Deep Learning in Financial Modeling

金融建模中的深度学习

• 批准号: 550308-2020
• 负责人: Jaimungal, Sebastian
• 金额: $ 2.19万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=721238
• 关键词: Deep; Learning; Financial; Modeling

The last four decades saw the development of the financial derivatives valuation technology. Only a few practical models, however, can be solved in closed form, and as most utilize numerical methods such as finite-difference partial differential equation solvers, discrete-time trees, or Monte-Carlo simulators, these traditional methods are quite slow. Running book valuation and risk processes on hundreds of CPU cores requires an overnight process to comply with regulatory and accounting standards. This creates a huge cost (~$10 million/year) and a commensurate environmental and carbon impact. Similar issues are now facing the Insurance industry due to the accounting standard IFRS 17.The key goal of the partnership is to develop deep learning, and more generally machine and reinforcement learning, models to accelerate these processes, and to provide efficient simulation engines for asset prices, volatility surfaces, derivative valuation, and hedging.

在过去的四十年里，金融衍生品估值技术得到了发展。然而，只有少数实用模型能够以封闭的形式求解，而且由于大多数使用数值方法，如有限差分偏微分方程解、离散时间树或蒙特卡罗模拟器，这些传统方法相当缓慢。在数百个CPU核心上运行账面估值和风险流程需要一夜之间的流程，以符合监管和会计标准。这造成了巨大的成本(约1000万美元/年)以及相应的环境和碳影响。由于会计准则IFRS 17，保险业现在面临着类似的问题。合作的主要目标是开发深度学习，更广泛地说，机器学习和强化学习模型，以加速这些过程，并为资产价格、波动率表面、衍生品估值和对冲提供高效的模拟引擎。