差分机器学习

摘要：

差分机器学习在金融衍生品的风险管理背景下将自动伴随差分（AAD）与现代机器学习（ML）相结合。我们引入了新的算法，用于训练快速，准确的定价和风险近似，在线，实时，收敛保证。我们的机器适用于任意衍生工具或交易账簿，在基础市场变量的任意随机模型下。它有效地解决了衍生品风险报告和资本计算的计算瓶颈。差分ML是监督学习的一般扩展，其中ML模型不仅在输入和标签的示例上训练，而且在标签与输入的差分上训练。它也适用于金融以外的许多情况，其中高质量的一阶衍生品wrt训练输入可用。例如，物理学中的应用可以利用第一原理中已知的微分来更有效地学习函数近似。在金融领域，AAD以显著的效率计算路径微分，因此差分ML算法提供了非常有效的定价和风险近似。我们可以在对封闭式解决方案来说过于复杂的模型中进行快速分析，提取复杂交易和交易账簿的风险因素，并有效地计算风险管理指标，例如跨大量场景的报告，对冲策略的回测和模拟，或XVA，CCR，FRTB或SIMM-MVA等法规。TensorFlow实现可在https://github.com/differential-machine-learning上获得

英文摘要：

Differential machine learning combines automatic adjoint differentiation (AAD) with modern machine learning (ML) in the context of risk management of financial Derivatives. We introduce novel algorithms for training fast, accurate pricing and risk approximations, online, in real-time, with convergence guarantees. Our machinery is applicable to arbitrary Derivatives instruments or trading books, under arbitrary stochastic models of the underlying market variables. It effectively resolves computational bottlenecks of Derivatives risk reports and capital calculations. Differential ML is a general extension of supervised learning, where ML models are trained on examples of not only inputs and labels but also differentials of labels wrt inputs. It is also applicable in many situations outside finance, where high quality first-order derivatives wrt training inputs are available. Applications in Physics, for example, may leverage differentials known from first principles to learn function approximations more effectively. In finance, AAD computes pathwise differentials with remarkable efficacy so differential ML algorithms provide extremely effective pricing and risk approximations. We can produce fast analytics in models too complex for closed form solutions, extract the risk factors of complex transactions and trading books, and effectively compute risk management metrics like reports across a large number of scenarios, backtesting and simulation of hedge strategies, or regulations like XVA, CCR, FRTB or SIMM-MVA. TensorFlow implementation is available on https://github.com/differential-machine-learning

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。