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This paper aims to attempt to capture the intertemporal/time-varying risk–return relationship in the Brazil, Russia, India and China (BRIC) equity markets after the global financial crisis (2007-2009), i.e. during a relative calm period. There has been a significant increase in advanced economies’ equity allocations to the emerging markets ever since the financial crisis. So, the present study is an attempt to account for the said relationship, thereby justifying investments made by the international investors.

The study uses non-linear models comprising asymmetric component generalised autoregressive conditional heteroskedastic model in mean (CGARCH-M) (1,1) model, generalised impulse response functions under vector autoregressive framework and Markov regime switching in mean and standard deviation model. The span of data ranges from 1 July 2009 to 31 December 2014.

The ACGARCH-M (1,1) model reports a positive and significant risk-return relationship in the Russian and Chinese equity markets only. There is leverage and volatility feedback effect in the Russian market because falling returns further increase conditional variance making the investors to expect a risk premium in the expected returns. The impulse responses indicate that for all of the BRIC markets, the ex-ante returns respond positively to a shock in the long-term risk component, whereas the response is negative to a shock in the short-term risk component. Finally, the Markov regime switching model confirms the existence of two regimes in all of the BRIC markets, namely, Bull and Bear regimes. Both the regimes exhibit negative relationship between risk and return.

It is an imperative task to comprehend the relationship shared between risk and returns for an investor. The investors in the emerging economies should understand the risk-return dynamics well ahead of time so that the returns justify the investments made under riskier environment.

The present study contributes to the literature in three senses. First, the data relate to a period especially after the global financial crisis (2007-2009). Second, the study has used a relatively newer version of GARCH based model [ACGARCH-M (1,1) model], generalised impulse response functions and Markov regime switching model to account for the relationship between risk and return. Finally, the study provides an insightful understanding of the risk–return relationship in the most promising emerging markets group “BRIC nations”, making the study first of its kind in all the perspectives.

This paper aims to attempt to re-capture the stock market contagion effect from the US to the BRIC equity markets during the recent global financial crisis in a multivariate framework. Apart from this, the study also identifies optimal portfolio hedging strategies to minimize the underlying portfolio risk during the period undertaken for the purpose of study.

To account for the dynamic interactions, the study uses vector autoregression (p) dynamic conditional correlation (DCC)-asymmetric generalized autoregressive conditional heteroskedastic (1,1) model in a multivariate framework, coupled with a monthly heat map relating to the co-movement between the US and the BRIC equity markets during the period 2007-2009. Finally, by following the studies, Hammoudeh et al. (2010) and Syriopoulos et al. (2015), the time-varying optimal portfolio hedge ratios and weights are computed.

The results report a contagion impact of the US subprime crisis (following the collapse of the Lehman Brothers) on the Indian and Russian stock markets only. On the other hand, a higher degree of interdependence between the US and Brazilian market has been observed. The US and Chinese equity markets indicate a relatively lower level of interdependence among themselves. The optimal hedge ratios are found to be most effective for a portfolio comprising the US and Chinese stocks even during the crisis period. A US investor should invest approximately 30 cents in the Indian market and rest of the 70 cents in the US market in a US$1 portfolio to minimize the portfolio risk without lowering the expected returns. During the crisis period (2007-2009), the optimal portfolio weights indicate a higher weightage to the BRIC stocks.

The results support the construction of optimal US–BRIC stock portfolios and provide an insight to the investors and policy makers both domestic as well as international, with regard to the contagion impact and interdependence, especially during a crisis period.

The study uses a DCC model in a multivariate framework instead of bivariate, wherein all the markets are factored into a single interaction framework across a very long period (2004-2014). Second, a heat map of monthly correlation combinations has been created for the period 2007-2009, to comprehend the contagion impact or interdependence among the markets. Finally, the study ascertains time-varying optimal hedge ratios and portfolio weights for a two asset portfolio, from a US investor viewpoint, making the study first of its kind in all the perspectives.

This study aims to examine the farmers’ awareness level and explores the factors, which may influence their adoption intention regarding solar powered pumps.

The study consist of a sample of 510 respondents selected from the rural region of Punjab (India) by using convenience sampling. Descriptive analysis, exploratory factor analysis, confirmatory factor analysis and multiple regression analysis techniques have been used for the analytical purpose.

The study reveals that dimensions such as perceived benefit, perceived compatibility and government incentives have a significant impact on intention to use solar powered pumps, whereas high investment cost and lack of awareness regarding government subsidies are the main reason for non-adoption of the same.

The sample size has been selected on the basis of convenience sampling and has been taken from the rural area, which may affect its generalizability.

The present research is expected to be useful for the manufacturers, regulators, customers, commercial banks, product and service providers, and other environmental institutions.

