milder impact on the volatility of ESG indices in all of the studied countries except for Germany. This suggests that designing a portfolio based on ESG-compliant companies could be a prudent choice for investors, as it yields relatively better risk-adjusted returns compared to the respective market indices. Furthermore, there is insufficient evidence to definitively establish that the performance of ESG indices varies significantly between developed and emerging markets.

Cryptocurrencies have gained a lot of attraction across the globe. Most observers of the cryptocurrency market will agree that crypto volatility is in a different league altogether. There has been a growing need to understand the nature of volatility in cryptocurrency. This paper analyzes the performance of four mostly traded, different cryptocurrencies in terms of their risk and return. The relationship between the return and returns volatility among different currencies has been examined considering the daily closing prices from 1 January 2017 to 30 June 2022, using the family of the GARCH model. The study has explored the spillover and asymmetric effect of volatility by using the DCC GARCH model and EGARCH model, respectively. The causal behavior among different cryptocurrencies has also been examined using Granger causality. There has been a strong spillover effect among different cryptocurrencies, Bitcoin and Ether, which are the top two cryptocurrencies with the highest market capitalization which have exhibited an asymmetric impact in their volatility as compared to the other two currencies, which are Litecoin and XRP.

The purpose of the paper is to select the right market proxy for calculating the expected return, since critically evaluating proxies or selecting the correct proxy market portfolio is essential for portfolio management because the change in the market portfolio proxy affects returns. In this study, monthly data of equity indices are evaluated to find out the better market proxy. The indices taken are BSE 30 (Sensex), Nifty 50, BSE 100, BSE 200, and BSE 500. The macroeconomic variables used in the study are industrial production index (IIP), consumer price index (CPI), money supply (M1), and exchange rate in India. To avoid the influence of COVID-19, the research period was from January 2013 to December 2019 to critically evaluate these proxies in order to find the most appropriate market proxy. This paper reveals a noteworthy relationship between stock market returns and macroeconomic factors, while suggesting that the BSE 500 is a better choice for all equity indices, as the index also shows a significant relationship with all macroeconomic variables. BSE500 is a composite index comprising all sectors with low, mid and large cap securities, therefore it reflects the impact of macroeconomic factors most efficiently, taking it as a market proxy. This study was carried out in the context of India and can be replicated for other countries.

COVID-19 is one of the unprecedented global crises that hit mankind in the last 100 years. It has engulfed the entire globe in its ambit, and what makes it peculiar is that it is not an economic crisis like the Great Depression or the Financial Crisis in 2007, but this is a natural crisis. The crisis has brought the entire global economy to a standstill due to lockdowns across different geographies, which has retarded the growth of economies. The current study explores how different major stock markets across the world have reacted to this grave pandemic. It is studied by comparing the returns of different phases of the market with the normal phase by applying T-test and Mann–Whitney U-test. Results indicate the short-term impact of this epidemic and much faster recoveries across different markets. The study also explores the spillover effect of different markets over others.

The current empirical study attempts to analyze the impact of COVID-19 on the performance of the Indian stock market concerning two composite indices (BSE 500 and BSE Sensex) and eight sectoral indices of Bombay Stock Exchange (BSE) (Auto, Bankex, Consumer Durables, Capital Goods, Fast Moving Consumer Goods, Health Care, Information Technology, and Realty) of India, and compare the composite indices of India with three global indexes S&P 500, Nikkei 225, and FTSE 100. The daily data from January 2019 to May 2020 have been considered in this study. GLS regression has been applied to assess the impact of COVID-19 on the multiple measures of volatility, namely standard deviation, skewness, and kurtosis of all indices. All indices’ key findings show lower mean daily return than specific, negative returns in the crisis period compared to the pre-crisis period. The standard deviation of all the indices has gone up, the skewness has become negative, and the kurtosis values are exceptionally large. The relation between indices has increased during the crisis period. The Indian stock market depicts roughly the same standard deviation as the global markets but has higher negative skewness and higher positive kurtosis of returns, making the market seem more volatile.

Predicting volatility is a must in the finance domain. Estimations of volatility, along with the central tendency, permit us to evaluate the chances of getting a particular result. Financial analysts are frequently challenged with the assignment of diversifying assets in order to form efficient portfolios with a higher risk to reward ratio. The objective of this research is to analyze the influence of COVID-19 on the return and volatility of the stock market indices of the top 10 countries based on GDP using a widely applied econometric model—generalized autoregressive conditional heteroscedasticity (GARCH). For this purpose, the daily returns of market indices from January 2019 to June 2020 were taken into consideration. The results reveal daily negative mean returns for all market indices during the COVID period (January 2020 to June 2020). Though the second quarter of the COVID period reflects a bounce back for all market indices with altered strengths, the volatility remains higher than in normal periods, signaling a bearish tendency in the market. The COVID variable, as an exogenous variance regressor in GARCH modeling, is found to be positive and significant for all market indices. Furthermore, the results confirmed the mean-reverting process for all market indices.

Due to many theoretical and practical shortcomings of the traditional CAPM model, this study aims at analyzing the CAPM with possible extensions. The analysis aims to know the empirical soundness of Conditional Higher Moment CAPM in emerging India’s capital market. The sample consists of 69 company’s daily stock price data from April 2004 to March 2019 from NSE 100. Panel data analysis is used on 21 cross-sections. The overall results show that when both up and down markets are incorporated separately, all three moments, namely, co-variance, co-skewness, and co-kurtosis, are priced during the normal Indian economy phase. Further, this study states that including higher moments (co-skewness and co-kurtosis) in the two-moment model provides symmetry in both the up and down markets. This is one of the first studies in the Indian Stock market explaining the variation in portfolio returns through panel data analysis by extending CAPM with conditional higher-order co-moments. The portfolio managers should consider skewness and kurtosis along with variance in constructing the optimal portfolios.