Affiliation:
1. Department of Insurance and Risk Management, University of Uyo, Nigeria
Abstract
This chapter examines the volatility characteristics of global public climate finance from multilateral sources over the period 2003-2022. Annual data intervals are analyzed to assess the impact of the COVID-19 pandemic. Three GARCH model variations are employed, integrating both traditional Ito techniques and modern machine learning methods. Performance evaluation via root mean square error reveals that the Support Vector Regression-GARCH model provides the best fit. The detection of ARCH effects in specific years—2008, 2013, 2021, and 2022—indicates that climate finance volatility is sensitive to extraordinary events such as global financial crises and the COVID-19 pandemic. Although longer-term volatility modeling yields a smoother representation than short-term approaches, the analysis uncovers a lack of sustained consistency and stability. These findings aim to enhance stakeholders' understanding of climate finance volatility, aiding in the optimization of fund allocation strategies, performance metric assessments, and risk mitigation associated with public climate finance.
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