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Title: Data-driven market simulators and some simple applications of signature kernel methods in mathematical finance Authors:  Blanka Horvath - TU Munich (Germany) [presenting]
Abstract: Techniques that address sequential data have been a central theme in machine learning research in the past years. More recently, such considerations have entered the field of finance-related ML applications in several areas where we face inherently path-dependent problems: from (deep) pricing and hedging (of path-dependent options) to generative modelling of synthetic market data, which we refer to as a market generation. We revisit Deep Hedging from the perspective of the role of the data streams used for training and highlight how this perspective motivates the use of highly accurate generative models for synthetic data generation. Stochastic processes are at their core random variables with values on path space. However, while the distance between two (finite-dimensional) distributions was historically well understood, the extension of this notion to the level of stochastic processes remained a challenge until recently. We discuss the effect of different choices of such metrics while revisiting some topics that are central from a regulatory (and model governance) perspective.