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: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=702568
• 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.

在过去的四十年中，金融衍生品评估技术的发展。但是，只有少数几个实用的模型才能以封闭形式解决，并且由于大多数使用数值方法，例如有限差异部分微分方程求解器，离散时间树或蒙特卡洛模拟器，这些传统方法非常慢。在数百个CPU内核上运行书籍估值和风险流程需要隔夜过程以符合监管和会计标准。这产生了巨大的成本（每年约1000万美元），并具有相称的环境和碳影响力。由于会计标准IFRS 17，现在类似的问题正面临保险行业。 合作伙伴关系的主要目标是开发深度学习，更一般的机器和强化学习，模型以加速这些过程，并为资产价格，波动性表面，衍生品评估和对冲提供有效的仿真引擎。