Environmental Financial Derivatives: Pricing and Risk Management

该提案的总体目标是开发评估环境金融衍生品的技术，以及对冲公司财务状况与气候相关变量（如温度、降雨、降雪、风和碳排放）风险的方法，重点是能源和农业部门。***该项目的长期目标，即研究气候变化对加拿大金融业和当地社区的影响，以及如何减轻其风险，通过实施具体政策评估和分析金融对冲工具，与短期目标相结合。***本项目涉及几个方面：首先，结合能源和电价以及其他非常规能源资源，对潜在气候变量的时空动态及其相关性进行建模。其次，利用参数估计方法和机器学习技术对这些模型进行拟合和标定。最后，在实物期权、天气衍生品和近似封闭形式的巨灾债券的背景下实施创新的定价和对冲策略，以及对新的天气相关金融合约的分析。***方案的原创性体现在项目的各个方面。我们打算在多元环境中评估基于Levy和更一般的非平稳独立增量噪声的新模型，新的波动动力学和随机相关性。还将分析捕捉不同地点之间相关性的空间随机模型。我们计划使用新颖的统计技术，将基于特征函数的似然、最小距离方法与基于机器学习和神经网络的分析相结合，以管理现有的大型数据库。这些模型、估计、定价和套期保值方法将在天气变量的背景下推广现有的模型和/或转换已知的模型。***预计所得结果将对业绩可能受到气候变化影响的公司、监管机构和地方政府产生影响。它们将通过提供一系列工具来评估和管理固有风险，并为最佳投资决策提供指导，从而协助实施气候变化政策的决策过程。***项目成果将立即纳入瑞尔森大学现有的本科、硕士和博士金融数学课程。这将体现在我们的研究小组的发展，培养16名HQPs，具体来说，有1名博士后，1名博士，5名硕士，7篇本科论文和2名研究助理。预计在主流期刊上发表6篇科学论文，参加国际会议，并在课程活动方面有所改善

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.***