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This paper investigates the static and dynamic directional return spillovers and dependence among green investments, carbon markets, financial markets and commodity markets from January 2013 to September 2020.

This study employed both the quantile vector autoregression (QVAR) and time-varying parameter VAR (TVP-VAR) technique to examine the magnitude of static and dynamic directional spillovers and dependence of markets.

Results show that the magnitude of connectedness is extremely higher at quantile levels (q = 0.05 and q = 0.95) compared to those in the mean of the conditional distribution. This connotes that connectedness between green bonds and other assets increases with shock size for both negative and positive shocks. This further indicates that return shocks spread at a higher magnitude during extreme market conditions relative to normal periods. Additional analyses show the behavior of return transmission between green bond and other assets is asymmetric.

The findings of this study offer significant implications for portfolio investors, policymakers, regulatory authorities and investment community in terms of carefully assessing the unique characteristics offered by each markets in terms of return spillovers and dependence and diversifying the portfolios.

The study, first, uses a relatively new statistical technique, the QVAR advanced by Ando et al. (2018), to capture upper and lower tails’ quantile price connectedness and directional spillover. Therefore, the results possess adequate power against departure from mean-based conditional connectedness. Second, using a portfolio of green investments, carbon markets, financial markets and commodity markets, the uniqueness of this study lies in the examination of the static and dynamic dependence of the markets examined.

This paper has used account-level data of corporate and retail borrowers, assessed their credit risk through the risk-neutral principle and examined its implication on loan pricing.

It derives the capital charge and credit risk-premium for expected and unexpected losses through a risk-neutral approach. It estimates the risk-adjusted return on capital as the pricing principle for loans. Using GMM regression, the article has assessed the determinants of risk-based pricing.

It has been found that risk-premium is not reflected in the current loan pricing policy as per Basel II norms. However, the GMM estimation on RAROC can price risk premium and probability of default, LGD, risk weight, bank beta and capital adequacy, which are the prime determinants of loan pricing. The average RAROC for retail loans is more than that of corporate loans despite the same level of risk capital requirement for both categories of loans. The robustness tests indicate that the RAROC method of loan pricing and its determinants are consistent against the time and type of borrowers.

The RAROC method of pricing effectively assesses the inherent risk associated with loans. Though the empirical findings are confined to the sample bank, the model can be used for any bank implementing the Basel principle of risk and capital assessments.

The article has developed and validated the model for estimating RAROC, as per Basel II guidelines, for loan pricing that any bank can use.

It has developed the risk-based loan pricing model for retail and corporate borrowers. It has significant practical utility for banks to manage their risk, reduce their losses and productively utilise the public deposits for societal developments.

The article empirically validated the risk-neutral pricing principle using a unique 1,520 retail and corporate borrowers dataset.

This study aims to explore the oil–stock market nexus from a novel angle by examining the predictive role of oil prices over the excess returns associated with the market, size, book-to-market and momentum factors via bivariate cross-quantilograms.

This study makes use of the bivariate cross-quantilogram methodology recently developed by Han et al. (2016) to analyze the predictability patterns across the oil and stock markets by focusing on various quantiles that formally distinguish between normal, bull and bear as well as extreme market states.

The study analysis of systematic risk premia across the four regions shows that crude oil returns indeed capture predictive information regarding excess factor returns in stock markets, particularly those associated with market, size and momentum factors. However, the predictive power of oil return over excess factor returns is asymmetric and primarily concentrated on extreme quantiles, suggesting that large fluctuations in oil prices capture markedly different predictive information over stock market risk premia during up and down states of the oil market.

The findings have significant implications for the profitability of factor- or style-based active portfolio strategies and suggest that the predictive information contained in oil market fluctuations could be used to enhance returns via conditional strategies based on these predictability patterns.

This study contributes to the vast literature on the oil–stock market nexus from a novel perspective by exploring the effect of oil price fluctuations on the risk premia associated with the systematic risk factors including market, size, value and momentum.

This study aims to investigate factors that influence the attitudes and intentions of investors towards environmental, social and governance (ESG) stocks in the presence of perceived risk as a moderator.

Data was collected through an online survey method from 341 investors with more than three years of investing experience. Smart PLS was used to analyse the data using two-stage structural equation modelling. First, a measurement model was performed for construct reliability and validity, followed by path analysis (structural model) for hypothesis testing and overall model predictability.

The findings show that both environmental concern (altruistic value) and economic concern (egoistic value) are crucial for the attitude and intention of investors to invest in ESG-backed stocks; however, environmental concern was found to be a more significant predictor of their behaviour, showing evidence of pro-environmental values in the decision-making of utility-seeking individuals. No significant impact of perceived risk was evident as a moderator of the relationship between attitude and intention towards ESG stocks.

The study's findings have implications for fund managers, policymakers, and the government. Values as antecedents were found to be influential in shaping investors’ attitudes and intentions towards the environmental cause. Fund managers could include more ESG-compliant companies in their portfolios, and the government can play an important role in encouraging investors by providing financial incentives. Corporates should also take strategic steps to adopt green production processes to secure long-term, sustainable capital funding.

