Scholarlink

<p>As climate change and its impacts have become more pronounced, financial firms, investors, and regulatory supervisors are increasingly seeking to understand financial climate risks. One frequently mentioned barrier to using global change science is that information coming from global change scenarios and models may not match the needs of financial users. For example, there is often a spatial and temporal mismatch between scenarios and decisions. In addressing this barrier, much of the global change science literature has focused on further model advancement and communicating limitations. We argue that these efforts are insufficient because they do not adequately address that financial climate risk assessment will be subject to financial institutions’ model risk management processes, which mitigate the risk that modeling implementation errors, incomplete understanding of model adequacy, or inappropriate model use will lead to poor quality decisions. We argue that current model risk management is insufficient to ensure good financial decisions involving climate risk and then suggest a way forward. We cover three points. First, we outline why common issues of global change scenarios and models—data availability, deep uncertainty, and linking complex models—create problems for existing model risk management practices to mitigate risk due to incomplete understanding of model adequacy. Second, we note that the broader discourse around the use of global change scenarios, which feed global change information forward into risk models, has not kept pace with trends in model risk management. In particular, the financial industry’s reverse stress testing uses a risk model to discover plausible scenarios that lead to a pre-defined adverse outcome, such as insolvency. Finally, we outline why applying existing reverse stress testing methods to climate risk will not necessary address model risks introduced by global change scenarios, which usually require linking complex models. To adequately treat this model risk, we propose iterative reverse scenario logic, which couples scenario discovery with periodic expert judgment to check for scenario plausibility between and across models. We argue that the proposed method has the potential to better compartmentalize individual model risks and hence minimize the combined model risk of chaining difficult to validate models under conditions of deep uncertainty.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Using iterative reverse scenario logic to manage model risk in financial climate risk assessment

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月25日

查看全文
  • Michael D. Gerst,
  • Clifford Rossi,
  • Robert Brammer,
  • Timothy Canty,
  • Flannery Dolan,
  • Robert Lempert,
  • Elena Rovenskaya

摘要

As climate change and its impacts have become more pronounced, financial firms, investors, and regulatory supervisors are increasingly seeking to understand financial climate risks. One frequently mentioned barrier to using global change science is that information coming from global change scenarios and models may not match the needs of financial users. For example, there is often a spatial and temporal mismatch between scenarios and decisions. In addressing this barrier, much of the global change science literature has focused on further model advancement and communicating limitations. We argue that these efforts are insufficient because they do not adequately address that financial climate risk assessment will be subject to financial institutions’ model risk management processes, which mitigate the risk that modeling implementation errors, incomplete understanding of model adequacy, or inappropriate model use will lead to poor quality decisions. We argue that current model risk management is insufficient to ensure good financial decisions involving climate risk and then suggest a way forward. We cover three points. First, we outline why common issues of global change scenarios and models—data availability, deep uncertainty, and linking complex models—create problems for existing model risk management practices to mitigate risk due to incomplete understanding of model adequacy. Second, we note that the broader discourse around the use of global change scenarios, which feed global change information forward into risk models, has not kept pace with trends in model risk management. In particular, the financial industry’s reverse stress testing uses a risk model to discover plausible scenarios that lead to a pre-defined adverse outcome, such as insolvency. Finally, we outline why applying existing reverse stress testing methods to climate risk will not necessary address model risks introduced by global change scenarios, which usually require linking complex models. To adequately treat this model risk, we propose iterative reverse scenario logic, which couples scenario discovery with periodic expert judgment to check for scenario plausibility between and across models. We argue that the proposed method has the potential to better compartmentalize individual model risks and hence minimize the combined model risk of chaining difficult to validate models under conditions of deep uncertainty.