The study has acknowledged various intentional factors, which influence the adoption decision of solar powered pumps. Therefore, the present study will be useful to formulate action plans to improve the environmental quality.

The authors aim to report empirical linkages between the US and Brazil, Russia, India and China (BRIC) financial stress indices catalyzing catalyzing dependent economic policy initiatives (an extended version of Singh and Singh, 2017a).

Initially, the study develops financial stress indices for the respective BRIC financial markets. Later, it captures linkages among the said US-BRIC indices by using Johansen cointegration, vector autoregression/vector error correction models (VECM), generalized impulse response functions, Toda–Yamamoto Granger causality, variance decomposition analyses and bivariate generalized autoregressive conditional heteroskedasticity (GARCH) model under constant conditional correlation framework, in general. Markov regime switching and efficient causality tests proposed by Hill (2007) are also used.

Overall, there are both short-run and long-run dynamic interactions observed between the US and Indian financial stress indices. For rest of the markets, only short-run interactions are found to be in existence. The time-varying co-movement coefficients report financial contagion impact of the US financial crisis on Russian and Indian financial systems only. Contrary to this, Brazilian and Chinese financial systems are largely exhibiting interdependence with the US financial system. Efficient causality tests report indirect impact of the Russian financial system on Brazilian via auxiliary Indian financial system.

The present study is the first of its kind capturing linkages among the US-BRIC financial stress indices by using diverse econometric models. The results support different market participants and policymakers in understanding effectiveness and implementation of economic policies while considering their cross-market interactions as well.

The underlying research is an attempt to discover the factors responsible for influencing customer purchase intention towards solar water heaters.

Six dimensions have been considered to assess customer purchase intention towards a solar water heater. The primary data involves the responses collected from 695 respondents belonging to the north region of India using a convenience sampling technique. Structural equation modeling analysis has been employed to examine the dimensions' impact on customer purchase intention.

The results concluded that dimensions such as increasing energy prices, product knowledge and experience, financial support and subsidies, perceived cost, have a positive influence on customers' purchase intention of solar water heater except for the dimension of “solar water heater aesthetics”.

Due to the limited sample size, findings cannot be generalized for the large-scale population. Moreover, this study is only confined to the assessment of particular factors that are affecting the purchase intention of customers confined to solar water heaters only.

The present study will provide an advantage to organizations that are in the process of implementing solar energy products into working. Moreover, this research will also assist policymakers regarding the formulation of policies on solar energy products. An undue advantage of this would be the increasing adoption of a solar water heater by understanding the factors that impact customer purchase intention.

Considerably, this research, by filling up the gap in the existing studies, will empirically contribute to the customer purchase intention towards SWHs. Additionally, the results of the study will also endow an additive advantage to the existing firms.

With the globalization and liberalization in terms of increasing financial flows across the countries, the policy makers around the world are not independent in the context of monetary and fiscal policy initiatives. In this regard, this paper aims to attempt to quantify and capture long run, short run as well as time-varying linkages among the two financial stress indices, namely, Kansas City Financial Stress Index (KCFSI) and Indian Financial Stress Index (IFSI) across the monthly period (2004 to 2014).

Owing to the non-existence of a standardized financial stress index with regards to the Indian financial system, the study has developed an index/stress indicator using principal component analysis. Furthermore, to comprehend the linkages, the study uses bivariate Johansen cointegration model, vector error correction model, impulse response functions (IRF), variance decomposition analysis (VDA), Toda-Yamamoto’s Granger causality test and, finally, bivariate generalized autoregressive conditional heteroskedastic (BVGARCH) (1,1) model under constant conditional correlation (CCC) framework.

The results report a stochastic trend among the two indices wherein the US financial system acts as a source of a shock causing disequilibrium in the long run co-movement. About 40 per cent of the adjustments take place in one month and rest in the coming months. Both the IRF and VDA report a greater degree impact of the US financial stress on the Indian financial system. Moreover, there is a uni-directional short run causality running from the stress in the US financial system to the Indian financial stress. Furthermore, the co-movement between the US and Indian financial stress reached to its maximum significant level during the sub-prime crisis even confirmed by the Markov switching model results.

Overall, the results provide an insight to the financial market investors both domestic as well as international in their act of risk management. The financial stress prevailing in an economy further has an impact on different economic factors like foreign exchange rates, interest rates, yield curves, equity market returns and volatility. So, the empirical results support strong implications for the Indian policy makers as well as investors in the Indian financial markets.

The present study contributes to the literature in three senses. First, the study considers indices reflecting financial stress in the Indian as well as US financial system. Second, the study captures long run as well as short run linkages among the financial stress indices relating to a developed and an emerging market. Finally, the study uses CCC-BVGARCH (1,1) model to account for the time-varying co-movement among the financial stress indices. This helps in comprehending time-varying nature of the co-movement of the stress in the financial system prevalent in the respective markets.

