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• We explore the spillover and interconnectedness between climate policy initiatives, green finance, and carbon risk. • We used the quantile VAR based connectedness methods. • We examined this relationship under different market conditions, including two periods of market turmoil. • The study results show heterogeneous connectedness among climate policy initiatives, green. • finance, and carbon risk turmoil subperiods. • Climate policy and carbon risk are net givers of shocks, which is significant information for. • market participants. This paper assesses the role of climate policy initiatives and carbon risk in shaping green finance. It also explores the evolving dynamics of interconnectedness with green finance against the backdrop of the energy crisis, weather anomalies, and the escalating risk of environmental degradation. The study uses daily data from January 01, 2016, to March 31, 2023. It implements a quantile VAR-based connectivity technique to show that shocks are transmitted from green finance and received by policy initiatives and carbon risk at extreme quantiles rather than the mean. The interconnectivity strengthens during the health crisis and the war periods. The research findings substantially impact risk mitigation strategies and portfolio diversification, especially considering investors' transition toward alternative energy sector investments. [ABSTRACT FROM AUTHOR]

• Investigate whether the energy market signals a common trend toward volatility among green, brown, and sustainable energy indices. • Commonality among green energy indices is highest over others exhibiting similar characteristics. • The brown energy market possesses the least commonality, with heterogeneity among the brown energy indices. • The Paris Agreement lowered commonality in volatility across green and brown energy markets, thereby reducing risk exposure. Based on research conducted by Chordia et al. in 2000, we analyzed the volatility of energy indices to determine whether there is a commonality among them. Our dataset included green, sustainable, and brown energy indices, and we discovered that there is indeed a commonality in energy markets, with brown energy exhibiting the least commonality. Furthermore, we found that the commonality in volatility among energy markets has decreased since the Paris Agreement was signed. These results indicate that the Paris Agreement and other global policy initiatives are crucial for energy markets. [ABSTRACT FROM AUTHOR]

• We explore the heterogeneous interconnectedness and transmission of spillovers between environmentally sustainable, green, and brown ETFs. • We used the TVP-VAR-based connectedness methods in line with Stenfors et al. (2022). • We examined this relationship under different market conditions, including two periods of market turmoil. • The study results show heterogeneous connectedness among the sustainable, green, and brown ETFs in tranquil and turmoil subperiods. • Sustainable and green ETFs are the net givers, and brown ETFs are the net receivers, which is significant information for market participants. Over the last decade, increased awareness about environmental sustainability raised additional focus on green assets as a popular investment vehicle for reaching a carbon-free world. In this study, we analyze whether the dynamics of interconnectedness between ecologically sustainable, green energy, and brown ETFs have changed with the changing market conditions induced by the pandemic and a war scenario using an empirical network model based on TVP-VAR. The study findings report that sustainable and green energy ETFs are the primary givers, and brown energy ETFs are net receivers. The information transmission heightens during the first two waves of the COVID-19 pandemic, followed by a cooling phase. The results fully comprehend the effect of shifts in information transmission between environmentally sustainable, green, and brown ETFs, which aids policy-making, asset allocation, and pricing. [ABSTRACT FROM AUTHOR]

• How fake and media-hype news about COVID–19 impact the functional behaviour of commodity futures returns. • Deep learning algorithms to examine and forecast commodity future returns. • Fake and media-hype news of COVID-19 influence commodity futures. • Deep learning algorithms are adaptive to forecasting the behaviour of futures contract returns. We investigate the reactions of eight commodity futures to media hype and fake news during COVID-19, utilising the Ravenpack news database, along with deep learning algorithms. Results identify a significant impact on commodity prices of media hype and fake news, with this reaction amplified during COVID-19. Compared to alternative deep learning algorithms, bi-directional long-short-term memory is adaptive to forecasting the returns of the commodity futures contracts with lower mean absolute error and root mean square error. Findings, confirmed by Diebold-Mariano testing, as well as alternative data partitioning, show commodity markets are susceptible to fake news and media hype. [ABSTRACT FROM AUTHOR]

• The first paper to use 1-minute high-frequency trade data. • The first paper to examine the intraday behavior of the Government of India (GOI) 10-year benchmark bond to surprises in 19 different macroeconomic news releases. • Provide contrarian evidence about the efficiency of bond markets in India, especially the GOI benchmark bond, whose behavior resembles those of the mature markets. • The presence of asymmetric impact on negative and positive news and bigger surprises have moved the market more. • Isolating the impact of news surprises when released contemporaneously. • The paper has policy implications. • Provide valuable inputs for both domestic institutional investors, foreign institutional investors, and fund managers in context to the responses of the bond market to news signals and framing strategies for investment. We study the response of macroeconomic news on the intraday prices of the Indian benchmark Government bond. Contrary to the common understanding that the emerging bond markets are informationally less efficient, we found both returns and conditional volatility to respond swiftly to surprises in most macroeconomic news. The impact on volatility is more pronounced than returns, and there is an asymmetric impact implying that the negative news has a stronger market reaction over positive news. Robustness checks are undertaken to ascertain our findings' significance, including the news's contemporaneous response, release, frequency, and timing. [ABSTRACT FROM AUTHOR]

• The first paper to study financial contagion in derivative markets during the COVID-19 crisis period. • The paper is the first to analyze financial contagion from china to its major trading partners, which includes both developed and emerging countries (17 in total number). • The paper uses a modified framework to capture asymmetry, spillover and contagion, and or interdependence. • The study results document financial contagion in 13 developed and emerging economies, and 4 exhibiting interdependence. • It provides useful insights into a different class of market participants like policymakers, investors, and risk managers. The paper aims to investigate the existence of financial contagion between China and its major trading partners during the ongoing COVID-19 pandemic using the multivariate ADCC-EGARCH model. The analysis results reveal significant financial contagion in most developed and emerging markets having significant trade relationships with China during COVID-19 syndrome. The evidence about financial contagion is vital for regulators and different classes of market participants for varying purposes, and hence the results should find practical implications similar to policymakers, investors, and risk managers. [ABSTRACT FROM AUTHOR]