Readiness, riskiness, and renewables appear to form a "tripartite symbiosis" in the clean energy realm. Previous research underscores the significance of readiness as a prerequisite for a nation's advancement toward sustainable energy, urging careful navigation of uncertainties within this framework. Our research expands upon existing literature by delving into how country-level readiness influences a country's innovation in renewable energy in the face of uncertainty. Employing panel fixed effect threshold regression with four distinct models, we analyze this dynamic across 65 countries, representing both advanced and emerging economies. The results validate the presence of an uncertainty threshold effect across all model regressions, confirming a non-linear relationship among uncertainty, country-level readiness, and renewable energy innovation. Overall country-level readiness, along with its components-economic and social readiness-individually fosters renewable energy innovation under low uncertainty. However, this positive influence weakens as uncertainty exceeds the threshold. Conversely, governance readiness exerts a negative impact on renewable energy innovation under low uncertainty, with its detrimental effects becoming more significant at higher levels of uncertainty. The lagged uncertainty has a significant negative association with renewable energy innovation. Policymakers and investors should prioritize developing country level readiness to successfully manage the potential negative influence of uncertainties on renewable energy innovation.

Exchange rate modeling has always fascinated researchers because of its complex macroeconomic dynamics. This study documents the exchange rate dynamics of major emerging economies after accounting for their macroeconomic cycles and explores the Bayesian Vector Error Correction Model (VECM) Markov Regime switching model, which uses time-varying transition probabilities. The main objective is to study the exchange rate dynamics of Brazil, Russia, India, China, and South Africa (BRICS) vis-a-vis the US dollar. The Bayesian setup uses two hierarchal shrinkage priors, the normal-gamma (NG) prior and the Litterman prior, for parameters' estimation. These shrinkage priors allow for a more comprehensive assessment of the regime-specific coefficients. The model performed well in differentiating between the two regimes for all currencies. The Russian ruble was identified to be the most depreciated currency, whereas the African Rand was the most appreciated. The evaluation of model features revealed that many regime-specific coefficients differed significantly from their common mean. A forecasting exercise was then performed for the out-of-sample period to assess the model's performance. A significant improvement was observed over the basic random walk (RW) model and the linear Bayesian vector autoregression (BVAR) model.

This study explores the interconnectedness of bond markets in the Association of Southeast Asian Nations (ASEAN-4: Indonesia, Malaysia, the Philippines, and Thailand) with significant regional and global markets, focusing on transmitting global shocks to these markets. The analysis is based on the forecast error variance decomposition (FEVD) of a vector autoregression (VAR) model and wavelet-based longer horizon approaches, enabling us to identify the net transmitter and receiver of return and volatility shocks among the underlying markets at various investment horizons. In addition, we evaluate the primary drivers of ASEAN-4 bond markets, employing global and ASEAN-4 macroeconomic indicators and uncertainty measures to explain variations in the spillover dynamics. Empirical findings reveal strong inter-country connectedness among bond markets, with the U.S. market having the most significant links for 7- and 10-year bond maturities. However, there is limited connectedness within ASEAN-4 and other developing countries, indicating strong diversification potential. Furthermore, our findings indicate increased interconnectedness during COVID-19 and that macroeconomic fundamentals significantly influence the ASEAN-4 markets, rationalizing the heterogeneous cross-border transmission of uncertainty shocks. The findings contribute to the literature on bond market spillovers, offering valuable insights for policymakers, regulators, and investors seeking portfolio diversification, risk management, and sustainable growth in the ASEAN-4 markets.

While the process of carbon capture, utilization, and storage (CCUS) plays a pivotal role in mitigating climate change impacts, rising economic uncertainty, geopolitical conflict, and oil price volatility tend to retard CCUS deployment; which carbon emissions trading mechanisms can mitigate. The literature shows that such schemes are still immature in developing economies such as China, where carbon pricing seems to be a key strategy to lower CO2 power generation emissions. In this study, we thus investigate the Chinese carbon market’s volatility, concentrating on time-dependent jumps in emissions pricing. As jump-induced volatility represents an important risk, precise information thereon is important for increased carbon trading efficiency. The GARCH-jump process finds that such jumps do occur in the Chinese emissions market and that key uncertainty indicators including the aforementioned economic policy uncertainty, crude oil volatility index, and geopolitical risk can explain the resulting volatility, with important implications for policymakers and socially responsible investors.