This study examines the relationship between oil price uncertainty (OPU) and corporate inventory investments using a sample of 6,072 USA manufacturing firms from 1992 to 2019.

The author's study employs a panel dataset to examine the relationship between OPU and corporate inventory investments. The author uses several alternative specifications such as fixed effects models, an instrumental variable analysis, an impact threshold for confounding variable (ITCV) analysis, alternative measures, additional control variables and the percent bias analysis to account for endogeneity issues.

Corporate inventory investments decrease in response to high OPU. This decrease in inventory investments happens regardless of firms' expected stockout costs, information environment and reliance on external financing. As a potential mechanism, an uncertainty-induced increase in cash holdings contributes to this reduction in inventory investments. Also, the effect of OPU is non-linear and asymmetric. In response to the volatility of positive (negative) oil price changes, inventory investments decrease (increase) up to a certain point and increase (decrease) after that. Further, uncertainty-induced adjustments in inventory investments positively influence the operating performance of firms.

The author's study adds to the growing literature that examines the impact of OPU on corporate outcomes. Inventory investments directly affect business operations and could better reflect firms' responses to an uncertain environment.

This study examines the value implications of oil price uncertainty for investors in diversified firms using a sample of 922 USA firms from 2001 to 2019.

Our study employs a panel dataset to examine the value implications of oil price uncertainty for diversified firm investors. We consider several alternative specifications to account for unobserved factors and measurement errors that could potentially bias our results. In particular, we use alternative measures of the excess value of diversified firms and oil price uncertainty, additional control variables, fixed-effects models, the Oster test, impact threshold for confounding variable (ITCV) analysis, two-stage least square instrumental variable (2SLS-IV) analysis and the system-GMM model.

We find that the excess value of diversified firms, relative to a benchmark portfolio of single-segment firms, increases with high oil price uncertainty. The impact of oil price uncertainty is asymmetric, as corporate diversification is value-increasing for diversified firm investors only when the volatility is due to positive oil price changes and amidst supply-driven oil price shocks. The excess value increases irrespective of diversified firms’ financial constraints and oil usage. Diversified firms become conservative in their internal capital allocations with high oil price uncertainty. Such conservatism is value-increasing for diversified firm investors, as it supports higher performance in response to oil price uncertainty.

Our study has three important implications: first, they are relevant to investors in understanding the portfolio value implications of oil price uncertainty. Second, they are helpful for firm managers while comprehending the value-relevant implications of internal capital allocations. Finally, our findings are policy relevant in the context of the future of diversified firms in developed markets.

This paper aims to attempt to capture the co-movement of the Indian equity market with some of the major economic giants such as the USA, Europe, Japan and China after the occurrence of global financial crisis in a multivariate framework. Apart from these cross-country co-movements, the study also captures an intertemporal risk-return relationship in the Indian equity market, considering the covariance of the Indian equity market with the other countries as well.

To account for dynamic correlation coefficients and risk-return dynamics, vector autoregressive (1) dynamic conditional correlation–asymmetric generalized autoregressive conditional heteroskedastic model in a multivariate framework and exponential generalized autoregressive conditional heteroskedastic model in mean with covariances as explanatory variables are used. For an in-depth analysis, Markov regime switching model and optimal hedging ratios and weights are also computed. The span of data ranges from August 10, 2010 to August 7, 2015, especially after the global financial crisis.

The Indian equity market is not completely decoupled from mature markets as well as emerging market (China), but the time-varying correlation coefficients are on a downward spree after the global financial crisis, except for the US market. The Indian and Chinese equity markets witness a highest level of correlation with each other, followed by the European, US and Japanese markets. Both the optimal portfolio hedge ratios and portfolio weights with two asset classes point out toward portfolio risk minimization through the combination of the Indian and US equity market stocks from a US investor viewpoint. A negative co-movement between the Indian and US market increases the conditional expected returns in the Indian equity market. There is an insignificant but a negative relationship between the expected risk and returns.

The study provides an insight to the international as well as domestic investors and supports the construction of cross-country portfolios and risk management especially after the occurrence of global financial crisis.

The present study contributes to the literature in three senses. First, the period relates to the events after the global financial crisis (2007-2009). Second, the study examines the co-movement of the Indian equity market with four major economic giants such as the USA, Europe, Japan and China in a multivariate framework. These economic giants are excessively following the easy money policies aftermath the financial crisis so as to wriggle out of deflationary phases. Finally, the study captures risk-return relationship in the Indian equity market, considering its covariance with the international markets.