论文标题
差异机器学习
Differential Machine Learning
论文作者
论文摘要
在金融衍生品风险管理的背景下，差异机器学习将自动伴随分化（AAD）与现代机器学习（ML）结合在一起。我们介绍了新颖的算法，以实时，在线，准确，准确的定价和风险近似，并实时融合保证。我们的机械适用于在基础市场变量的任意随机模型下，适用于任意衍生工具或交易簿。它有效地解决了衍生品风险报告和资本计算的计算瓶颈。 差异ML是监督学习的一般扩展，其中ML模型不仅是输入和标签的示例，而且还通过标签输入的差异进行培训。它也适用于金融以外的许多情况，在那里提供高质量的一阶WRT培训输入。例如，物理中的应用可能利用从第一原理中知道的差异来更有效地学习函数近似。 在金融中，AAD计算具有显着疗效的路线差异，因此差异ML算法提供了极有效的定价和风险近似值。我们可以在过于复杂的模型中生成快速的分析，无法用于封闭式解决方案，提取复杂交易和交易书籍的风险因素，并有效地计算风险管理指标，例如在大量场景中的报告，对冲策略或XVA，CCR，CCR，FRTB或SIMM-MVA等法规的反应和模拟。 TensorFlow实现可在https://github.com/differential-machine-learning上获得
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