While numerous empirical papers have investigated the volatility dynamics of Chinese clean energy equity markets, this is among the first studies to assess the impact of climate uncertainty on the risk levels of such assets. Given that China is extensively investing in green projects to achieve carbon neutrality, this strand of research offers important implications for investors and policymakers. Methodologically, we employ the GARCH-MIDAS model to examine the effect of the climate policy uncertainty (CPU) index on the volatility levels of the Chinese clean energy exchange-traded fund (ETF). We compare the effects of the CPU index with leading uncertainty indicators, including the crude oil volatility index, geopolitical risk, and technology sector volatility. The in-sample and out-of-sample analyses show that CPU has significant predictive contents for forecasting the volatility of renewable energy ETF and that the GARCH-MIDAS-CPU process outperforms other approaches. These results offer key implications for policymakers and socially responsible investors.

According to the Schumpeterian cleansing hypothesis, economic downturns force inefficient firms off the market, freeing resources that can be allocated to more efficient firms. India, as an emerging economy, may experience a similar reallocation. The study uses micro-level data for publicly traded firms, including manufacturing and services sector firms, from 1988 to 2020. We find that reallocation is productivity-enhancing in general, i.e., credit moves from firms with low productivity to firms with high productivity, and normal economic downturns induce this efficiency-enhancing reallocation. We also observe that reallocation is less efficiency-enhancing during the Indian financial crisis, and constraints on productive firms could be one of the potential explanations for the lack of a cleansing effect.

China is promoting carbon neutrality to cope with environmental degradation and economic loss, issuing more green bonds than any other country to finance its green transformation. Uncertainty affects green bonds more than traditional bonds, including policy uncertainty, natural disasters, and energy crises. This chapter advocates green bond underlying volatility swaps, a derivative that allows investors to trade green bond price volatility, which market participants can use for hedging. We propose a framework for forecasting realized volatility by demonstrating that Chinese green bonds are highly homogeneous, making them useful in such forecasting and thus guiding trading in such swaps. We also examine the forecasting performance of a novel model, RVNET-GARCH, which synthesizes multiple green bonds’ historical realized volatility into a network factor. Testing for robustness with three Monte Carlo simulations and six rolling horizons shows that the proposed methodology can provide reliable results.

We analyse time-varying risk spillover and dependence to assess the systemic risk benefits of ethical, sustainable, and green investments. Our data comprise sustainable investments from ethical, environmental, social and governance (ESG), and green bonds. We investigate the link to major asset classes, including equity, commodity, and currency markets. We find evidence of close connection between the major asset classes and sustainable assets, except green bonds. We also explore the improvement in hedging efficiency from combining ethical and ESG investments with commodities and currencies over investment horizons. Our analysis based on systemic risk measures indicates that there is evidence of lower time-scale systemic risk connectedness in the case of commodities and currencies combined with ethical and ESG assets. These findings have significant implications for portfolio managers, policymakers, and market participants.

This study examines the interconnectedness and risk profile of hydrogen in comparison to other assets from December 2019 to April 2022, using a time-varying copula approach. The findings reveal that hydrogen's relationships with conventional commodities and markets can shift significantly during extreme uncertainty. The COVID-19 pandemic had a marked impact on hydrogen's market connections. However, hydrogen did not uniformly increase linkages, highlighting its distinct profile even as an energy commodity. Despite its sensitivity to crises, hydrogen's reactions were varied, emphasizing the necessity for customized interpretations. Calculation of value-at-risk, conditional value-at-risk and Delta CoVaR demonstrated that green bonds exhibited the lowest investment risks across frequencies, which can be attributed to factors such as government support and growing demand. In the case of hydrogen, the risk metrics were inconsistent, reflecting its status as an early-stage adoption asset with niche applications in transportation and industry. These findings have key implications for managing risk associated with energy transition and for facilitating the adoption of hydrogen. The empirical insights provided herein can aid policymakers in developing supportive regulations and incentives to expedite the scaling of hydrogen. Moreover, Investors can leverage hydrogen's risk quantification for portfolio optimization and hedging strategies. Overall, by elucidating hydrogen's unique dynamics, this study informs stakeholder decisions to facilitate the hydrogen economy.

This paper examines quantile coherency in bonds, commodities, currencies, and equities using a novel quantile coherency approach. While recent literature has explored single-frequency tail and time-frequency dependence in asset returns, we provide fresh evidence on asset return dependence across quantiles (proxying business cycles or market conditions) at different frequencies (representing investment horizons). Considering sixty-seven individual asset return series in four asset classes, we observe that low frequency (yearly) dependence is stronger in the bond, foreign exchange, and equity markets. Specifically, we find strong dependence between the German and French bond markets, heating oil and crude oil, gold and silver, British Pound, and Euro, French and German and Canadian and US equities. As we report asset return interdependence in different business cycles and at different time horizons, these results have important implications for portfolio allocation and investment strategy formulation.