ABSTRACT
Understanding and examining energy markets correctly is crucial for stakeholders to attain maximum benefit and avoid risks. As a matter of fact, the volatility that occurred in energy markets and recent crises had major impacts on national economies. Dynamic connectedness relationships (DCRs) can make quite powerful predictions for both low-frequency data and limited time-series data. The objective of this study is to explicate the dynamic connectedness relationships among the BIST sustainability index, BIST 100 index, S&P Global Clean Energy index (S&P GCEI), and S&P GSCI carbon emission allowances (EUA). The daily data obtained over the period 11 April 2014–11 November 2022 were used for the research study. The DCRs among the variables used in the study were investigated by employing the time-varying parameter vector autoregressive (TVP-VAR) model. As a result of the study, the volatility from carbon emission allowances was determined to spill over to S&P GCEI, BIST 100, and BIST sustainability indexes. During the COVID-19 pandemic, significant reductions were detected in the volatility spillover (VS) from carbon emission allowances to S&P GCEI, BIST 100, and BIST sustainability indexes. Moreover, it was revealed that a weak VS existed from S&P GCEI to BIST sustainability and BIST 100 indexes. The findings reveal the importance of policymakers taking some incentive measures in EUA prices and also its role in portfolio diversification. © 2023 by the authors.
ABSTRACT
Central banks and regulators increasingly consider climate-related financial risks (CRFR) relevant to their responsibilities for maintaining financial stability and using daily data from 2016 to 2021 for China. Specifically, we used the S&P Green Bond Price Index, the Solactive Global Solar Price Index, the Solactive Global Wind Price Index, and the S&P Global Clean Energy and Carbon Price Index as our data set. We use the TVP-VAR method to probe return spillovers and interconnectedness. We test several portfolio strategies, including the minimum variance portfolio, the minimum correlation portfolio, and the more recent minimum connectedness portfolio. However, the evolving policy structure for dealing with CRFR has generally focused on market-based solutions that attempt to address perceived data gaps that preclude the appropriate pricing of CRFR, even though CRFR is thought to have certain distinctive features. Disclosure and openness fall within this category. We propose limiting the approach's influence since CRFR is characterized by extreme attainability. A 'precautionary' financial policy option is presented as an alternative, providing a conceptual foundation for justifying more aggressive financial policy intervention in the present to better cope with these long-term dangers.