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South Asian region is a focal point owing to its vulnerabilities to climate-sensitive diseases, dependence on climate-sensitive livelihoods, projected levels of crop decline in the region, and high rates of poverty and malnutrition. Women are particularly vulnerable to climate change and this affects women disproportionately during different extreme events. The purpose of this paper is to understand the issue of climate change and its impact, and climate resilience among women in South Asia. Further, it also identifies the gaps and suggests future policy implications.

Climate change is increasingly being recognised as an alarming issue and the present review is important when South Asian countries are facing the brunt of climate change impacts. This paper tries to understand the issue by review of the literature and conceptual framework methodology. To understand women’s vulnerability due to climate change and its aftermath, the authors conducted both offline and online desk reviews for this study.

The findings of this study show a clear linkage between climate change and women’s vulnerabilities in South Asia. Climate change has significant socio-economic impacts on women, and it affects them disproportionately in various domains of agriculture, livelihood, food security, both physical and mental health, water and sanitation in the South Asia region.

The paper also highlights that the programmes that aim at combating the effects of climate change require a gender-sensitive approach so that climate change does not obstruct the development and reduction of poverty in the region.

The findings of this paper will add value in helping families to come out of poverty by undertaking adaptive measures with proactive assistance from the government and grassroots level organisations.

The present study also advocates for more gender- and climate-sensitive measures from governments, and implementation of intervention- and evidence-based research in the South Asian countries.

This paper aims to identify the variables responsible for classifying the investors into risk takers (RT) and risk avoiders (RA) across their economic perspectives.

The research offers a novel and unobtrusive measure of classifying investors into RT and RA based on a set of financial risk tolerance (FRT) questions. The authors have investigated the causes of discrimination across economic perspectives over a sample of 552 investors exposed to market risk.

The authors identify that out of the total of 11 risk assessment variables, only three are responsible for classifying investors into RA and RT. The variables are risk return trade-off, comfort level dealing with risk, and understanding short-term volatility. Financial literacy is considered as an emerging cause of discrimination. Further, the authors highlight the most striking finding to be the discriminating factors across wealth and source of income of the investors.

Existing research on FRT can be loosely segregated into three groups: the relationship between an individual's financial and non-FRT, estimation of FRT score (FRTS), and perceived self-assessed FRTS. The current research roughly falls into the third category of study where the authors have not only studied the self-assessed risk tolerance but also evaluated the predictors. Most of the studies have focussed on estimating self-assessed FRT with the help of one direct question to the respondent. However, the uniqueness of this study is that the researchers have used an instrument comprising a series of direct and indirect questions that can easily estimate the self-assessed risk perception and also discriminate the role of the economic factors that have any impact on self-assessed FRTS.

The purpose of this paper is to examine the regime shifts and stock market volatility in the stock market returns of seven emerging economies popularly called as “BRIICKS” which stands for Brazil, Russia, India, Indonesia, China, South Korea and South Africa, over the period from February 1996 to January 2012 by applying Markov regime switching (MS) in mean-variance model.

The authors apply MS model developed by Hamilton (1989) using its mean-variance switching framework on the monthly returns data of BRIICKS stock markets. Further, the estimated probabilities along with variances have been used to calculate the time-varying volatility. The authors also examine market synchronization and portfolio diversification possibilities in sample markets by calculating the Logit transformation based cross-market correlations and Sharpe ratios.

The applied model finds two regimes in each of these markets. The estimated results also helped in formulating the asset allocation strategies based on market synchronization and Sharpe ratio. The results suggest that BRIICKS is not a homogeneous asset class and each market should be independently evaluated in terms of its regime-switching behavior, volatility persistence and level of synchronization with other emerging markets. The study finally concludes that Russia, India and China as the best assets to invest within this emerging market basket which can be pooled with a mature market portfolio to achieve further benefits of risk diversification.

The study does not provide macroeconomic and financial explanations of the observed differences in dynamics among sample emerging stock markets. The study does not examine these markets under multivariate framework.

The results highlight the role of regime shifts and stock market volatility in the asset allocation and risk management. This study has important implications for international asset allocation and stock market regulation by way of identifying and recognizing the differences on regimes and on the dynamics of the swings which can be very useful in the field of portfolio and public financial management.

The paper is novel in employing tests of MS under mean-variance framework to examine the regime shifts and volatility switching behavior in seven promising BRIICKS stock market. Further, using MS model, the authors analyze the duration (persistence) of each identified regime across sample markets. The empirical results of MS model have been used for making portfolio allocation strategies and also examine the synchronization across markets. All these aspects of stock market regime have been largely ignored by the existing studies in emerging market context particularly the BRIICKS markets.