We introduce  a path-dependent geometric framework which generalizes the HJM modeling approach to a wide variety of other asset classes. A machine learning regularization frameworkis developed with the objective of removing arbitrage opportunities from models within this general framework. The regularization method relies on minimal deformations of a model subject to a path-dependent penalty that detects arbitrage opportunities. We prove that the solution of this regularization problem is independent of the arbitrage-penalty chosen, subject to a fixed information loss functional. In addition to the general properties of the minimal deformation, we also consider several explicit examples. This paper is focused on placing machine learning methods in finance on a sound theoretical basis and the techniques developed to achieve this objective may be of interest in other areas of application .
We introduce an unsupervised and non-anticipative machine learning algorithm which is able to detect and remove arbitrage from a wide variety models. In this framework, fundamental results and techniques from risk-neutral pricing theory such as NFLVR, market completeness, and changes of measure are given an equivalent formulation and extended to models which are deformable into arbitrage-free models. We use this scheme to construct a meta-algorithm which ensures that a wide range of factor estimation schemes return arbitrage-free estimates and incorporate this additional information into their estimation procedure. We show that using our meta-algorithm we are able to produce more accurate estimates of forward-rate curves, specifically at the long-end. The spread between a model and its arbitrage-free regularization is then used to construct a mis-pricing detection or classification algorithm, which is in turn used to develop a pairs trading strategy. Our theory provides a sound theoretical foundation for a risk-neutral pricing theory capable of handling models which potentially admit arbitrage but which can which can be deformed into arbitrage-free models .
A
an unsupervised and non-anticipative machine learning algorithm which is able to detect and remove arbitrage from
D
path-dependent geometric framework which generalizes the HJM modeling approach to a
R
of other asset classes. A machine learning regularization frameworkis developed with the objective of removing arbitrage opportunities from
models. In this framework, fundamental results and techniques from risk-neutral pricing theory such as NFLVR, market completeness, and changes of measure are given an equivalent formulation and extended to models which are deformable into arbitrage-free models. We use this scheme to construct a meta-algorithm which ensures that a wide range of factor estimation schemes return arbitrage-free estimates and incorporate this additional information into their estimation procedure. We show that using our meta-algorithm we are able to produce more accurate estimates of forward-rate curves, specifically at the long-end. The spread between a model and its arbitrage-free regularization is then used to construct a mis-pricing detection or classification algorithm, which is in turn used to develop a pairs trading strategy. Our theory provides a sound theoretical foundation for a risk-neutral pricing theory capable of handling models which potentially admit arbitrage but which can which can be deformed into arbitrage-free
D
within this general framework. The regularization method relies on minimal deformations of a model subject to a path-dependent penalty that detects arbitrage opportunities. We prove that the solution of this regularization problem is independent of the arbitrage-penalty chosen, subject to a fixed information loss functional. In addition to the general properties of the minimal deformation, we also consider several explicit examples. This paper is focused on placing machine learning methods in finance on a sound theoretical basis and the techniques developed to achieve this objective may be of interest in other areas of application