To the best of the authors’ knowledge, there has been no research done in the field of ESG investing that takes into account the values (both altruistic and egoistic) of investors as potential antecedents of their attitudes and intentions.

This paper aims to examine the cross-quantile correlation and causality-in-quantiles between green investments and energy commodities during the outbreak of COVID-19. To be specific, the authors aim to address the following questions: Is there any distributional predictability among green bonds and energy commodities during COVID-19? Is there exist any directional predictability between green investments and energy commodities during the global pandemic? Can green bonds hedge the risk of energy commodities during a period of the financial crisis.

The authors use the nonparametric causality in quantile and cross-quantilogram (CQ) correlation approaches as the estimation techniques to investigate the distributional and directional predictability between green investments and energy commodities respectively using daily spot prices from January 1, 2020, to March 26, 2021. The study uses daily closing price indices S&P Green Bond Index as a representative of the green bond market. In the case of energy commodities, the authors use S&P GSCI Natural Gas Spot, S&P GSCI Biofuel Spot, S&P GSCI Unleaded Gasoline Spot, S&P GSCI Gas Oil Spot, S&P GSCI Brent Crude Spot, S&P GSCI WTI, OPEC Oil Basket Price, Crude Oil Oman, Crude Oil Dubai Cash, S&P GSCI Heating Oil Spot, S&P Global Clean Energy, US Gulf Coast Kerosene and Los Angeles Low Sulfur CARB Diesel Spot.

From the CQ correlation results, there exists an overall negative directional predictability between green bonds and natural gas. The authors find that the directional predictability between green bonds and S&P GSCI Biofuel Spot, S&P GSCI Gas Oil Spot, S&P GSCI Brent Crude Spot, S&P GSCI WTI Spot, OPEC Oil Basket Spot, Crude Oil Oman Spot, Crude Oil Dubai Cash Spot, S&P GSCI Heating Oil Spot, US Gulf Coast Kerosene-Type Jet Fuel Spot Price and Los Angeles Low Sulfur CARB Diesel Spot Price is negative during normal market conditions and positive during extreme market conditions. Results from the non-parametric causality in the quantile approach show strong evidence of asymmetry in causality across quantiles and strong variations across markets.

The quantile time-varying dependence and predictability results documented in this paper can help market participants with different investment targets and horizons adopt better hedging strategies and portfolio diversification to aid optimal policy measures during volatile market conditions.

The outcome of this study will promote awareness regarding the environment and also increase investor’s participation in the green bond market. Further, it allows corporate institutions to fulfill their social commitment through the issuance of green bonds.

This paper differs from these previous studies in several aspects. First, the authors have included a wide range of energy commodities, comprising three green bond indices and 14 energy commodity indices. Second, the authors have explored the dependency between the two markets, particularly during COVID-19 pandemic. Third, the authors have applied CQ and causality-in-quantile methods on the given data set. Since the market of green and sustainable finance is growing drastically and the world is transmitting toward environment-friendly practices, it is essential and vital to understand the impact of green bonds on other financial markets. In this regard, the study contributes to the literature by documenting an in-depth connectedness between green bonds and crude oil, natural gas, petrol, kerosene, diesel, crude, heating oil, biofuels and other energy commodities.

This paper aims to examine the weak form of efficiency for price series of four precious metals, i.e. gold, silver, platinum and palladium, using a generalized spectral method.

The method has the advantage of detecting both linear and non-linear serial dependence in the conditional mean, and it is robust to various forms of conditional heteroscedasticity. The authors use three different rolling windows for the purpose of robustness.

The authors report weak form of efficiency across metals series for almost all rolling windows. The optimum efficiency for Gold and Palladium is achieved through 250 days rolling window estimates whereas it is 500 days rolling window for silver. Platinum has similar efficiency levels across rolling windows. The degree of efficiency for metal prices is observed to be varying over time with silver market possessing highest levels of efficiency. The efficiency synchronization also varies across rolling windows and metals.

The results reveal that metal markets are efficient for most times implying the low predictability and the low likelihood of earning abnormal returns by speculating in these markets.

The study uses a relatively new statistical technique, the generalized spectral test, to capture linear and non-linear serial dependence. Therefore, the results possess adequate power against departure from market efficiency.

This study aims at identifying critical success factors of a sustainable mobile banking application using text mining approach.

A total of 6,073 consumer reviews relating to a mobile banking application were collected and analyzed to meet the study objective. Latent Semantic Analysis (LSA) was done to identify the critical success factors of a sustainable mobile banking application.

The results indicated that privacy and security, navigation, customer support, convenience and efficiency are the key factors.

The study findings enrich the mobile banking and sustainable service delivery channel literature.

The results are expected to benefit the bankers in delivering effective banking services through a mobile banking application.

Studies in the sustainability are few yet promising particularly the ones that use rigorous statistics suitable on thousands of data points to accomplish the study objectives.