Mojtahedi Fatemeh, Ahelegbey Daniel Felix, Martina Mario
Department of Science, Technology and Society, University School for Advanced Studies IUSS, Pavia, Italy.
Department of Economics and Management, University of Pavia, Italy.
Heliyon. 2024 Aug 19;10(17):e36316. doi: 10.1016/j.heliyon.2024.e36316. eCollection 2024 Sep 15.
This paper introduces a comprehensive approach to studying the impact of climate-related factors on commodity and financial markets using network analysis. We utilize a Bayesian network Vector Autoregressive model to investigate whether climate risk significantly influ-ences commodity prices and financial market returns. Our findings provide evidence of a climate effect on major commodities and global financial markets. Specifically, we identify Crude oil, Cotton, and Sugar as the commodities most affected by climate risk, with Gold demonstrating the least susceptibility. Additionally, we observe that climate-related risk on commodities is likely propagated by patterns such as PNA, NN1, and AO. In terms of financial markets, we find that stock markets in Hong Kong, India, and Spain are the most susceptible to climate risk, while Switzerland's market appears to be the least affected. Furthermore, we document evidence that climate-related risk capable of altering financial markets is likely propagated by factors like ENP, NN1, and WH. Overall, our study underscores the intricate relationship between climate factors and market dynamics, highlighting the importance of considering climate risk in assessing market behavior and performance.
本文介绍了一种使用网络分析研究气候相关因素对商品和金融市场影响的综合方法。我们利用贝叶斯网络向量自回归模型来研究气候风险是否显著影响商品价格和金融市场回报。我们的研究结果为气候对主要商品和全球金融市场的影响提供了证据。具体而言，我们确定原油、棉花和糖是受气候风险影响最大的商品，而黄金的敏感性最低。此外，我们观察到商品上与气候相关的风险可能通过诸如太平洋北美模式（PNA）、北方涛动1（NN1）和北极涛动（AO）等模式传播。在金融市场方面，我们发现中国香港、印度和西班牙的股票市场最容易受到气候风险的影响，而瑞士市场似乎受影响最小。此外，我们记录的证据表明，能够改变金融市场的与气候相关的风险可能由诸如东大西洋-北非模式（ENP）、北方涛动1（NN1）和西太平洋模式（WH）等因素传播。总体而言，我们的研究强调了气候因素与市场动态之间的复杂关系，突出了在评估市场行为和表现时考虑气候风险的重要性。
