Is Default Risk Contagious? Evidence from Global Energy Leaders and Environmentally Conscious Energy Firms

IF 1.9 4区 经济学 Q2 ECONOMICS Computational Economics Pub Date : 2024-07-13 DOI:10.1007/s10614-024-10631-3
Zaheer Anwer, Wajahat Azmi, M. Kabir Hassan, Shamsher Mohamad
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Abstract

We examine the default risk spillover for two groups of global energy firms, including top energy firms from seven different sectors as well as energy firms scoring highest in terms of environment disclosure. We first perform a bibliometric review to uncover the trends in existing literature related to our research objectives. We then utilize novel, daily frequency data of ‘distance to default’ measure to perform two important co-movement techniques namely wavelet and TVP-VAR. The sample period is from 29 June 2009 to 30 June 2021. Our wavelet results reveal that both the groups exhibit spillover of default risk. However, there is higher interdependence of default risk in environment conscious energy firms during normal as well as crisis periods. The TVP-VAR results portray the interaction across both groups of firms and show heightened connectedness between the sampled firms for the sample period. We also identify net transmitters and receivers of shocks. The results carry important implications for investors and policymakers.

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违约风险会传染吗?来自全球能源龙头企业和具有环保意识的能源企业的证据
我们研究了两组全球能源公司的违约风险溢出,包括来自七个不同行业的顶级能源公司以及在环境信息披露方面得分最高的能源公司。我们首先进行了文献计量学回顾,以发现与我们的研究目标相关的现有文献的发展趋势。然后,我们利用新颖的 "违约距离 "日频数据,采用小波和 TVP-VAR 两种重要的共动技术。样本期为 2009 年 6 月 29 日至 2021 年 6 月 30 日。我们的小波结果显示,两个组别都表现出违约风险的溢出效应。然而,在正常时期和危机时期,注重环保的能源企业的违约风险相互依赖性更高。TVP-VAR 结果反映了两组公司之间的互动关系,并显示出样本期内抽样公司之间的关联性更强。我们还确定了冲击的净传播者和接收者。这些结果对投资者和政策制定者具有重要意义。
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来源期刊
Computational Economics
Computational Economics MATHEMATICS, INTERDISCIPLINARY APPLICATIONS-
CiteScore
4.00
自引率
15.00%
发文量
119
审稿时长
12 months
期刊介绍: Computational Economics, the official journal of the Society for Computational Economics, presents new research in a rapidly growing multidisciplinary field that uses advanced computing capabilities to understand and solve complex problems from all branches in economics. The topics of Computational Economics include computational methods in econometrics like filtering, bayesian and non-parametric approaches, markov processes and monte carlo simulation; agent based methods, machine learning, evolutionary algorithms, (neural) network modeling; computational aspects of dynamic systems, optimization, optimal control, games, equilibrium modeling; hardware and software developments, modeling languages, interfaces, symbolic processing, distributed and parallel processing
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