过渡情景不确定情况下的公司债务价值

IF 1.6 3区 经济学 Q3 BUSINESS, FINANCE
Theo Le Guenedal, Peter Tankov
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引用次数: 0

摘要

我们建立了一个结构模型,用于为受气候过渡风险影响的公司发行的可违约债券定价。我们假定过渡风险影响的大小取决于过渡情景,而过渡情景最初是未知的,但会通过观察碳税轨迹逐步显现。然后,债券价格、信用利差和最佳违约/重组阈值都是企业收入水平和碳税的函数。我们使用真实数据讨论并说明了计算公式的数值实现。我们的结果表明,在过渡方案不确定的情况下,碳税调整比真实方案已知时更有可能触发违约,因为每次调整后,更严格的环境方案变得更有可能。我们还发现,更快地发现情景信息会导致更高的信用利差,因为更好的信息可以让股东优化违约时机,增加违约期权的价值,降低债券价格。作为延伸,我们考虑了公司可能投资于减排技术的情况,从而提高股价和债券价格的价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Corporate debt value under transition scenario uncertainty
We develop a structural model for pricing a defaultable bond issued by a company subject to climate transition risk. We assume that the magnitude of the transition risk impacts depends on a transition scenario, which is initially unknown but is progressively revealed through the observation of the carbon tax trajectory. The bond price, credit spread, and optimal default/restructuring thresholds are then expressed as function of the firm's revenue level and the carbon tax. Numerical implementation of the resulting formulas is discussed and illustrated using real data. Our results show that under transition scenario uncertainty, carbon tax adjustments are more likely to trigger a default than when the true scenario is known because after each adjustment, the more environmentally stringent scenario becomes more likely. We also find that faster discovery of scenario information leads to higher credit spreads since better information allows the shareholders to optimize the timing of default, increasing the value of default option and decreasing the bond price. As an extension, we consider the situation where the company may invest into abatement technology, increasing the value of both the share price and the bond price.
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来源期刊
Mathematical Finance
Mathematical Finance 数学-数学跨学科应用
CiteScore
4.10
自引率
6.20%
发文量
27
审稿时长
>12 weeks
期刊介绍: Mathematical Finance seeks to publish original research articles focused on the development and application of novel mathematical and statistical methods for the analysis of financial problems. The journal welcomes contributions on new statistical methods for the analysis of financial problems. Empirical results will be appropriate to the extent that they illustrate a statistical technique, validate a model or provide insight into a financial problem. Papers whose main contribution rests on empirical results derived with standard approaches will not be considered.
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