英文标题：
《Deep Learning in a Generalized HJM-type Framework Through Arbitrage-Free
Regularization》
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作者：
Anastasis Kratsios and Cody B. Hyndman
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最新提交年份：
2019
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英文摘要：
We introduce a regularization approach to arbitrage-free factor-model selection. The considered model selection problem seeks to learn the closest arbitrage-free HJM-type model to any prespecified factor-model. An asymptotic solution to this, a priori computationally intractable, problem is represented as the limit of a 1-parameter family of optimizers to computationally tractable model selection tasks. Each of these simplified model-selection tasks seeks to learn the most similar model, to the prescribed factor-model, subject to a penalty detecting when the reference measure is a local martingale-measure for the entire underlying financial market. A simple expression for the penalty terms is obtained in the bond market withing the affine-term structure setting, and it is used to formulate a deep-learning approach to arbitrage-free affine term-structure modelling. Numerical implementations are also performed to evaluate the performance in the bond market.
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中文摘要：
我们引入了一种正则化方法来选择无套利因子模型。所考虑的模型选择问题旨在学习与任何预先指定的因子模型最接近的无套利HJM类型模型。这是一个先验计算上难以处理的问题，其渐近解表示为一个单参数优化器族对计算上可处理的模型选择任务的限制。这些简化模型选择任务中的每一项都试图学习与规定的因子模型最相似的模型，当参考度量是整个基础金融市场的局部鞅度量时，会进行惩罚检测。在仿射期限结构设置下，得到了债券市场中惩罚条款的一个简单表达式，并利用该表达式建立了无套利仿射期限结构建模的深度学习方法。还进行了数值实现，以评估债券市场的表现。
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分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：Mathematical Finance 数学金融学
分类描述：Mathematical and analytical methods of finance, including stochastic, probabilistic and functional analysis, algebraic, geometric and other methods
金融的数学和分析方法，包括随机、概率和泛函分析、代数、几何和其他方法
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一级分类：Mathematics 数学
二级分类：Probability 概率
分类描述：Theory and applications of probability and stochastic processes: e.g. central limit theorems, large deviations, stochastic differential equations, models from statistical mechanics, queuing theory
概率论与随机过程的理论与应用：例如中心极限定理，大偏差，随机微分方程，统计力学模型，排队论
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一级分类：Quantitative Finance 数量金融学
二级分类：Pricing of Securities 证券定价
分类描述：Valuation and hedging of financial securities, their derivatives, and structured products
金融证券及其衍生产品和结构化产品的估值和套期保值
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一级分类：Statistics 统计学
二级分类：Machine Learning 机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
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