Environmental Financial Derivatives: Pricing and Risk Management

环境金融衍生品：定价和风险管理

• 批准号: RGPIN-2019-06117
• 负责人: Olivares, Pablo
• 金额: $ 1.17万
• 依托单位: Ryerson University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=675730
• 关键词: Environmental; Financial; Derivatives; Pricing; Risk

The general objective of the proposal is the developing of techniques to evaluate environmental financial derivatives, as well as methods for hedging the risk of a company's financial position against climate related variables such as temperature, rainfall, snowfall and, wind and carbon emissions with a focus on the energy and agriculture sectors.***The long-term goal of the project, namely the study of the impact of climate change in the Canadian financial industry and local communities and how its risk can be mitigated, is combined with short-term goals via the implementation of specific policy evaluations and the analysis of financial hedging instruments.***The project involves several facets: Firstly, the modeling of the time and spatial dynamic of underlying climate variables combined with energy and electricity prices, as well as other non-conventional energy resources, and their correlations. Secondly, the fitting and calibration of such models using parameter estimation methods and machine learning techniques. Finally, the implementation of innovative pricing and hedging strategies in the context of real options, weather derivatives and catastrophe bonds with approximated closed-form techniques, together with the analysis of new weather-related financial contracts.***The original character of the proposal is found in different aspects along the project. We intend to evaluate new models based on Levy and more general noises with non-stationary independent increments, new volatility dynamics and stochastic correlation in a multivariate setting. Spatial stochastic models to capture correlation between different locations will be also analyzed. We plan the use of novel statistics techniques that combines likelihood, minimum distance methods based on characteristic functions, with an analysis founded on machine learning and neural networks to manage existing large databases. These models, estimation, pricing and hedging methods will generalize existent ones and/or will translate known models in the context of weather variables.***The results to be obtained are expected to have an impact in companies, regulators and local governments whose performance may be affected by climate change. They will assist in the decision-making process to implement climate change policies, by providing a series of tools to evaluate and manage the inherent risks and offer a guide leading to optimal investment decisions.***Project results will be immediately incorporated to the existing programs in Financial Mathematics at Ryerson University, at undergraduate, MSc and PhD levels. It will be reflected in the development of our research group, the training of 16 HQPs, specifically with the supervision of 1 postdoctoral fellow, 1 PhD, 5 MSc and 7 undergraduate theses plus 2 research assistants. Publication of 6 scientific papers in main stream journals, participation in international conferences, as well as a general improvement in curricular activities are expected.***

该提案的总体目标是开发评估环境财务导数的技术，以及对应对与气候相关的变量（例如温度，降雨，降雪以及风力和碳排放，重点关注能量和农业领域）的财务和碳排放的方式来对抗公司财务状况的风险。可以通过实施特定的政策评估和对金融对冲工具的分析结合使用短期目标。 其次，使用参数估计方法和机器学习技术对此类模型的拟合和校准。最后，在实际选择，天气衍生品和灾难债券的背景下，以近似封闭形式的技术的方式实施了创新的定价和对冲策略，以及对新的与天气相关的金融合同的分析。我们打算根据征费和更通用的噪声来评估新模型，并具有非平稳的独立增量，新的波动率动力学和在多变量环境中的随机相关性。还将分析以捕获不同位置之间相关性的空间随机模型。我们计划使用基于特征功能的可能性，最小距离方法的新颖统计技术，并建立在机器学习和神经网络上的分析，以管理现有的大型数据库。这些模型，估计，定价和对冲方法将概括存在的模型和/或将在天气变量的背景下翻译已知模型。他们将通过提供一系列评估和管理固有风险的工具来协助实施气候变化政策的决策过程，并提供指南，从而实现最佳投资决策。***项目结果将立即与本科生，MSC和PHD水平的Ryerson大学的现有金融数学课程合并。它将反映在我们的研究小组的开发中，即16个HQP的培训，特别是对1名博士后研究员的监督，1位博士学位，5 MSC和7个本科论文以及2位研究助理。 预计在主流期刊上发表了6篇科学论文，参与国际会议以及课程活动的总体改进。***